{"meta":{"query_hash":"78337e7ccc35","filters":{"topic":"Water Systems and Optimization"},"cohort_total":1136,"direct_labels_cover":2,"predictions_cover":1136,"exported":1136,"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/78337e7ccc35","api":"https://metacan.xera.ac/api/v1/cohort?topic=Water+Systems+and+Optimization"},"results":[{"id":"W1039122418","doi":"10.14796/jwmm.r236-15","title":"Using SWMM 5 to Develop Wet Weather Operating Strategies in a Large Sewer System","year":2010,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Sanitary sewer; Wastewater; Combined sewer; Environmental science; Environmental engineering; Hydrology (agriculture); Sewage treatment; Civil engineering; Engineering; Water resource management; Geotechnical engineering; Stormwater; Surface runoff","score_opus":0.017464711570100818,"score_gpt":0.2281240206700292,"score_spread":0.2106593090999284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1039122418","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":[0.9511662,0.00005151985,0.04024535,0.000121581645,0.000032716765,0.000090079324,0.0002862816,0.00042355456,0.0075825583],"genre_scores_gemma":[0.96517944,0.000028340193,0.03312218,0.000032398406,0.0000044842923,0.00007302743,0.00018692813,0.000051643998,0.0013215821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987173,0.00004918638,0.0000064123574,0.000019334468,0.00001886182,0.000034431247],"domain_scores_gemma":[0.9992669,0.00049769186,0.000057721267,0.000026203143,0.00012032347,0.000031139058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006686281,0.0006770377,0.00049982057,0.00048538783,0.0005038533,0.0005659078,0.0007225333,0.00093935034,0.002798544],"category_scores_gemma":[0.0013280021,0.0006668783,0.0007220512,0.00035749903,0.00027518335,0.00045038428,0.0004020881,0.0005314699,0.00013068154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025217258,0.000047810154,0.00080782967,0.000014930646,0.0000073899373,0.000017902634,0.0000131393945,0.9954483,0.00038582,0.0002485121,0.000102071186,0.0028811058],"study_design_scores_gemma":[0.000008281977,0.00002381803,0.00019511393,0.0000010838753,0.0000030258393,9.972299e-7,0.000010566557,0.999358,0.00028152572,0.000063677595,0.00005187666,0.0000020444177],"about_ca_topic_score_codex":0.062206868,"about_ca_topic_score_gemma":0.06530803,"teacher_disagreement_score":0.062206868,"about_ca_system_score_codex":0.0012895506,"about_ca_system_score_gemma":0.0015275817,"threshold_uncertainty_score":0.12368953},"labels":[],"label_agreement":null},{"id":"W111909870","doi":"10.1007/978-1-4614-0189-6_18","title":"Enhanced Monitoring to Protect Distribution System Water Quality","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Water Systems and 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":"American Water (Canada)","funders":"","keywords":"Water quality; Computer security; Computer science; Risk analysis (engineering); Engineering; Business","score_opus":0.025182315449345873,"score_gpt":0.2093486190625879,"score_spread":0.18416630361324202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W111909870","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":[0.006303893,0.014821806,0.5753643,0.0031194275,0.0013420342,0.0000999213,0.000281318,0.0018538317,0.3968134],"genre_scores_gemma":[0.13267794,0.024395617,0.15152647,0.0012131409,0.00073854724,0.00010644901,0.0003960898,0.00049204234,0.6884537],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99976796,0.000033229542,0.0000050806007,0.00003254493,0.00014815104,0.000013007999],"domain_scores_gemma":[0.9998529,0.00005832295,0.00001043948,0.000024707953,0.00004936943,0.0000042698534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026070344,0.0005833288,0.00033925587,0.0003846335,0.00017191315,0.0008460887,0.00070808624,0.0005600023,0.009916651],"category_scores_gemma":[0.0004251883,0.0002288839,0.00023362183,0.00043513754,0.00035341264,0.0014419141,0.0005381659,0.000902746,0.0020352267],"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":[0.000040024675,0.0000932147,0.00035305938,0.0003182112,0.000019190371,0.000076043645,0.00008772116,0.01775204,0.012535064,0.092453726,0.084336765,0.7919349],"study_design_scores_gemma":[0.000013536962,0.00007253948,0.0019151053,0.0003407573,0.00003845139,0.00044987022,0.00006685583,0.05556071,0.030042864,0.07643437,0.8350296,0.000035294575],"about_ca_topic_score_codex":0.0013064949,"about_ca_topic_score_gemma":0.0029007462,"teacher_disagreement_score":0.009916651,"about_ca_system_score_codex":0.0006989781,"about_ca_system_score_gemma":0.00043169138,"threshold_uncertainty_score":0.033174515},"labels":[],"label_agreement":null},{"id":"W1127605711","doi":"10.1007/s11269-015-1108-6","title":"Using Data Mining to Understand Drinking Water Advisories in Small Water Systems: a Case Study of Ontario First Nations Drinking Water Supplies","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Water supply; Decision tree; Certification; Hydrogeology; Data mining; Computer science; Environmental science; Engineering; Environmental engineering","score_opus":0.07730290404225432,"score_gpt":0.24032933995306788,"score_spread":0.16302643591081356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1127605711","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":[0.989925,0.0001505582,0.004349418,0.00072744634,0.0000041085705,0.000071536044,0.0014697735,0.00005551953,0.0032464901],"genre_scores_gemma":[0.9896347,0.00016859233,0.007875302,0.00003769255,0.0000030066383,0.000020056885,0.0012067173,0.000013515164,0.0010405078],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996822,0.00009719254,0.00002282766,0.00005797972,0.000084114654,0.0000555904],"domain_scores_gemma":[0.99772364,0.0016377455,0.0001997026,0.00011596101,0.00027167684,0.000051307245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000574362,0.00032774798,0.00021731907,0.0009854969,0.0008654922,0.001163618,0.000653519,0.0006144025,0.00078618305],"category_scores_gemma":[0.0038391813,0.00022622052,0.00031168968,0.002162891,0.0004948982,0.0009524176,0.00036136832,0.00038460124,0.00009002901],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002872323,0.0007689448,0.6604303,0.00038601676,0.00021926945,0.0030607667,0.01052259,0.15780765,0.0052748933,0.005429505,0.0068136724,0.1489992],"study_design_scores_gemma":[0.000045970475,0.00012109401,0.35481888,0.0001199617,0.00011066893,0.00036858104,0.02463231,0.5906855,0.0045161583,0.005405819,0.019104294,0.00007072417],"about_ca_topic_score_codex":0.7500233,"about_ca_topic_score_gemma":0.8771863,"teacher_disagreement_score":0.2499767,"about_ca_system_score_codex":0.005296453,"about_ca_system_score_gemma":0.0039627897,"threshold_uncertainty_score":0.5028977},"labels":[],"label_agreement":null},{"id":"W1139123150","doi":"10.1007/978-3-319-14215-9_10","title":"A Data Mining Tool for Planning Sanitary Sewer Condition Inspection","year":2015,"lang":"en","type":"book-chapter","venue":"","topic":"Water Systems and 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 Guelph","funders":"","keywords":"Computer science; Construction engineering; Mining engineering; Data mining; Engineering","score_opus":0.08424005979164313,"score_gpt":0.26384382454596395,"score_spread":0.17960376475432083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1139123150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021512287,0.0015331296,0.97292274,0.00031985284,0.00015903811,0.00007489452,0.0032107274,0.008958629,0.010669762],"genre_scores_gemma":[0.02572746,0.0023507026,0.9437201,0.00021153416,0.0000939443,0.00019145983,0.0066751014,0.0009800924,0.02004965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962616,0.000035577184,0.0000371476,0.00009495368,0.00018882948,0.000017242584],"domain_scores_gemma":[0.9994905,0.00030733252,0.000021882348,0.000062179075,0.000102823025,0.000015247319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005201599,0.0012862618,0.0009458227,0.0016857273,0.00043959185,0.0015969343,0.0017408553,0.0008205637,0.014495826],"category_scores_gemma":[0.0014936222,0.0008046163,0.0012586861,0.0023959375,0.000325429,0.0016918845,0.0009090183,0.0013354389,0.006487323],"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":[0.00006398952,0.000088136345,0.0010034147,0.0005188728,0.00009112682,0.00023432079,0.00013947624,0.04937076,0.0040558074,0.021131327,0.08067629,0.8426264],"study_design_scores_gemma":[0.000038804697,0.000069219415,0.0020997527,0.0003836331,0.00009607416,0.00076264545,0.00012826287,0.57789904,0.014785975,0.0931646,0.3105007,0.000071232076],"about_ca_topic_score_codex":0.003360786,"about_ca_topic_score_gemma":0.005576386,"teacher_disagreement_score":0.014495826,"about_ca_system_score_codex":0.00047312563,"about_ca_system_score_gemma":0.00059332914,"threshold_uncertainty_score":0.048493326},"labels":[],"label_agreement":null},{"id":"W122185726","doi":"","title":"Élaboration d'une méthodologie de détection et de caractérisation des courts-circuits hydrauliques entre aquifères causés par un défaut de scellement de puits","year":2005,"lang":"fr","type":"dissertation","venue":"Publications Et Travaux Academiques de Lorraine (Universite de Lorraine)","topic":"Water Systems and 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":"Humanities; Physics; Philosophy","score_opus":0.030654162329195427,"score_gpt":0.2895030349688593,"score_spread":0.2588488726396639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W122185726","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":[0.019021448,0.00014405749,0.97748154,0.00012167086,0.0000682957,0.00022048249,0.0002933086,0.0014748181,0.0011743705],"genre_scores_gemma":[0.20911628,0.00034361475,0.78329,0.00013471465,0.00002615084,0.0010288298,0.00061794196,0.0003211116,0.0051212683],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658966,0.00049148116,0.00020240196,0.0010186761,0.0014524158,0.00024533932],"domain_scores_gemma":[0.9933409,0.0028487046,0.0005696858,0.0010251177,0.0021045625,0.000111020585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002441113,0.001221702,0.001466001,0.0017021787,0.0008166548,0.0028101706,0.0020260012,0.0019976015,0.0052084737],"category_scores_gemma":[0.00781628,0.0010688875,0.001607948,0.0013730138,0.0014780337,0.0018738037,0.0014865367,0.0022102566,0.0014674243],"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":[0.0006066302,0.0004401146,0.019877754,0.0019750318,0.00028875176,0.000522502,0.0013040713,0.13395658,0.4603321,0.021120954,0.0029492734,0.35662627],"study_design_scores_gemma":[0.00012345056,0.0005187361,0.01300204,0.00018778509,0.00013395307,0.00047342124,0.0007885912,0.5547637,0.39675972,0.013467636,0.019534789,0.00024628697],"about_ca_topic_score_codex":0.008741714,"about_ca_topic_score_gemma":0.006800974,"teacher_disagreement_score":0.008741714,"about_ca_system_score_codex":0.0014765532,"about_ca_system_score_gemma":0.0031546224,"threshold_uncertainty_score":0.017424047},"labels":[],"label_agreement":null},{"id":"W1253866567","doi":"10.1205/psep.82.6.453.53207","title":"Risk Analysis and Management in the Water Utility Sector","year":2004,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Water Systems and Optimization","field":"Engineering","cited_by":91,"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":"Risk management; Risk analysis (engineering); Water sector; Maturity (psychological); Public sector; Business; Risk management framework; Reliability (semiconductor); IT risk management; Risk assessment; Water industry; Environmental resource management; Engineering; Water supply; Finance; Economics; Environmental engineering; Management","score_opus":0.0048620669658637206,"score_gpt":0.1596336752141795,"score_spread":0.1547716082483158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1253866567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42845976,0.009511215,0.50927716,0.015508545,0.00015737228,0.00013987001,0.00026866017,0.00021344496,0.036464036],"genre_scores_gemma":[0.9850721,0.0014964291,0.010365066,0.00008523786,0.00006656589,0.000029146535,0.000051845516,0.000018146457,0.0028153642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997727,0.0014105409,0.000084318504,0.00013661389,0.00046031186,0.00018123185],"domain_scores_gemma":[0.99452,0.0042435634,0.00048459618,0.00013854286,0.00045141423,0.00016191788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036954118,0.0005563028,0.0005468519,0.0010497855,0.0005940874,0.002907867,0.0006279255,0.0013967721,0.0014346527],"category_scores_gemma":[0.0093329055,0.00041528945,0.00049904507,0.0010979102,0.00140726,0.002765762,0.0009936104,0.0011647786,0.000103846985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015630771,0.00015103597,0.0066852104,0.00009873417,0.00012784965,0.00026658076,0.00039801368,0.73191524,0.00074160245,0.20402397,0.002898591,0.052536774],"study_design_scores_gemma":[0.000012841739,0.000048222173,0.001989531,0.00003413645,0.000034446308,0.000060511506,0.00060627237,0.65235466,0.0005205562,0.34115446,0.0031593374,0.000025040083],"about_ca_topic_score_codex":0.009258228,"about_ca_topic_score_gemma":0.007212539,"teacher_disagreement_score":0.009258228,"about_ca_system_score_codex":0.0031715818,"about_ca_system_score_gemma":0.0027130146,"threshold_uncertainty_score":0.023011506},"labels":[],"label_agreement":null},{"id":"W1273641221","doi":"","title":"Instrumentation, control and automation in urban water management – state-of-the-art and future perspectives","year":2010,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Water Systems and 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":"EnviroSim (Canada)","funders":"","keywords":"Instrumentation (computer programming); Automation; State (computer science); Systems engineering; Control (management); Engineering; Engineering management; Computer science; Mechanical engineering; Artificial intelligence","score_opus":0.00323366956055022,"score_gpt":0.1745700867907317,"score_spread":0.17133641723018148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1273641221","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":[0.006885041,0.9085158,0.05288884,0.011896517,0.0013736754,0.000030626474,0.000052595242,0.00012820143,0.01822868],"genre_scores_gemma":[0.17963125,0.77765125,0.021776106,0.0017103953,0.006145212,0.000055869772,0.000124542,0.000047977413,0.012857349],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803203,0.0005815839,0.000096739015,0.00036931143,0.0006647434,0.00025564455],"domain_scores_gemma":[0.99499017,0.003395049,0.0002669591,0.00019203019,0.0009571258,0.00019863706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004534028,0.00074761716,0.0017686606,0.0011437671,0.00056934677,0.0059099454,0.0018755684,0.0047302996,0.008025004],"category_scores_gemma":[0.0027312913,0.0004503175,0.00074758974,0.0029119567,0.0039038106,0.006776436,0.0016773144,0.002023523,0.0017332177],"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":[0.0006756953,0.0003395878,0.0021703814,0.0057437164,0.00016149404,0.00026178596,0.00035075462,0.031143693,0.008181295,0.11443804,0.019726045,0.8168075],"study_design_scores_gemma":[0.00010054545,0.0013672601,0.0059164353,0.0033769314,0.00020339231,0.0011125529,0.0023315698,0.102152236,0.013508093,0.33536702,0.5341931,0.0003707907],"about_ca_topic_score_codex":0.0017116853,"about_ca_topic_score_gemma":0.0016132555,"teacher_disagreement_score":0.008025004,"about_ca_system_score_codex":0.0014247056,"about_ca_system_score_gemma":0.0016736335,"threshold_uncertainty_score":0.02684629},"labels":[],"label_agreement":null},{"id":"W132522011","doi":"","title":"Managing Pipeline Corrosion Control with Information Flow on the Internet","year":2000,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Canadian Society of Intestinal Research","funders":"","keywords":"Corrosion; Pipeline (software); The Internet; Flow control (data); Control (management); Flow (mathematics); Petroleum engineering; Information flow; Forensic engineering; Computer science; Environmental science; Materials science; Engineering; World Wide Web; Metallurgy; Telecommunications; Mechanical engineering; Physics; Artificial intelligence; Mechanics","score_opus":0.003018046954733871,"score_gpt":0.14326519863894394,"score_spread":0.14024715168421006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W132522011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35878035,0.0022644761,0.5049818,0.006556933,0.0006218283,0.0006608725,0.0008466209,0.01340515,0.111882016],"genre_scores_gemma":[0.9724824,0.000354997,0.020021776,0.00020401692,0.00015703203,0.0000609091,0.00033604,0.00019288635,0.0061898823],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988569,0.00032442808,0.00008922461,0.00016402609,0.000382094,0.00018334662],"domain_scores_gemma":[0.9952785,0.0024316418,0.0004076024,0.0006306853,0.0010301975,0.00022147351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022315662,0.000890334,0.00095226115,0.0025722038,0.0010474479,0.0038426805,0.0017146979,0.0015519238,0.005640405],"category_scores_gemma":[0.0060957014,0.00050649664,0.00041567205,0.0018863425,0.00064889935,0.0056743645,0.0013168757,0.0012934167,0.0011654894],"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":[0.0015304162,0.0016647942,0.0083238715,0.00022934351,0.00015076243,0.0005842144,0.00048388244,0.34897244,0.016161997,0.025636658,0.032133028,0.5641286],"study_design_scores_gemma":[0.00011522445,0.00013914623,0.0016330727,0.00002374949,0.00009502287,0.00007703557,0.00019745101,0.95550865,0.008589179,0.020744452,0.012831423,0.000045639586],"about_ca_topic_score_codex":0.006893159,"about_ca_topic_score_gemma":0.0065442575,"teacher_disagreement_score":0.006893159,"about_ca_system_score_codex":0.0012150644,"about_ca_system_score_gemma":0.0014335294,"threshold_uncertainty_score":0.018869102},"labels":[],"label_agreement":null},{"id":"W1495359447","doi":"10.14796/jwmm.r206-04","title":"Software Engineering Issues in the Design of an Upwardly-Complex Water Network Analysis Program","year":2000,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Sultan Qaboos University","keywords":"Graphical user interface; Computer science; Software; Software engineering; State (computer science); Systems engineering; Operating system; Engineering; Programming language","score_opus":0.018406737959913787,"score_gpt":0.2215831031968388,"score_spread":0.203176365236925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495359447","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":[0.019589972,0.0000722454,0.97139627,0.00027855564,0.000018958319,0.0001756356,0.000039391078,0.0041544214,0.0042745564],"genre_scores_gemma":[0.15173592,0.00016836118,0.8433043,0.00012694966,0.000018668872,0.00036231388,0.00023983506,0.0012636378,0.0027800493],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800056,0.0009147207,0.00016508407,0.00018615554,0.00060686085,0.00012651425],"domain_scores_gemma":[0.99075437,0.006605863,0.00033655425,0.0006713167,0.0014339092,0.00019791976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005471332,0.00061193114,0.00039228122,0.0007964384,0.00077721605,0.0028091006,0.0015674797,0.00071719446,0.0037982534],"category_scores_gemma":[0.015780272,0.0008806718,0.00072816946,0.0007442077,0.00089052325,0.0022017162,0.0013049813,0.0014867953,0.0009209601],"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":[0.00046361992,0.00040578266,0.008546952,0.0005395843,0.0001528771,0.0008299479,0.0022794083,0.3673826,0.025723029,0.1272524,0.0077877254,0.4586362],"study_design_scores_gemma":[0.00013237064,0.00025041847,0.0009936268,0.0001858781,0.000071501156,0.00029596436,0.00028085633,0.90342164,0.015259311,0.036895152,0.042167544,0.0000456542],"about_ca_topic_score_codex":0.0024375187,"about_ca_topic_score_gemma":0.0030026368,"teacher_disagreement_score":0.005471332,"about_ca_system_score_codex":0.0008047647,"about_ca_system_score_gemma":0.0021369308,"threshold_uncertainty_score":0.028935552},"labels":[],"label_agreement":null},{"id":"W1496075208","doi":"","title":"Detection of conditions leading to breakage of pharmaceutical tablets in pneumatic transport","year":2005,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Water Systems and 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":"Western University","funders":"","keywords":"Breakage; Kurtosis; Particle (ecology); Mechanics; Materials science; Composite material; Physics; Mathematics; Geology; Statistics","score_opus":0.009659867920454066,"score_gpt":0.22181670828434927,"score_spread":0.2121568403638952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496075208","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":[0.97505796,0.00033888963,0.02327958,0.00006562142,0.000028028473,0.000068531095,0.00020179807,0.0002589928,0.0007005595],"genre_scores_gemma":[0.9913721,0.0001055785,0.0079111,0.000021254376,0.000008553428,0.000028230394,0.00014584999,0.000019345134,0.0003879698],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959177,0.00004409288,0.000017491364,0.00009187826,0.00020192246,0.000052834155],"domain_scores_gemma":[0.9988826,0.00035346564,0.00037101077,0.00004342422,0.00026780216,0.00008169405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000306937,0.00045063635,0.00061117514,0.00066389405,0.00032588604,0.00044488814,0.00035886455,0.0007500341,0.0007695675],"category_scores_gemma":[0.0013580292,0.0002672557,0.00020391945,0.00038650015,0.00051202695,0.00041486867,0.00038448852,0.000607178,0.00023649247],"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":[0.00087646063,0.000056629044,0.008544168,0.00011209482,0.000013620722,0.00048069685,0.00012113176,0.0010694918,0.9770338,0.000066550936,0.00015723404,0.011468143],"study_design_scores_gemma":[0.000039449864,0.0009525724,0.09757011,0.000013668113,0.00003377613,0.00076405687,0.00021839779,0.032475557,0.86703813,0.000105524494,0.00073486485,0.00005384876],"about_ca_topic_score_codex":0.0010478043,"about_ca_topic_score_gemma":0.0012889298,"teacher_disagreement_score":0.0010478043,"about_ca_system_score_codex":0.00038361695,"about_ca_system_score_gemma":0.0002586495,"threshold_uncertainty_score":0.002783358},"labels":[],"label_agreement":null},{"id":"W1523224277","doi":"10.1023/a:1006774101991","title":"Genetic Algorithms for Optimizing the Remediation of Contaminated Aquifer","year":2000,"lang":"en","type":"article","venue":"Transport in Porous Media","topic":"Water Systems and 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 Alberta","funders":"","keywords":"Aquifer; Petroleum engineering; Hydrogeology; Groundwater; Plume; Injection well; Environmental science; Drawdown (hydrology); Environmental remediation; Groundwater remediation; Environmental engineering; Contamination; Engineering; Geotechnical engineering","score_opus":0.011112822671270811,"score_gpt":0.19758501735488854,"score_spread":0.18647219468361773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523224277","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":[0.19012794,0.0018777881,0.79659975,0.0013574486,0.00014329224,0.000112417205,0.00015181079,0.00046415837,0.00916544],"genre_scores_gemma":[0.871882,0.0006602712,0.12255611,0.00018961592,0.00006528717,0.00022067376,0.00015900811,0.00007186913,0.0041950685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973124,0.00013803145,0.000009697218,0.000028588875,0.000053923675,0.00003865685],"domain_scores_gemma":[0.99891615,0.0008426172,0.00007356804,0.000025240612,0.00011366234,0.000028656072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010152033,0.0007562588,0.00100652,0.0009413846,0.0004396147,0.00085257523,0.0008952778,0.0020757567,0.0010807697],"category_scores_gemma":[0.0031133015,0.0005122328,0.0005593928,0.0009801232,0.0011433776,0.00063106354,0.000681125,0.0008661366,0.00013250335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010158186,0.000007651409,0.000093046896,0.0000055564365,0.000008261375,0.000005445557,0.000005711316,0.9956815,0.000055216857,0.0012443924,0.00009638976,0.002786622],"study_design_scores_gemma":[0.000009094263,0.000006507583,0.000027698494,0.0000016823594,0.0000027559543,0.0000013432677,0.0000032266678,0.99853396,0.000054652555,0.0012863216,0.00007157184,0.0000012248662],"about_ca_topic_score_codex":0.01799565,"about_ca_topic_score_gemma":0.009532192,"teacher_disagreement_score":0.01799565,"about_ca_system_score_codex":0.0016175722,"about_ca_system_score_gemma":0.0013227874,"threshold_uncertainty_score":0.0357818},"labels":[],"label_agreement":null},{"id":"W1523570279","doi":"","title":"Using a spherical microphone array for identification of airborne sound transmission paths","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Acoustics; Reverberation; Microphone; Soundproofing; Microphone array; Transmission (telecommunications); Sound transmission class; Transmission loss; Identification (biology); Architectural acoustics; Path (computing); Computer science; Loudspeaker; Physics; Telecommunications","score_opus":0.017350119507888652,"score_gpt":0.22697707708595843,"score_spread":0.2096269575780698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523570279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054794963,0.00043450034,0.93660885,0.00008493198,0.00013465162,0.000116177056,0.00033816954,0.001616224,0.0058715004],"genre_scores_gemma":[0.25470725,0.00051966246,0.7393776,0.00009361433,0.000069553666,0.00012285754,0.00040360517,0.000071134265,0.004634694],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922144,0.00021947791,0.000038827107,0.00018716148,0.00028024867,0.00005279268],"domain_scores_gemma":[0.99939024,0.00018023416,0.00004138114,0.00007436759,0.0002840174,0.000029801737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000465944,0.0006353481,0.00060206855,0.00061085395,0.00022668215,0.00050685497,0.00037550015,0.0006684943,0.0034680413],"category_scores_gemma":[0.0010148005,0.00021782337,0.00033854134,0.0005802081,0.0002136829,0.0006215465,0.000459263,0.0003458917,0.0031798678],"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":[0.00033444076,0.00005713134,0.0018624731,0.00019259882,0.0000413401,0.00007809307,0.00009591597,0.0034782225,0.74654514,0.0011557022,0.0010286436,0.24513024],"study_design_scores_gemma":[0.00013954027,0.001944566,0.025836818,0.000053286134,0.0002261002,0.0023829741,0.00022402871,0.20100524,0.7383698,0.001443874,0.028177945,0.00019576802],"about_ca_topic_score_codex":0.0005952158,"about_ca_topic_score_gemma":0.0011853549,"teacher_disagreement_score":0.0034680413,"about_ca_system_score_codex":0.00012267423,"about_ca_system_score_gemma":0.00036830967,"threshold_uncertainty_score":0.011601746},"labels":[],"label_agreement":null},{"id":"W15371206","doi":"10.1080/15287390490492421","title":"Optimum spacing and design of drainage culverts in the hilly stretch of Buangpui-Lunglei State Road in Mizoram","year":2011,"lang":"en","type":"article","venue":"Indian highways","topic":"Water Systems and 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":"","keywords":"Culvert; Terrain; Drainage; Geotechnical engineering; Ridge; Environmental science; Slab; Inlet; Hydraulic structure; Hydrology (agriculture); Geology; Engineering; Structural engineering; Geography; Geomorphology","score_opus":0.01964121251613875,"score_gpt":0.18180349246494404,"score_spread":0.16216227994880528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W15371206","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":[0.9930535,0.000081456536,0.0048408145,0.000022989241,0.0000052254104,0.0001343897,0.00011473724,0.000092961454,0.001653952],"genre_scores_gemma":[0.99124944,0.000050082097,0.007853622,0.0000048881766,0.0000013883025,0.00006838397,0.00012223228,0.000006727111,0.00064322446],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956435,0.00009964461,0.000024276718,0.00012264073,0.00006195229,0.0001270319],"domain_scores_gemma":[0.9994474,0.00007430562,0.00013362276,0.000027784314,0.00012358815,0.00019321317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004269217,0.00041484312,0.00042401085,0.0008321489,0.0007942667,0.0011035411,0.00085889857,0.00034902192,0.0015130866],"category_scores_gemma":[0.00090800767,0.00034036624,0.0001958522,0.0007944639,0.00038972238,0.00031028345,0.00041484076,0.00021612574,0.0002877646],"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":[0.0008761238,0.0025962815,0.45952457,0.00042794523,0.00011259183,0.0009633737,0.0012753455,0.2922148,0.05002711,0.002158817,0.0025437786,0.18727933],"study_design_scores_gemma":[0.00038907063,0.0031407715,0.5323784,0.00008554388,0.00017078496,0.00036234775,0.0076944944,0.4248449,0.02318977,0.0015502315,0.0060974,0.000096401425],"about_ca_topic_score_codex":0.017241938,"about_ca_topic_score_gemma":0.06795941,"teacher_disagreement_score":0.017241938,"about_ca_system_score_codex":0.0016182591,"about_ca_system_score_gemma":0.0021149546,"threshold_uncertainty_score":0.03428316},"labels":[],"label_agreement":null},{"id":"W1539174661","doi":"10.1002/j.1551-8833.2008.tb09679.x","title":"Effect of water main repairs on water quality","year":2008,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and Optimization","field":"Engineering","cited_by":22,"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":"Canadian Water Network","keywords":"Flushing; Contamination; Environmental science; Water quality; Turbidity; Fecal coliform; Environmental engineering; Mains electricity; Hydrology (agriculture); Geotechnical engineering; Engineering; Ecology; Biology","score_opus":0.004567450884618033,"score_gpt":0.20692341621065763,"score_spread":0.20235596532603958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539174661","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":[0.99972266,0.00003191224,0.00011214078,0.000007940555,0.0000020797213,0.000005017444,0.00002514528,0.000011472925,0.00008166464],"genre_scores_gemma":[0.99960274,0.000018446595,0.00013083947,0.000003874823,9.754759e-7,0.0000029690516,0.000038639646,0.0000015219357,0.0002001167],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99937445,0.00015068515,0.000038782793,0.000153411,0.00013304615,0.0001495784],"domain_scores_gemma":[0.9976799,0.0006873867,0.00096191274,0.0001346103,0.00028326648,0.00025290126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006293824,0.00035571522,0.00028468698,0.00032336125,0.000487534,0.0005921916,0.0003956276,0.0004647102,0.001177432],"category_scores_gemma":[0.0027751748,0.00022803895,0.00027903318,0.00024711387,0.00051564275,0.0004383125,0.00040850142,0.00028859766,0.00013501156],"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":[0.002039526,0.00050282193,0.87496233,0.0001771796,0.00017182762,0.0016187797,0.0009440404,0.009721288,0.068614595,0.000074787684,0.00034536814,0.040827494],"study_design_scores_gemma":[0.000023313507,0.0033069858,0.9689399,0.000016432083,0.000085709224,0.00050544745,0.0017281522,0.004018415,0.020374844,0.0000483766,0.00092731725,0.000025224745],"about_ca_topic_score_codex":0.014837099,"about_ca_topic_score_gemma":0.033585265,"teacher_disagreement_score":0.014837099,"about_ca_system_score_codex":0.0015329841,"about_ca_system_score_gemma":0.00073271844,"threshold_uncertainty_score":0.029501438},"labels":[],"label_agreement":null},{"id":"W154034098","doi":"10.2166/ws.2003.0099","title":"Pipe breaks and the role of leaks from an economic perspective","year":2003,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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":"Body orifice; Perspective (graphical); Leak; Sustainability; Focus (optics); Dissipation; Energy (signal processing); Hydraulics; Environmental science; Computer science; Environmental economics; Petroleum engineering; Mechanics; Engineering; Mechanical engineering; Economics; Environmental engineering; Physics; Thermodynamics","score_opus":0.0021061562661776967,"score_gpt":0.17381050424041386,"score_spread":0.17170434797423617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W154034098","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":[0.61665887,0.010879547,0.19992986,0.014045462,0.00021923834,0.00019509358,0.0010486624,0.0001581713,0.15686512],"genre_scores_gemma":[0.98552203,0.0033494707,0.0032373334,0.00006828269,0.00006664203,0.000045323217,0.000054296488,0.000028581151,0.0076280762],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992269,0.00030323202,0.00002957542,0.000061448656,0.00021606384,0.00016272208],"domain_scores_gemma":[0.9971782,0.0017317436,0.0005012137,0.00009489602,0.00029535012,0.00019854262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013313066,0.0004637672,0.00055044267,0.0013835409,0.0007725666,0.0038809227,0.00070336624,0.0016283744,0.00784664],"category_scores_gemma":[0.0046704398,0.00044501346,0.0005409479,0.0018124173,0.002451374,0.0065549216,0.001464571,0.0013684846,0.0003179871],"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":[0.00024881604,0.000080177866,0.007264275,0.00030304468,0.000058600777,0.0008881062,0.00034448737,0.36918703,0.0019989202,0.58650106,0.002045539,0.031079873],"study_design_scores_gemma":[0.000036715184,0.00051425415,0.019935807,0.00033653932,0.00010327258,0.00079943944,0.004188605,0.35336322,0.0023315644,0.5678095,0.050445028,0.00013601789],"about_ca_topic_score_codex":0.002783019,"about_ca_topic_score_gemma":0.0028917594,"teacher_disagreement_score":0.00784664,"about_ca_system_score_codex":0.0024420854,"about_ca_system_score_gemma":0.0009374374,"threshold_uncertainty_score":0.026249647},"labels":[],"label_agreement":null},{"id":"W1545789443","doi":"10.3233/978-1-60750-606-5-655","title":"Automating Layouts of Sewers in Subdivisions","year":2010,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Water Systems and 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":"Sanitary sewer; Subdivision; Computer science; Engineering; Civil engineering; Waste management","score_opus":0.018043419681822177,"score_gpt":0.22343675923780043,"score_spread":0.20539333955597824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1545789443","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":[0.058645688,0.00034155976,0.92273366,0.0001194812,0.000055288638,0.00015634047,0.00026428653,0.0093299365,0.008353706],"genre_scores_gemma":[0.23250878,0.00033659706,0.7583529,0.000028166483,0.000012093729,0.000086622626,0.0006563325,0.00076578895,0.0072527993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968874,0.000048760066,0.000013895054,0.00009096055,0.00011420471,0.000043453758],"domain_scores_gemma":[0.999645,0.0001296231,0.000033152148,0.00011014838,0.000055986235,0.000026033036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000320594,0.00086131954,0.0012357302,0.00047994894,0.00077835814,0.0010343564,0.0014796258,0.00082666555,0.006656838],"category_scores_gemma":[0.0011011154,0.0008573058,0.000793568,0.0008998887,0.00048704143,0.0009225234,0.001225358,0.0008208194,0.0021597184],"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":[0.00016149349,0.00011391684,0.001870089,0.00030890832,0.000040179133,0.0002504774,0.00070522155,0.35881707,0.06894743,0.013610502,0.009601267,0.5455735],"study_design_scores_gemma":[0.000065498134,0.00012483555,0.0019102162,0.000041626237,0.00003839187,0.0003006555,0.00028272305,0.88350475,0.04873681,0.01733829,0.047612336,0.000043919554],"about_ca_topic_score_codex":0.0053559984,"about_ca_topic_score_gemma":0.012063146,"teacher_disagreement_score":0.006656838,"about_ca_system_score_codex":0.000568915,"about_ca_system_score_gemma":0.00085645664,"threshold_uncertainty_score":0.022269309},"labels":[],"label_agreement":null},{"id":"W1546248909","doi":"10.21236/ada487571","title":"Detailed Field Investigation of Vapor Intrusion Processes","year":2008,"lang":"en","type":"report","venue":"","topic":"Water Systems and 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":"Intrusion; Field (mathematics); Geology; Environmental science; Geochemistry; Mathematics","score_opus":0.022567500598945238,"score_gpt":0.21565393217009396,"score_spread":0.19308643157114871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546248909","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":[0.94934255,0.00022689103,0.043104216,0.00012279194,0.000018909326,0.0014162958,0.00066336116,0.00036319514,0.004741814],"genre_scores_gemma":[0.9603424,0.00021391471,0.035620183,0.00006057923,0.000011756202,0.0005716754,0.00042091386,0.000026910291,0.0027316972],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99928313,0.0001464544,0.00004112289,0.00015091946,0.00028932255,0.00008909684],"domain_scores_gemma":[0.9991817,0.00019974378,0.00014186911,0.00013225168,0.00030671473,0.000037781265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093750306,0.00026658538,0.00032356635,0.00037262525,0.0005837704,0.00033502132,0.00047087847,0.00045349143,0.0018077692],"category_scores_gemma":[0.0009719974,0.00016965954,0.00022860865,0.00029646713,0.00031565502,0.00051018206,0.00068613974,0.00046174385,0.00032371812],"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":[0.0003503478,0.00049064285,0.014275296,0.00020786819,0.000015464913,0.0003291226,0.0005191754,0.0030946585,0.9464532,0.0006129297,0.00054291426,0.03310848],"study_design_scores_gemma":[0.00009692923,0.007490882,0.068857804,0.000050448525,0.000050507064,0.00056760985,0.0009200964,0.019337837,0.8898953,0.0006278915,0.01204194,0.00006272334],"about_ca_topic_score_codex":0.0021750613,"about_ca_topic_score_gemma":0.005398145,"teacher_disagreement_score":0.0021750613,"about_ca_system_score_codex":0.00041070112,"about_ca_system_score_gemma":0.00078980497,"threshold_uncertainty_score":0.0060476065},"labels":[],"label_agreement":null},{"id":"W1547185164","doi":"10.1002/j.1551-8833.2007.tb07846.x","title":"Constructing water main break databases for asset management","year":2007,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of British Columbia; U.S. Department of Agriculture","keywords":"Asset (computer security); Asset management; Computer science; Data warehouse; Data management; Database; Data collection; Data science; Risk analysis (engineering); Business; Finance; Computer security","score_opus":0.006005294298460457,"score_gpt":0.21282587336569722,"score_spread":0.20682057906723678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547185164","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":[0.021427432,0.000925014,0.8922598,0.0021586341,0.00023333446,0.0014725239,0.032705486,0.020247936,0.028569782],"genre_scores_gemma":[0.08660883,0.0010212763,0.8505457,0.00027171473,0.00007743138,0.00087075174,0.055796713,0.0008580442,0.0039494787],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961378,0.00065203104,0.0006334043,0.00070629123,0.0016388634,0.00023157756],"domain_scores_gemma":[0.9882372,0.0019240004,0.0011435475,0.005261922,0.0029025066,0.0005307778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051157605,0.00066284934,0.0009267583,0.00952648,0.0019050427,0.008230113,0.0033860544,0.0012945585,0.0051798136],"category_scores_gemma":[0.0191899,0.0010336756,0.0012407126,0.011895003,0.0007504852,0.009045308,0.004903821,0.0019916394,0.0040110704],"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":[0.00019538718,0.00041889903,0.028239796,0.00070525554,0.00023357425,0.00071531814,0.0021376137,0.037472688,0.008041894,0.15001184,0.12632196,0.6455057],"study_design_scores_gemma":[0.00008765713,0.00015899654,0.01511235,0.00057903054,0.00015249249,0.0006388277,0.0049901847,0.17399776,0.018915772,0.10832042,0.6768376,0.00020887644],"about_ca_topic_score_codex":0.0121145295,"about_ca_topic_score_gemma":0.012612282,"teacher_disagreement_score":0.0121145295,"about_ca_system_score_codex":0.0020496899,"about_ca_system_score_gemma":0.0044625527,"threshold_uncertainty_score":0.027055085},"labels":[],"label_agreement":null},{"id":"W1553627555","doi":"10.1109/cisti.2015.7170477","title":"Classification of water for production using parameters in real time","year":2015,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Binary classification; Artificial intelligence; Multiclass classification; Binary number; Computer science; Feature (linguistics); Heuristic; Production (economics); Pattern recognition (psychology); Feature selection; Decision tree; Selection (genetic algorithm); Random forest; Quality (philosophy); Data mining; Machine learning; Mathematics; Support vector machine","score_opus":0.05917627962406652,"score_gpt":0.24017294636004402,"score_spread":0.1809966667359775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553627555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35072482,0.0010394417,0.6402261,0.00020399925,0.00021324083,0.00012965621,0.001436861,0.0017753706,0.0042505483],"genre_scores_gemma":[0.93254244,0.0002295169,0.06506806,0.000023180537,0.000045491797,0.000066431945,0.00093019777,0.00006675278,0.0010280138],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994004,0.00008431764,0.0000561578,0.00017890247,0.0002289756,0.00005131628],"domain_scores_gemma":[0.9991425,0.00030908227,0.00017047345,0.0001382494,0.0002121457,0.00002755293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045537492,0.00064324745,0.0006024038,0.0016126849,0.00018116312,0.0009766639,0.00049598987,0.0007291669,0.000906818],"category_scores_gemma":[0.0019116602,0.0001719202,0.00042038524,0.0016134284,0.00033975998,0.0013170388,0.00023377691,0.00044942315,0.000549896],"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":[0.0005760698,0.0002575615,0.05777603,0.00038903017,0.00018533527,0.00028364654,0.00018941762,0.16385134,0.07585945,0.0016294324,0.0028449476,0.69615775],"study_design_scores_gemma":[0.0000281549,0.0003245578,0.066994786,0.00004799369,0.00007760714,0.00027776576,0.00022834036,0.86926,0.05136171,0.003511836,0.0077833426,0.00010378666],"about_ca_topic_score_codex":0.0013755739,"about_ca_topic_score_gemma":0.001366682,"teacher_disagreement_score":0.0016126849,"about_ca_system_score_codex":0.00041461975,"about_ca_system_score_gemma":0.00028192895,"threshold_uncertainty_score":0.003033638},"labels":[],"label_agreement":null},{"id":"W1560495772","doi":"10.14796/jwmm.r227-05","title":"Mechanisms for Stormwater Surges in Vertical Shafts","year":2007,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":15,"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":"Stormwater; Storm surge; Stormwater management; Surge; Business; Environmental science; Geology; Surface runoff; Oceanography; Storm; Geomorphology","score_opus":0.01422317055514438,"score_gpt":0.2119419798104099,"score_spread":0.19771880925526553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560495772","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":[0.9335796,0.0007829496,0.048834983,0.0010119177,0.00018673111,0.0001741912,0.00019201275,0.0009499408,0.014287696],"genre_scores_gemma":[0.9974962,0.0001356844,0.0006791885,0.000015908556,0.000024165247,0.0000159737,0.000023725663,0.0000101258165,0.0015990665],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984217,0.000013147357,0.000008699615,0.000024451061,0.000045038996,0.00006646138],"domain_scores_gemma":[0.99958843,0.000098259436,0.00016413965,0.00003337286,0.00006212122,0.0000537337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028928163,0.00038259907,0.0003460663,0.00063822814,0.0005806252,0.00062905374,0.0007247736,0.00062577,0.007233729],"category_scores_gemma":[0.0007307742,0.00042756237,0.00043079027,0.00020453772,0.0008632117,0.0007733521,0.00072587095,0.0003718405,0.00066340645],"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":[0.0014342386,0.0007467251,0.033236347,0.0010566411,0.00014041433,0.008035934,0.0013584623,0.19422172,0.58283085,0.11948764,0.0073430426,0.050107975],"study_design_scores_gemma":[0.000669677,0.0015132044,0.06430552,0.0001297817,0.00007350268,0.0020166468,0.0014198589,0.82106715,0.061716165,0.039615665,0.0073175635,0.0001553251],"about_ca_topic_score_codex":0.0016109669,"about_ca_topic_score_gemma":0.0009997641,"teacher_disagreement_score":0.007233729,"about_ca_system_score_codex":0.00062618847,"about_ca_system_score_gemma":0.00031599525,"threshold_uncertainty_score":0.024199247},"labels":[],"label_agreement":null},{"id":"W1570985969","doi":"10.1007/978-3-540-27009-6_85","title":"Investigation of imploding shock waves using the hydraulic analogy","year":2005,"lang":"en","type":"book-chapter","venue":"Shock Waves","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Shock wave; Mechanics; Shock (circulatory); Hydraulic jump; Range (aeronautics); Physics; Materials science; Medicine; Composite material","score_opus":0.028760749975950985,"score_gpt":0.213592368356586,"score_spread":0.184831618380635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1570985969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061233897,0.009450398,0.65103585,0.0016559452,0.0007053806,0.00011787622,0.00016093953,0.0003980546,0.2752416],"genre_scores_gemma":[0.7190731,0.010976842,0.11095783,0.00040425934,0.00056478725,0.00011928227,0.0002192456,0.0002160096,0.15746863],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998816,0.000034965233,0.0000057468073,0.000014161035,0.00005172763,0.000011742686],"domain_scores_gemma":[0.999795,0.00012702281,0.000013844228,0.000018261942,0.00003773033,0.000008121784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002582737,0.00058765616,0.00059413415,0.00061313604,0.00029818466,0.00119885,0.0007827321,0.0008832424,0.006183849],"category_scores_gemma":[0.00090036,0.0003708498,0.00045321466,0.0006980557,0.0009414232,0.0014608616,0.000666092,0.0013410968,0.00050818414],"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":[0.0000673786,0.00008932814,0.00034579576,0.00038156926,0.000030585084,0.00050407794,0.00023184411,0.125738,0.013219771,0.78901017,0.0057340804,0.064647354],"study_design_scores_gemma":[0.00002332906,0.00007618254,0.00091778697,0.000060695977,0.000020447078,0.00038715408,0.00015760104,0.6742491,0.006976138,0.29783747,0.019264655,0.000029501216],"about_ca_topic_score_codex":0.0008183133,"about_ca_topic_score_gemma":0.00070504367,"teacher_disagreement_score":0.006183849,"about_ca_system_score_codex":0.00044355573,"about_ca_system_score_gemma":0.00037852948,"threshold_uncertainty_score":0.020687103},"labels":[],"label_agreement":null},{"id":"W1580203092","doi":"","title":"TRANSPORTE DE CONTAMINANTES ORGÂNICOS NA FRANJA CAPILAR: IMPLICAÇÕES PARA A INVESTIGAÇÃO DE ÁREAS CONTAMINADAS","year":2011,"lang":"pt","type":"article","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Water Systems and 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":"Chemistry","score_opus":0.03047024213132944,"score_gpt":0.2248949111932405,"score_spread":0.19442466906191108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580203092","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":[0.99091035,0.0010679258,0.005777823,0.00014839772,0.000024368856,0.000050120332,0.00026605348,0.00008468787,0.0016703026],"genre_scores_gemma":[0.9890878,0.0010593353,0.0072563747,0.0000491486,0.0000069431476,0.000040727013,0.00022325174,0.000022753093,0.0022536363],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99945873,0.000071338894,0.000016520413,0.00016444564,0.00021448308,0.000074488096],"domain_scores_gemma":[0.9994012,0.00015867544,0.00012397625,0.00003395125,0.0002398527,0.00004233078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006009518,0.0006926982,0.0006556204,0.00059054914,0.00086977176,0.0016660199,0.0006852921,0.0013362628,0.001680729],"category_scores_gemma":[0.0007094388,0.0002999925,0.0007137981,0.00060459296,0.00045110958,0.0007330638,0.00039824555,0.00060541433,0.00031527845],"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":[0.00044866564,0.00011148038,0.015048927,0.0005872494,0.00005388171,0.00025970017,0.00021087819,0.004402326,0.96410185,0.0003390204,0.00016887415,0.014267087],"study_design_scores_gemma":[0.000026134872,0.0018650163,0.033628967,0.00009019923,0.00016726946,0.00024439953,0.00087994273,0.020962264,0.9363152,0.00024367106,0.0055136764,0.00006327822],"about_ca_topic_score_codex":0.031661462,"about_ca_topic_score_gemma":0.041318238,"teacher_disagreement_score":0.031661462,"about_ca_system_score_codex":0.0016895786,"about_ca_system_score_gemma":0.0013661967,"threshold_uncertainty_score":0.06295431},"labels":[],"label_agreement":null},{"id":"W1581132942","doi":"10.1111/j.1467-8667.2012.00773.x","title":"A Survival Analysis Model for Sewer Pipe Structural Deterioration","year":2012,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Weibull distribution; Calibration; Convolution (computer science); Exponential function; Survival function; Sample (material); Statistics; Exponential distribution; Function (biology); Survival analysis; Engineering; Mathematics; Computer science; Artificial intelligence; Physics","score_opus":0.006721659024082559,"score_gpt":0.18603061487722108,"score_spread":0.17930895585313852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581132942","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":[0.0963106,0.0007139273,0.8918155,0.0010011508,0.00008614621,0.00009389786,0.00075872394,0.00044071247,0.008779354],"genre_scores_gemma":[0.9429903,0.00089710887,0.022013403,0.00015125061,0.00007908095,0.0002291983,0.00058154773,0.00011380904,0.03294435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965894,0.000110571535,0.000014462918,0.00008044995,0.000051744326,0.00008394497],"domain_scores_gemma":[0.9986738,0.00083721406,0.00013739427,0.00004399902,0.00022904032,0.00007861475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001585248,0.0009853579,0.0016119381,0.0012471356,0.00080200256,0.0011710792,0.0023087391,0.0026361998,0.00534284],"category_scores_gemma":[0.00344339,0.0007265392,0.0012326539,0.0011532563,0.0014238782,0.0011555969,0.0010384435,0.0015855477,0.0008952176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017473792,0.000012088931,0.0005255959,0.000022411195,0.000018331057,0.00005634029,0.000057023062,0.9824499,0.00042650546,0.012561189,0.00046967552,0.0033835461],"study_design_scores_gemma":[0.0000033438316,0.000006846057,0.000106733496,0.0000027316607,0.000008242311,0.000009146938,0.000007718745,0.99741083,0.00003355666,0.0022699966,0.00013722482,0.0000037058758],"about_ca_topic_score_codex":0.03461025,"about_ca_topic_score_gemma":0.017301206,"teacher_disagreement_score":0.03461025,"about_ca_system_score_codex":0.0019804717,"about_ca_system_score_gemma":0.0015843299,"threshold_uncertainty_score":0.068817556},"labels":[],"label_agreement":null},{"id":"W1582743648","doi":"10.5220/0003063801530162","title":"MULTI-OBJECTIVE OPTIMIZATION OF BOTH PUMPING ENERGY AND MAINTENANCE COSTS IN OIL PIPELINE NETWORKS USING GENETIC ALGORITHMS","year":2010,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Pipeline (software); Computer science; Genetic algorithm; Pipeline transport; Energy (signal processing); Mathematical optimization; Optimization algorithm; Algorithm; Engineering; Machine learning; Mathematics; Mechanical engineering; Programming language; Statistics","score_opus":0.007426476632015,"score_gpt":0.19973729782631275,"score_spread":0.19231082119429774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1582743648","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":[0.27924156,0.0011111947,0.71033543,0.00081634626,0.00009762724,0.00015741603,0.00024089738,0.0002854032,0.007714159],"genre_scores_gemma":[0.9215825,0.0003270822,0.07384318,0.000066332315,0.00003831029,0.00018854107,0.00013307742,0.000060204646,0.0037607462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994567,0.00026871584,0.000020739271,0.00006743636,0.000103023114,0.00008331299],"domain_scores_gemma":[0.99821484,0.0013724981,0.00015424658,0.000037714053,0.00016094082,0.000059875998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002169442,0.0013195844,0.0019161253,0.0014842524,0.0006547665,0.0019217492,0.0013649103,0.002384972,0.0016752844],"category_scores_gemma":[0.004407518,0.0013437849,0.0010877537,0.0016627429,0.00101203,0.0016147131,0.00079850963,0.0012012505,0.00015016423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008714117,0.0000073439455,0.000054260323,0.0000046270266,0.0000073266524,0.000005394252,0.0000027925398,0.9984296,0.000048995294,0.00027627262,0.000030461122,0.0011241618],"study_design_scores_gemma":[0.0000057215675,0.000008055748,0.000038053076,0.0000012164414,0.000004088112,0.0000012381147,0.0000021030573,0.9995161,0.000055144108,0.0003434521,0.000023546107,0.0000013415586],"about_ca_topic_score_codex":0.02122219,"about_ca_topic_score_gemma":0.016179478,"teacher_disagreement_score":0.02122219,"about_ca_system_score_codex":0.0027346706,"about_ca_system_score_gemma":0.0016849819,"threshold_uncertainty_score":0.042197287},"labels":[],"label_agreement":null},{"id":"W1588969859","doi":"10.1002/j.1551-8833.2006.tb07712.x","title":"Assessment of water main break data for ASSET MANAGEMENT","year":2006,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and Optimization","field":"Engineering","cited_by":16,"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":"Data collection; Asset (computer security); Survey data collection; Data set; Asset management; Set (abstract data type); Data processing; Computer science; Data management; Business; Data mining; Statistics; Database; Finance; Mathematics; Computer security","score_opus":0.005675870370562704,"score_gpt":0.22094486811804673,"score_spread":0.21526899774748404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588969859","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":[0.87134534,0.0019472252,0.07220603,0.007919204,0.00014487373,0.001448879,0.0128718885,0.00071134523,0.031405248],"genre_scores_gemma":[0.94085854,0.00041413802,0.05299752,0.00011123828,0.000045291563,0.00028686883,0.0044282447,0.000035823214,0.0008223658],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9656118,0.017664665,0.0025432946,0.0011502703,0.012417787,0.0006121903],"domain_scores_gemma":[0.6903585,0.20042166,0.020130226,0.026889538,0.05854497,0.0036551196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03845182,0.00045708843,0.00072863174,0.006278718,0.0011154839,0.0040713944,0.0013171171,0.0007603952,0.0027369107],"category_scores_gemma":[0.18415216,0.00036793962,0.000540627,0.007246215,0.0008622417,0.005996003,0.0026012238,0.0010502547,0.0004082844],"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":[0.0011840116,0.0005213506,0.38094014,0.0012940429,0.00035882372,0.0003024303,0.0036426643,0.037180774,0.0049958546,0.01652658,0.0097972,0.5432562],"study_design_scores_gemma":[0.00025829786,0.002339606,0.57719153,0.0018991453,0.00032621506,0.0004629571,0.019761309,0.23635605,0.015872896,0.02280084,0.12245362,0.0002774456],"about_ca_topic_score_codex":0.011903262,"about_ca_topic_score_gemma":0.01386284,"teacher_disagreement_score":0.03845182,"about_ca_system_score_codex":0.0025486252,"about_ca_system_score_gemma":0.0035460687,"threshold_uncertainty_score":0.20335507},"labels":[],"label_agreement":null},{"id":"W1651686692","doi":"10.1029/2002wr001904","title":"Optimal replacement of water pipes","year":2003,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Research Council Canada","keywords":"Hazard; Constant (computer programming); Minification; Exponential function; Function (biology); Probability density function; Basis (linear algebra); Mathematics; Mathematical optimization; Statistics; Computer science; Mathematical analysis; Geometry","score_opus":0.029217086453595626,"score_gpt":0.26795517642055267,"score_spread":0.23873808996695703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1651686692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48157418,0.0014298917,0.5104026,0.00038566522,0.000093586335,0.0001545841,0.00021211152,0.00048532878,0.005262141],"genre_scores_gemma":[0.9706198,0.00018325335,0.02730069,0.000026697768,0.000009775733,0.000048353417,0.000099207966,0.0000407527,0.001671487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914706,0.00029896825,0.000047369867,0.00016702127,0.00013321037,0.00020642082],"domain_scores_gemma":[0.9985877,0.0005844159,0.00033204452,0.00016842593,0.00018822378,0.00013925739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014411905,0.0006110109,0.0012493196,0.0006610939,0.0004212443,0.00073501543,0.0008524749,0.0010725392,0.0019734174],"category_scores_gemma":[0.005539426,0.0005442771,0.00052771706,0.0004367401,0.0008084926,0.0013037061,0.0006747738,0.00049424905,0.00028203486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003823135,0.00009573154,0.0024722547,0.00013740847,0.000030476922,0.00016126783,0.00009267653,0.9315967,0.006429834,0.0097469585,0.0011660215,0.047688473],"study_design_scores_gemma":[0.000034754426,0.00030241063,0.0011959597,0.000024902689,0.000028510778,0.00008004354,0.000087854554,0.98464555,0.0033587262,0.008842098,0.0013837718,0.00001541545],"about_ca_topic_score_codex":0.0033897804,"about_ca_topic_score_gemma":0.0029088503,"teacher_disagreement_score":0.0033897804,"about_ca_system_score_codex":0.0010644139,"about_ca_system_score_gemma":0.0012467875,"threshold_uncertainty_score":0.007722914},"labels":[],"label_agreement":null},{"id":"W1651775740","doi":"10.1029/2008wr007673","title":"Hybrid discrete dynamically dimensioned search (HD‐DDS) algorithm for water distribution system design optimization","year":2009,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":131,"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":"Algorithm; Meta-optimization; Computer science; Mathematical optimization; Metaheuristic; Ant colony optimization algorithms; Local search (optimization); Genetic algorithm; Population; Particle swarm optimization; Global optimization; Search algorithm; Hybrid algorithm (constraint satisfaction); Optimization problem; Heuristics; Mathematics","score_opus":0.02214005031540313,"score_gpt":0.2581974665869447,"score_spread":0.2360574162715416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1651775740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008649855,0.00032617414,0.98719853,0.000110307985,0.000040649607,0.00006529551,0.00004882011,0.00034638442,0.003213998],"genre_scores_gemma":[0.29681605,0.00038565797,0.69798994,0.00015744181,0.00003236934,0.00045748928,0.0002532936,0.000092101094,0.0038156828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995536,0.00014337426,0.000026826627,0.00006469327,0.00017853089,0.000032949953],"domain_scores_gemma":[0.99950445,0.00029000387,0.00004398823,0.000038646787,0.00010273418,0.000020183945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009808957,0.00086377695,0.0011722066,0.00095325016,0.00039654475,0.00077414286,0.0010839646,0.00087642914,0.0021230024],"category_scores_gemma":[0.0014004643,0.00045593525,0.00077156373,0.0009992855,0.0006451809,0.0006783411,0.00090456527,0.0009853754,0.00034562434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004314322,0.000031305543,0.00047140504,0.00006278834,0.000046948862,0.000027438618,0.000043433272,0.9017343,0.0010612558,0.0069050584,0.0012178955,0.08835494],"study_design_scores_gemma":[0.000023680568,0.000023238119,0.00006348525,0.0000045719485,0.0000054728557,0.000011799158,0.0000051498055,0.99710447,0.00034790413,0.0012823669,0.001123906,0.0000039639813],"about_ca_topic_score_codex":0.0054463577,"about_ca_topic_score_gemma":0.006066954,"teacher_disagreement_score":0.0054463577,"about_ca_system_score_codex":0.0009564078,"about_ca_system_score_gemma":0.0015291899,"threshold_uncertainty_score":0.01082927},"labels":[],"label_agreement":null},{"id":"W1662399702","doi":"10.5942/jawwa.2016.108.0008","title":"Using Decision Trees to Predict Drinking Water Advisories in Small Water Systems","year":2015,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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":true,"ca_institutions":"University of Guelph","funders":"Aboriginal Affairs and Northern Development Canada; Natural Sciences and Engineering Research Council of Canada; Health Canada","keywords":"Water source; Decision tree; Water use; Water infrastructure; Environmental planning; Geographic information system; Environmental resource management; Environmental science; Computer science; Water resource management; Geography; Water supply; Environmental engineering; Data mining; Ecology; Cartography","score_opus":0.015732023462669553,"score_gpt":0.21513462034933323,"score_spread":0.19940259688666367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1662399702","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":[0.9452268,0.00033460464,0.05014322,0.0005397292,0.00003906794,0.00013958436,0.0015124858,0.0002643801,0.0018000354],"genre_scores_gemma":[0.9817724,0.00014641872,0.016414769,0.000027505257,0.000009734238,0.000039664916,0.0010798274,0.0000100107045,0.0004995753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994573,0.0002614776,0.00003623213,0.000067099085,0.000094977484,0.00008297193],"domain_scores_gemma":[0.9931593,0.0056956774,0.00037195903,0.00008643145,0.00047474823,0.00021182201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017220983,0.00070602936,0.00047765166,0.0019299726,0.0004546055,0.0008170186,0.00048390005,0.0004622789,0.0010870716],"category_scores_gemma":[0.007798208,0.00031327578,0.00050386146,0.001437359,0.00023201933,0.0007750896,0.00035713828,0.0006975959,0.00014329662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017075536,0.00019986567,0.048296593,0.000058004243,0.000064688735,0.0001243298,0.00007520853,0.9136776,0.0002583163,0.0007581029,0.001204468,0.035112105],"study_design_scores_gemma":[0.000008045897,0.000030553245,0.0033895369,0.000009084207,0.0000091403535,0.0000070051183,0.000072023664,0.99489224,0.00019573844,0.0011474066,0.00023373221,0.0000055107116],"about_ca_topic_score_codex":0.07181267,"about_ca_topic_score_gemma":0.058643878,"teacher_disagreement_score":0.07181267,"about_ca_system_score_codex":0.0013321552,"about_ca_system_score_gemma":0.0016516823,"threshold_uncertainty_score":0.1427893},"labels":[],"label_agreement":null},{"id":"W16749183","doi":"10.1016/s0097-8485(96)80011-8","title":"New Fuzzy Performance Indices for Reliability Analysis of Water Supply Systems","year":2003,"lang":"en","type":"article","venue":"Computers & Chemistry","topic":"Water Systems and Optimization","field":"Engineering","cited_by":18,"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; Institute for Catastrophic Loss Reduction","keywords":"Reliability (semiconductor); Reliability engineering; Fuzzy logic; Computer science; Water supply; Risk analysis (engineering); Environmental science; Engineering; Business; Artificial intelligence; Environmental engineering","score_opus":0.0050290650371629395,"score_gpt":0.17382881702790498,"score_spread":0.16879975199074204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W16749183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032947183,0.0017638113,0.95806736,0.0003605154,0.00011305101,0.000113575545,0.00036306173,0.0002648034,0.0060065766],"genre_scores_gemma":[0.8592559,0.001306,0.13460514,0.00011196745,0.00029832238,0.0002675054,0.00079177564,0.00010512962,0.0032582101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977915,0.0007612069,0.00017621447,0.0003622762,0.0007337457,0.00017509512],"domain_scores_gemma":[0.99443793,0.0032060596,0.00061474036,0.0003390932,0.0012438862,0.00015825118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006000743,0.001533763,0.0014059419,0.0042117722,0.0006203824,0.0024654414,0.0016311177,0.0011330913,0.0032390591],"category_scores_gemma":[0.013350034,0.00036403487,0.001314501,0.0020837332,0.0010480484,0.0023099356,0.001047916,0.0018652449,0.00054154167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001699004,0.00008208226,0.0033322782,0.00030529068,0.00020555088,0.00013936643,0.00022101529,0.8219375,0.002711307,0.08698283,0.0026137698,0.081299014],"study_design_scores_gemma":[0.000003915871,0.000043717435,0.0008028623,0.000030205058,0.00002377343,0.000025844234,0.000036672347,0.9759301,0.00040433658,0.021917747,0.000761534,0.000019413357],"about_ca_topic_score_codex":0.00354246,"about_ca_topic_score_gemma":0.002018085,"teacher_disagreement_score":0.006000743,"about_ca_system_score_codex":0.0021605047,"about_ca_system_score_gemma":0.0007511392,"threshold_uncertainty_score":0.03173536},"labels":[],"label_agreement":null},{"id":"W1747612108","doi":"10.1061/(asce)hy.1943-7900.0001067","title":"Physical Understanding of Sudden Pressurization of Pipe Systems with Entrapped Air: Energy Auditing Approach","year":2015,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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 Toronto; HydraTek (Canada)","funders":"","keywords":"Cabin pressurization; Transient (computer programming); Mechanics; Energy (signal processing); Event (particle physics); Environmental science; Petroleum engineering; Engineering; Mechanical engineering; Physics; Computer science","score_opus":0.015826229610319816,"score_gpt":0.17701102048965586,"score_spread":0.16118479087933604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1747612108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12517151,0.002268961,0.83799875,0.0014807171,0.00025349518,0.00007610101,0.00018035446,0.00023276782,0.032337423],"genre_scores_gemma":[0.9789611,0.0014241718,0.013783126,0.0001182528,0.0001495691,0.00006459821,0.000054747085,0.000043087366,0.0054012313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998311,0.00003697558,0.000009715024,0.000030512201,0.000059653798,0.000031988497],"domain_scores_gemma":[0.99968076,0.0001375476,0.0000611742,0.0000427385,0.000055544002,0.000022256698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033575896,0.00061633094,0.0006613425,0.00062777865,0.00043468806,0.001009136,0.0008457332,0.00070042536,0.002561145],"category_scores_gemma":[0.0009449926,0.00032661512,0.00051235047,0.00031710463,0.0015661378,0.002626129,0.0012753761,0.0012511243,0.00019361022],"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":[0.00006663764,0.00008175999,0.0012636213,0.00022710998,0.000024356692,0.0007812321,0.00022383817,0.70265156,0.026319062,0.25102744,0.0010021256,0.016331274],"study_design_scores_gemma":[0.0000049870814,0.00004334027,0.0008468035,0.000011296071,0.000006814647,0.00010516737,0.00006790258,0.9453126,0.0016542838,0.050604265,0.0013238209,0.000018635334],"about_ca_topic_score_codex":0.001181323,"about_ca_topic_score_gemma":0.00055696175,"teacher_disagreement_score":0.002561145,"about_ca_system_score_codex":0.0005348119,"about_ca_system_score_gemma":0.0005711559,"threshold_uncertainty_score":0.008567929},"labels":[],"label_agreement":null},{"id":"W1748676127","doi":"10.14796/jwmm.r236-30","title":"Survival Rate Analyses of Watermains: A Comparison of Case Studies for Canada and Iran","year":2010,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"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 Guelph","funders":"Canada Research Chairs","keywords":"Statistics; Mathematics","score_opus":0.07300742150526211,"score_gpt":0.308511719595013,"score_spread":0.23550429808975087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1748676127","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":[0.9923741,0.00040750974,0.002866418,0.00014978857,0.0000076950155,0.00008917701,0.0011193524,0.000053013646,0.0029329567],"genre_scores_gemma":[0.9960517,0.0003156468,0.0012891187,0.000008789952,0.000003677577,0.000023871655,0.0011476494,0.000016279955,0.0011431724],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99927276,0.0001735521,0.000025328243,0.0000715781,0.00020891958,0.00024780066],"domain_scores_gemma":[0.9949935,0.002664155,0.0003612784,0.00022176428,0.0015241904,0.00023508798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023425093,0.00052279007,0.0005253364,0.003492867,0.0008322837,0.0009005612,0.0015839518,0.0006713188,0.0022010647],"category_scores_gemma":[0.006021475,0.00019663997,0.0012751186,0.0032865626,0.0008313527,0.00043311965,0.0004636335,0.0006043634,0.00017516939],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009308638,0.0004471149,0.5999152,0.0002989628,0.0006133161,0.003111814,0.0021555913,0.31944954,0.0016086715,0.011739877,0.0074944063,0.052234687],"study_design_scores_gemma":[0.000084520725,0.00033358342,0.55199796,0.0000727124,0.0003971433,0.0015953482,0.0069061094,0.42700985,0.0019699272,0.0033442357,0.006117377,0.00017128323],"about_ca_topic_score_codex":0.77172893,"about_ca_topic_score_gemma":0.7546402,"teacher_disagreement_score":0.22827107,"about_ca_system_score_codex":0.010433595,"about_ca_system_score_gemma":0.005420649,"threshold_uncertainty_score":0.45923078},"labels":[],"label_agreement":null},{"id":"W1836181586","doi":"10.1115/imece2014-36332","title":"Operational Costs Optimization in Water Distribution Systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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","funders":"McMaster University","keywords":"Mathematical optimization; Computer science; Optimization problem; Modal; Energy consumption; Space (punctuation); Set (abstract data type); Energy management; Multi-objective optimization; Energy (signal processing); Operations research; Engineering; Mathematics","score_opus":0.00381726825336477,"score_gpt":0.1623467745330249,"score_spread":0.15852950627966014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1836181586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12193934,0.006032848,0.8233177,0.0021458238,0.00015420679,0.00027274166,0.0005515852,0.00019934254,0.04538638],"genre_scores_gemma":[0.94247025,0.0021871466,0.043004695,0.00009447468,0.00006480548,0.00023227885,0.00024829688,0.00010690866,0.011591122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987546,0.0005764628,0.00004527261,0.00012930686,0.00030983007,0.00018462216],"domain_scores_gemma":[0.9989766,0.0006516782,0.00012589718,0.000031517517,0.00015372245,0.00006062699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014913987,0.0009306335,0.0008855134,0.0012290535,0.0004767353,0.002190958,0.000861893,0.0010198151,0.0033027898],"category_scores_gemma":[0.003918079,0.00055009255,0.0005800121,0.0016079664,0.00088192837,0.0016533501,0.00080021477,0.0009037326,0.00022138494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030613573,0.000023402949,0.00040868457,0.000095683245,0.00003708551,0.000046975536,0.000025681942,0.9644181,0.00033570736,0.020802164,0.0006196725,0.0131561905],"study_design_scores_gemma":[0.000011448672,0.00003459736,0.0007150997,0.000024807765,0.000020728476,0.000023815272,0.000054428874,0.9761973,0.00033978975,0.020453596,0.0021126808,0.000011566427],"about_ca_topic_score_codex":0.013121913,"about_ca_topic_score_gemma":0.008287526,"teacher_disagreement_score":0.013121913,"about_ca_system_score_codex":0.0030721128,"about_ca_system_score_gemma":0.0017195647,"threshold_uncertainty_score":0.02609104},"labels":[],"label_agreement":null},{"id":"W1846058565","doi":"10.1016/j.proeng.2015.08.931","title":"Application of Energy Use Indicators to Evaluate Energy Dynamics in Canadian Water Distribution Systems","year":2015,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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":true,"ca_institutions":"Queen's University","funders":"","keywords":"Energy (signal processing); Energy consumption; Efficient energy use; Leakage (economics); Energy accounting; Performance indicator; Energy distribution; Distribution (mathematics); Environmental science; Environmental economics; Computer science; Engineering; Statistics; Business; Mathematics; Economics; Physics; Electrical engineering","score_opus":0.005692423253057095,"score_gpt":0.17154983663741102,"score_spread":0.16585741338435392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1846058565","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":[0.97145814,0.00018435577,0.011749352,0.00010696815,0.000012841349,0.00020156181,0.0024122377,0.00013178894,0.013742785],"genre_scores_gemma":[0.9931346,0.000121820114,0.005262868,0.0000053208078,0.0000014878019,0.000028939094,0.00074419193,0.000012165183,0.00068859925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989135,0.00016300399,0.00007428874,0.000091328446,0.00058877154,0.00016905773],"domain_scores_gemma":[0.9986016,0.0003891303,0.00020618185,0.00006178428,0.0006614824,0.0000797533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013160638,0.0005981618,0.00038030668,0.0053581526,0.0011605937,0.0020196894,0.00085716567,0.0003495634,0.00088701776],"category_scores_gemma":[0.0060027763,0.00023492203,0.0004753171,0.010817081,0.000546658,0.0011118737,0.0008109408,0.00049777067,0.00006409489],"study_design_candidate":"observational","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":[0.0003633693,0.00022922289,0.42130837,0.00018359783,0.00025907598,0.0002890776,0.0014022789,0.5010623,0.0037287802,0.009762436,0.0011755904,0.06023591],"study_design_scores_gemma":[0.000024755082,0.00011389206,0.37874982,0.000044895227,0.000065787426,0.00006229472,0.0028456957,0.60370755,0.006200956,0.002235703,0.0058457335,0.00010290605],"about_ca_topic_score_codex":0.8604326,"about_ca_topic_score_gemma":0.86756086,"teacher_disagreement_score":0.13956738,"about_ca_system_score_codex":0.017400643,"about_ca_system_score_gemma":0.0052701808,"threshold_uncertainty_score":0.2807786},"labels":[],"label_agreement":null},{"id":"W1885676741","doi":"10.1139/cjce-2012-0424","title":"Review of statistical water main break prediction models","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Econometrics; Computer science; Environmental science; Statistics; Mathematics","score_opus":0.0057847524440179,"score_gpt":0.15558549730987645,"score_spread":0.14980074486585854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885676741","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":[0.012410907,0.735853,0.22743066,0.0054283184,0.0017383975,0.00007862279,0.003412179,0.0009205314,0.01272736],"genre_scores_gemma":[0.13742882,0.80139995,0.049171004,0.00092773454,0.0026811834,0.00015117951,0.0045718923,0.00019990267,0.003468448],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988978,0.00028104152,0.00014110592,0.00018552605,0.00044852568,0.000045917986],"domain_scores_gemma":[0.9942597,0.004022487,0.00038024408,0.00018010069,0.0011067638,0.000050647544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002582197,0.0013048211,0.0014974865,0.0028632705,0.00024019773,0.001281928,0.0027025754,0.0007399314,0.0019416579],"category_scores_gemma":[0.008148719,0.0005621301,0.0015870731,0.004589524,0.00038609136,0.0019496926,0.0005762368,0.0010260415,0.0010737287],"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":[0.000092219976,0.000084634026,0.005255969,0.0075530205,0.000563137,0.00019306193,0.000086173255,0.28259,0.0006916953,0.027452225,0.040613085,0.6348247],"study_design_scores_gemma":[0.000025418592,0.00031979216,0.008451277,0.0050204583,0.00082989386,0.0005982446,0.00018315372,0.6183123,0.0025787493,0.0648151,0.2986063,0.0002593903],"about_ca_topic_score_codex":0.008332574,"about_ca_topic_score_gemma":0.004556594,"teacher_disagreement_score":0.008332574,"about_ca_system_score_codex":0.0008955781,"about_ca_system_score_gemma":0.0021476909,"threshold_uncertainty_score":0.016568184},"labels":[],"label_agreement":null},{"id":"W1887831862","doi":"10.1016/j.proeng.2015.08.975","title":"Flexibility Ranking of Water Distribution System Designs Under Future Mechanical and Hydraulic Uncertainty","year":2015,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"University of British Columbia","keywords":"Flexibility (engineering); Ranking (information retrieval); Rank (graph theory); Adaptation (eye); Set (abstract data type); Computer science; Insert (composites); Engineering; Reliability engineering; Industrial engineering; Operations research; Civil engineering; Mechanical engineering; Mathematics; Statistics; Artificial intelligence","score_opus":0.018184052411408413,"score_gpt":0.19460011975039615,"score_spread":0.17641606733898774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1887831862","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":[0.564337,0.0011722905,0.42026109,0.00046264348,0.00006105179,0.00036281574,0.00081719464,0.00031403327,0.012211719],"genre_scores_gemma":[0.9628115,0.0001695386,0.035928283,0.000019966368,0.000016806282,0.00008486066,0.0002104791,0.000011836475,0.00074670726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99661285,0.0014296887,0.00015672714,0.0002536113,0.0012105819,0.00033656278],"domain_scores_gemma":[0.9974801,0.001435236,0.00032295487,0.00012583104,0.00044155665,0.00019433878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034635125,0.0012308143,0.00086333306,0.004249271,0.0006255461,0.0017659991,0.00052346947,0.00076925196,0.001875474],"category_scores_gemma":[0.005661752,0.0003906353,0.0010496121,0.0019021138,0.0006664968,0.0011044923,0.0007953869,0.00060288457,0.00015289205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030721957,0.00007563982,0.0065666363,0.00020581084,0.00013765336,0.00013874279,0.00010730725,0.9048768,0.0034139464,0.0062079537,0.0008389229,0.077123255],"study_design_scores_gemma":[0.000027269369,0.0005636884,0.0071166023,0.000044144268,0.00006696609,0.000076136515,0.00021884666,0.98066586,0.0021500476,0.008022871,0.000997698,0.00004985789],"about_ca_topic_score_codex":0.0024923137,"about_ca_topic_score_gemma":0.0029108094,"teacher_disagreement_score":0.004249271,"about_ca_system_score_codex":0.0015222781,"about_ca_system_score_gemma":0.001004703,"threshold_uncertainty_score":0.018316984},"labels":[],"label_agreement":null},{"id":"W1889120062","doi":"10.3968/j.aped.1925543820130501.1027","title":"Mechanistic Leak-Detection Modeling for Single Gas-Phase Pipelines: Lessons Learned from Fit to Field-Scale Experimental Data","year":2013,"lang":"en","type":"article","venue":"Advances in petroleum exploration and development","topic":"Water Systems and 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":"Leak; Gas leak; Pipeline transport; Petroleum engineering; Environmental science; Inlet; Engineering; Marine engineering; Environmental engineering; Chemistry; Mechanical engineering","score_opus":0.10272728026703065,"score_gpt":0.31432859298850174,"score_spread":0.2116013127214711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1889120062","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":[0.5703078,0.0009215753,0.42016983,0.00069472595,0.000064457214,0.00036179813,0.00087040896,0.0011613984,0.0054478915],"genre_scores_gemma":[0.9630273,0.0005374085,0.034533467,0.0000963604,0.000014579079,0.00028307774,0.0004542253,0.00011166964,0.00094196515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952996,0.00013619388,0.00004421808,0.00013720538,0.00010500266,0.00004747533],"domain_scores_gemma":[0.99661714,0.002453241,0.00022290791,0.00022371762,0.00043025264,0.000052890267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026180358,0.0010998332,0.0013707654,0.0006326293,0.00040389487,0.0009793536,0.0017807098,0.002127447,0.0012088916],"category_scores_gemma":[0.0072920797,0.0005835223,0.0013305494,0.00058659597,0.00074331387,0.0022831461,0.0006634093,0.0018156334,0.00027551255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004747489,0.00017113853,0.0022483938,0.0001418776,0.000029377303,0.0000716591,0.00007933808,0.98780626,0.002470102,0.0016687512,0.0002233873,0.0050421413],"study_design_scores_gemma":[0.000004780841,0.000029923749,0.00031170528,0.0000046827677,0.0000043700843,0.000006849323,0.000015713373,0.9982474,0.0004587886,0.00078972033,0.00011706206,0.00000907149],"about_ca_topic_score_codex":0.009722299,"about_ca_topic_score_gemma":0.0049010995,"teacher_disagreement_score":0.009722299,"about_ca_system_score_codex":0.0010997882,"about_ca_system_score_gemma":0.0009511001,"threshold_uncertainty_score":0.019331396},"labels":[],"label_agreement":null},{"id":"W189640054","doi":"","title":"Economic Comparison of Periodic Acoustic Surveys and DMA- based Leakage Management Strategies","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"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":"Leakage (economics); Leak; Parametric statistics; Leak detection; Econometrics; Environmental science; Computer science; Engineering; Economics; Statistics; Mathematics; Environmental engineering","score_opus":0.010350145388038811,"score_gpt":0.21781007575356592,"score_spread":0.20745993036552712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W189640054","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":[0.94851524,0.0006677542,0.03614095,0.00040753887,0.00002002387,0.00016578705,0.00012508869,0.000040946317,0.01391667],"genre_scores_gemma":[0.996608,0.000073797986,0.002663793,0.00000705113,0.0000030798171,0.000041476862,0.000024664738,0.0000029439293,0.0005752726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984634,0.00085247133,0.000038623846,0.00007068344,0.000424645,0.00015017409],"domain_scores_gemma":[0.99475807,0.0040689423,0.00051031436,0.00020260841,0.0003436067,0.00011647235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027424581,0.00044310503,0.00046514656,0.0014042866,0.00020934886,0.00072921417,0.0007569131,0.00054314884,0.002055971],"category_scores_gemma":[0.0072030975,0.00025323362,0.00037198976,0.00075020926,0.0006011133,0.0010908663,0.0007215869,0.00033208687,0.00008049963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016533216,0.0003156321,0.0071053086,0.00023735758,0.00007403898,0.00015241327,0.0000630506,0.89186025,0.004583544,0.019072646,0.0004928303,0.07438956],"study_design_scores_gemma":[0.00016071365,0.0025789912,0.015671887,0.000037273243,0.0001151899,0.00010587005,0.00029270293,0.96583796,0.006677227,0.006925953,0.0015546212,0.000041546267],"about_ca_topic_score_codex":0.0015539386,"about_ca_topic_score_gemma":0.0024189234,"teacher_disagreement_score":0.0027424581,"about_ca_system_score_codex":0.0019866922,"about_ca_system_score_gemma":0.00080638134,"threshold_uncertainty_score":0.014503658},"labels":[],"label_agreement":null},{"id":"W1900730857","doi":"10.14796/jwmm.c392","title":"Comparing Unsteady Modeling Approaches of Surges Caused by Sudden Air Pocket Compression","year":2015,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Auburn University","keywords":"Surge; Compression (physics); Mechanics; Pipeline (software); Unsteady flow; Flow (mathematics); Electrical conduit; Geology; Engineering; Physics; Mechanical engineering; Thermodynamics; Geomorphology","score_opus":0.08637126074145557,"score_gpt":0.21500262730070047,"score_spread":0.1286313665592449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1900730857","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":[0.7405864,0.0017361091,0.20981865,0.001136217,0.0004399546,0.00024354598,0.001143236,0.0012099877,0.04368594],"genre_scores_gemma":[0.9818559,0.00067503226,0.012968459,0.00007557144,0.000058009595,0.000108787804,0.00041016916,0.00017016003,0.003677818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998117,0.000057804144,0.000014505659,0.000024602869,0.000054549655,0.000036811045],"domain_scores_gemma":[0.99899465,0.0005210406,0.000072520736,0.00008355759,0.00026232115,0.000065846514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046117438,0.0006918217,0.0006447035,0.00078702334,0.000521144,0.0009823845,0.0009587826,0.0011402131,0.0024406733],"category_scores_gemma":[0.0018254764,0.0003150645,0.0010555374,0.0005035326,0.00031489454,0.0008338344,0.0005693736,0.0010090878,0.00042521404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085709886,0.00009350685,0.00126906,0.00006978112,0.000031607375,0.000041085204,0.0000708317,0.9872866,0.0013849612,0.0020335237,0.00048601502,0.0071473042],"study_design_scores_gemma":[0.000004528248,0.000023200144,0.00026126887,0.0000044398766,0.00000523291,0.0000044611147,0.000021308642,0.9988034,0.00038169805,0.00025614756,0.00022772807,0.0000065126355],"about_ca_topic_score_codex":0.021594016,"about_ca_topic_score_gemma":0.010965564,"teacher_disagreement_score":0.021594016,"about_ca_system_score_codex":0.0006660542,"about_ca_system_score_gemma":0.0010323997,"threshold_uncertainty_score":0.042936683},"labels":[],"label_agreement":null},{"id":"W1948860882","doi":"10.1139/cjce-2014-0077","title":"Comparative analysis of sewer physical condition grading protocols for the City of Edmonton","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"Drainage Services Department","keywords":"Sanitary sewer; Grading (engineering); Asset management; Protocol (science); Mains electricity; Certification; Computer science; Engineering; Transport engineering; Civil engineering; Environmental engineering; Business","score_opus":0.0167524972800329,"score_gpt":0.23759388307572504,"score_spread":0.22084138579569215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1948860882","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":[0.98354363,0.00022372785,0.0045479173,0.00015324561,0.000029031069,0.0008886863,0.0007213075,0.00014118226,0.009751197],"genre_scores_gemma":[0.97505164,0.00044455598,0.014680328,0.000069173686,0.000008522261,0.0007302356,0.0019588475,0.000052486932,0.0070042503],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9945785,0.0015451949,0.0003831747,0.0004894223,0.0026493736,0.00035433084],"domain_scores_gemma":[0.97970176,0.006590277,0.0016548698,0.0013288276,0.010023049,0.0007013052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009720959,0.00044498232,0.00048026405,0.0036277235,0.0009994416,0.0012712186,0.0012290943,0.00063945155,0.001955964],"category_scores_gemma":[0.023476232,0.00036547554,0.0005240429,0.0032198676,0.0009919518,0.0011148566,0.0012667587,0.00044209012,0.00029310305],"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":[0.007813207,0.002753909,0.47115856,0.0016502194,0.00071946107,0.0010576134,0.008799852,0.06497694,0.04069096,0.0064633195,0.0076976405,0.38621834],"study_design_scores_gemma":[0.00019444904,0.0031964132,0.9403428,0.00015163027,0.00029826112,0.00012087096,0.007662701,0.02033147,0.015684346,0.0006204373,0.011270157,0.00012658967],"about_ca_topic_score_codex":0.2228228,"about_ca_topic_score_gemma":0.4610271,"teacher_disagreement_score":0.2228228,"about_ca_system_score_codex":0.009519769,"about_ca_system_score_gemma":0.0073020034,"threshold_uncertainty_score":0.44305164},"labels":[],"label_agreement":null},{"id":"W1952796943","doi":"10.1139/l09-128","title":"Modelling wastewater effluent mixing and dispersion in a tidal channel","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Effluent; Dilution; Serial dilution; Stratification (seeds); Wastewater; Environmental science; Inlet; Dispersion (optics); Sewage; Hydrology (agriculture); Environmental engineering; Geology; Geotechnical engineering; Oceanography; Physics","score_opus":0.004986796087734657,"score_gpt":0.1462380781064545,"score_spread":0.14125128201871984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1952796943","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":[0.99505144,0.000034704626,0.0031779131,0.000049570528,0.0000036467677,0.000017737175,0.00013791039,0.000040990784,0.0014861171],"genre_scores_gemma":[0.99776626,0.000038553688,0.00090819824,0.0000062985446,0.0000015731786,0.000013635029,0.00006468706,0.000006229428,0.0011946657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986565,0.000020989723,0.000008022832,0.000037276535,0.00001762215,0.000050441122],"domain_scores_gemma":[0.99967325,0.00016503701,0.00005247689,0.000015522115,0.000047776197,0.000045924677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017236781,0.00048826257,0.00039317712,0.00041095805,0.00070001563,0.0012350837,0.0007702345,0.0013717897,0.0006997662],"category_scores_gemma":[0.0008207889,0.00047106954,0.00044510447,0.00061433954,0.0006671781,0.0004593175,0.0006162963,0.00048462194,0.00009028452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005705478,0.000054683962,0.010316932,0.000012982623,0.000013104552,0.000096556854,0.0000474142,0.9851586,0.0021930402,0.00039932661,0.00007164611,0.001578677],"study_design_scores_gemma":[0.000017169146,0.000026211348,0.0027232512,0.0000017374552,0.0000071866984,0.000009522254,0.000038048463,0.996573,0.00038628612,0.0001319391,0.00007838259,0.000007454122],"about_ca_topic_score_codex":0.3098253,"about_ca_topic_score_gemma":0.18005839,"teacher_disagreement_score":0.3098253,"about_ca_system_score_codex":0.0033675164,"about_ca_system_score_gemma":0.002259788,"threshold_uncertainty_score":0.6160438},"labels":[],"label_agreement":null},{"id":"W1963516529","doi":"10.1090/s0033-569x-2014-01354-6","title":"Analysis of water hammer attenuation in the Brunone model of unsteady friction","year":2014,"lang":"en","type":"article","venue":"Quarterly of Applied Mathematics","topic":"Water Systems and 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":"Dalhousie University","funders":"","keywords":"Water hammer; Attenuation; Mechanics; Closure (psychology); Parametric statistics; Pressure head; Geotechnical engineering; Geology; Physics; Materials science; Mathematics; Optics; Thermodynamics","score_opus":0.01098130149965885,"score_gpt":0.19046944877952907,"score_spread":0.17948814727987022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963516529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26917705,0.00074954383,0.7168249,0.0002613316,0.000051519197,0.00004398248,0.000049624687,0.00019195523,0.012650032],"genre_scores_gemma":[0.99289227,0.00023681145,0.003720492,0.000013418111,0.000011202478,0.000017922688,0.000019107081,0.00002524221,0.003063389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997279,0.000069092835,0.000008894641,0.000029628256,0.000115255425,0.000049211205],"domain_scores_gemma":[0.9994311,0.00022844714,0.00011479991,0.000042393778,0.00013782154,0.000045365872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052223005,0.00062812964,0.00069069973,0.0006474843,0.00023894553,0.00066560874,0.00080667954,0.0006457695,0.0009267092],"category_scores_gemma":[0.0023136486,0.00023890885,0.0005600511,0.00032752528,0.0011091564,0.0009716849,0.000819547,0.00073666783,0.00014828602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010123985,0.000039426235,0.0014703826,0.00013230673,0.000030026584,0.00048001175,0.00018645593,0.91613096,0.013967042,0.057521112,0.00054086186,0.009400173],"study_design_scores_gemma":[0.0000014157571,0.000009808595,0.00013291984,0.0000025971508,0.0000018645661,0.00001897153,0.00000935567,0.9977556,0.00027178094,0.001698997,0.00009280832,0.000003907704],"about_ca_topic_score_codex":0.0051047346,"about_ca_topic_score_gemma":0.0023054325,"teacher_disagreement_score":0.0051047346,"about_ca_system_score_codex":0.0008847826,"about_ca_system_score_gemma":0.0006044991,"threshold_uncertainty_score":0.010150015},"labels":[],"label_agreement":null},{"id":"W1963627585","doi":"10.1115/ipc2002-27043","title":"Solutions to Two Problems of Pipe Flow","year":2002,"lang":"en","type":"article","venue":"4th International Pipeline Conference, Parts A and B","topic":"Water Systems and 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":"Golder Associates (Canada)","funders":"","keywords":"Pipe flow; Flow (mathematics); Pipe network analysis; Nominal Pipe Size; Plug flow; Mechanics; Open-channel flow; Function (biology); Hele-Shaw flow; Choked flow; Materials science; Physics","score_opus":0.03672881878250206,"score_gpt":0.22668382320026495,"score_spread":0.1899550044177629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963627585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018241556,0.0018767574,0.9246495,0.006072766,0.00069156906,0.00015587833,0.00023665084,0.0001860962,0.04788919],"genre_scores_gemma":[0.33201152,0.0042240457,0.5810086,0.001489153,0.0013474697,0.00095397315,0.00059286295,0.00027104016,0.0781013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985096,0.00045273788,0.00007526212,0.0003685327,0.00043392344,0.00016006596],"domain_scores_gemma":[0.998481,0.00086258457,0.00017876807,0.00016866154,0.00022370528,0.00008531179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002057218,0.0009920516,0.0010052698,0.0010172583,0.0012135573,0.002972444,0.0016704504,0.005234033,0.014973548],"category_scores_gemma":[0.010390407,0.0007295638,0.001456991,0.0008476712,0.002639715,0.0033972224,0.0037617148,0.0025662503,0.0014497277],"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":[0.00008368721,0.00006166655,0.0003023454,0.00035441565,0.000028141338,0.00018007442,0.0004077197,0.079911925,0.0012554525,0.86624587,0.006546047,0.044622608],"study_design_scores_gemma":[0.00020045809,0.00016161683,0.000548935,0.00022974754,0.0000349272,0.0005247529,0.00037413358,0.27815336,0.002146699,0.639716,0.0778232,0.00008620066],"about_ca_topic_score_codex":0.0010360245,"about_ca_topic_score_gemma":0.00066483655,"teacher_disagreement_score":0.014973548,"about_ca_system_score_codex":0.0013913335,"about_ca_system_score_gemma":0.0016218664,"threshold_uncertainty_score":0.050091505},"labels":[],"label_agreement":null},{"id":"W1963660070","doi":"10.1002/cjce.20210","title":"Evaluating resistance coefficients of straight‐through diaphragm control valves","year":2009,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reynolds number; Laminar flow; Mechanics; Diaphragm (acoustics); Newtonian fluid; Mathematics; Pressure drop; Control valves; Materials science; Physics; Thermodynamics; Turbulence; Acoustics","score_opus":0.01145298685327814,"score_gpt":0.2151077919961559,"score_spread":0.20365480514287776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963660070","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":[0.9648739,0.0003100789,0.033908635,0.000011503558,0.00001111993,0.000025747724,0.000093500574,0.0001731768,0.00059234933],"genre_scores_gemma":[0.994892,0.00008010878,0.004698642,0.0000028374416,0.0000011956213,0.000009561427,0.000070479735,0.000010427862,0.00023468098],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916196,0.00010183186,0.00005410246,0.00013068091,0.00048231435,0.00006916188],"domain_scores_gemma":[0.9977725,0.0011195207,0.00044151343,0.00016699122,0.00044627715,0.000053106534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000969715,0.00038553012,0.0006067693,0.0009270462,0.0001516265,0.00058747915,0.0006583632,0.0003651969,0.0007120689],"category_scores_gemma":[0.0045805927,0.00027965478,0.0002927375,0.00053992716,0.00027431262,0.0007775797,0.00036512833,0.00031269807,0.00019886848],"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":[0.00086970103,0.0003473863,0.03188898,0.00050833315,0.00011991285,0.00029871525,0.00031625747,0.12891251,0.71975213,0.0007862829,0.00035202963,0.11584776],"study_design_scores_gemma":[0.00003018857,0.0010919488,0.04187566,0.00003200549,0.000046630685,0.00015403125,0.00010710436,0.4955846,0.4597941,0.00036674115,0.0008494132,0.00006756224],"about_ca_topic_score_codex":0.0011759397,"about_ca_topic_score_gemma":0.0008101406,"teacher_disagreement_score":0.0011759397,"about_ca_system_score_codex":0.00041225896,"about_ca_system_score_gemma":0.00031522708,"threshold_uncertainty_score":0.0051283836},"labels":[],"label_agreement":null},{"id":"W1964199026","doi":"10.1139/l03-071","title":"Comparison of computed water hammer pressures with test results for the Çatalan power plant in Turkey","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":19,"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":"Penstock; Water hammer; Transient (computer programming); Closure (psychology); Turbine; Power station; Engineering; Flow (mathematics); Momentum (technical analysis); Load rejection; Mechanics; Test data; Marine engineering; Structural engineering; Mechanical engineering; Computer science; Physics; Electrical engineering","score_opus":0.008821360000709067,"score_gpt":0.18133995536071273,"score_spread":0.17251859536000366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964199026","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":[0.9963438,0.00002841795,0.001585664,0.000021811875,0.0000058418022,0.000015735232,0.00036937458,0.00016575294,0.0014636618],"genre_scores_gemma":[0.9988972,0.000016294684,0.00054434413,0.0000024318992,6.5227414e-7,0.000009987573,0.00029001068,0.000013210759,0.00022594263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997626,0.000023458417,0.000018868252,0.000046033187,0.00008732965,0.00006172141],"domain_scores_gemma":[0.9993223,0.00033966257,0.00005177734,0.00006597762,0.0001764244,0.000043801192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032847057,0.00037112492,0.00039393446,0.0005877998,0.00051897584,0.0006187143,0.0009974474,0.0006964739,0.0018368074],"category_scores_gemma":[0.0010277055,0.00023915408,0.0003154372,0.00079061673,0.00060718955,0.00058360543,0.0003711637,0.0005662582,0.00030135672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014047587,0.0005472228,0.045567274,0.0004617558,0.000047737376,0.0012891521,0.00058641535,0.8513649,0.05044867,0.0012478302,0.0025824027,0.044451922],"study_design_scores_gemma":[0.0002770307,0.0011186452,0.1088091,0.00003147651,0.000039999104,0.0001985175,0.0009889135,0.7835166,0.102791294,0.0003879825,0.0017483723,0.00009204984],"about_ca_topic_score_codex":0.028390171,"about_ca_topic_score_gemma":0.031962506,"teacher_disagreement_score":0.028390171,"about_ca_system_score_codex":0.0014659342,"about_ca_system_score_gemma":0.0009919098,"threshold_uncertainty_score":0.05644983},"labels":[],"label_agreement":null},{"id":"W1964441459","doi":"10.1061/9780784412312.099","title":"Conceptual Water Sharing Plan for Tarrant Regional Water District (TRWD) and the City of Dallas, TX","year":2012,"lang":"en","type":"article","venue":"World Environmental And Water Resources Congress 2012","topic":"Water Systems and 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":"Cambridge Memorial Hospital","funders":"","keywords":"Pipeline transport; Water supply; Variety (cybernetics); Pipeline (software); Plan (archaeology); Capital (architecture); Reliability (semiconductor); Cost sharing; Business; Environmental economics; Environmental planning; Computer science; Engineering; Environmental science; Environmental engineering; Economics; Geography","score_opus":0.013651558979712868,"score_gpt":0.17070747369567993,"score_spread":0.15705591471596705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964441459","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":[0.26601282,0.00086303457,0.22091007,0.012379837,0.00033279802,0.004159573,0.012108419,0.0011326637,0.48210087],"genre_scores_gemma":[0.8044475,0.0004860415,0.11451363,0.0005675393,0.000030087218,0.0017087717,0.0043395357,0.0000787468,0.07382813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999183,0.00021509199,0.000039278184,0.00012296489,0.00026050318,0.0001791273],"domain_scores_gemma":[0.99954695,0.00007499894,0.000053066597,0.00003333623,0.00020494683,0.000086698965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012205341,0.00037461935,0.0002242476,0.00096039136,0.0015322063,0.003353627,0.0013029967,0.00091605657,0.012584865],"category_scores_gemma":[0.001143021,0.00041529498,0.00041249837,0.0014346467,0.00083955465,0.0017463812,0.0009916254,0.0010578098,0.00069823436],"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":[0.0001522444,0.00022921135,0.006710078,0.00014893567,0.000030502912,0.000546297,0.0007752071,0.3147225,0.0022965486,0.5838517,0.04244446,0.04809232],"study_design_scores_gemma":[0.00046300972,0.0005437242,0.01784763,0.00026518595,0.00008161539,0.00032509642,0.007408442,0.4355276,0.00421967,0.114997506,0.4181294,0.0001911331],"about_ca_topic_score_codex":0.12842087,"about_ca_topic_score_gemma":0.22348696,"teacher_disagreement_score":0.12842087,"about_ca_system_score_codex":0.008190396,"about_ca_system_score_gemma":0.016537659,"threshold_uncertainty_score":0.25534678},"labels":[],"label_agreement":null},{"id":"W1966174411","doi":"10.1016/j.scitotenv.2014.10.040","title":"A risk-based approach to sanitary sewer pipe asset management","year":2014,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Water Systems and Optimization","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Asset management; Risk analysis (engineering); Stormwater; Asset (computer security); Risk assessment; Risk management; Environmental planning; Business; Environmental science; Computer science; Finance; Surface runoff","score_opus":0.004724393950570629,"score_gpt":0.15325063564369218,"score_spread":0.14852624169312156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966174411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008973489,0.00050769054,0.98318535,0.0008615488,0.000084220206,0.000050466988,0.000070210546,0.000062568644,0.0062044146],"genre_scores_gemma":[0.74008507,0.0014695639,0.23597309,0.0003287405,0.000516545,0.0002755987,0.00020263044,0.0001250529,0.02102374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982279,0.0007605549,0.00007647351,0.00024105482,0.0005593975,0.00013462298],"domain_scores_gemma":[0.9982461,0.0010611675,0.00016425911,0.00007444059,0.00035514365,0.00009900241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025177163,0.0012864922,0.0015752151,0.0016960019,0.00062068814,0.0027244021,0.0028802112,0.0021231954,0.004007601],"category_scores_gemma":[0.0054204897,0.000917241,0.0014631755,0.0011674812,0.0012922746,0.00260638,0.002296017,0.0020204326,0.00032324504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016755297,0.00006138134,0.0003494141,0.000045413937,0.00006656263,0.000072176605,0.00004326161,0.9355986,0.00034440003,0.047610495,0.0007290845,0.015062335],"study_design_scores_gemma":[0.0000052742666,0.000019140487,0.00010438881,0.000009472146,0.000014633927,0.000013772537,0.0000126928535,0.9677471,0.00009616353,0.03143574,0.00053399,0.000007585201],"about_ca_topic_score_codex":0.005463753,"about_ca_topic_score_gemma":0.005241397,"teacher_disagreement_score":0.005463753,"about_ca_system_score_codex":0.0018815768,"about_ca_system_score_gemma":0.0017687954,"threshold_uncertainty_score":0.013651788},"labels":[],"label_agreement":null},{"id":"W1966459151","doi":"10.1115/power2013-98234","title":"Effects of Cavitation Damage on the Hydraulic Characteristics of a Gate Valve","year":2013,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Ontario Tech University","funders":"","keywords":"Cavitation; Gate valve; Pressure drop; Materials science; Mechanics; Hydraulic machinery; Computational fluid dynamics; Turbulence; Discharge coefficient; Flow (mathematics); Valve seat; Hydraulic fluid; Fluid dynamics; Mechanical engineering; Engineering; Physics","score_opus":0.0037931342690806598,"score_gpt":0.16003093809697372,"score_spread":0.15623780382789307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966459151","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":[0.99659616,0.00015854002,0.0027643465,0.00002439512,0.000011641762,0.0000051113593,0.000037923463,0.00006643054,0.00033552136],"genre_scores_gemma":[0.9995586,0.00003055169,0.00024281292,0.0000039674596,0.0000011529636,0.0000011202885,0.000019239806,0.000006868256,0.00013565541],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996357,0.000035978606,0.000027890545,0.000054798653,0.0001608209,0.00008472573],"domain_scores_gemma":[0.99844605,0.0008230669,0.00025618562,0.00013664042,0.00023678252,0.00010139911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042527707,0.00026857166,0.000491462,0.00035837328,0.00027776413,0.0004603686,0.00036957077,0.00051271095,0.00064608565],"category_scores_gemma":[0.0023134924,0.00019530898,0.00027375142,0.00020811273,0.0005565945,0.000569349,0.0003188146,0.0003260526,0.00011884689],"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":[0.0009598689,0.0001217805,0.01458198,0.00021394533,0.000046052202,0.0009973889,0.0004043246,0.0946205,0.8638101,0.00023838844,0.00026952685,0.02373616],"study_design_scores_gemma":[0.000024231418,0.0019235048,0.057794318,0.000021589678,0.00006364007,0.00067775784,0.00034557242,0.228298,0.70894295,0.00023204628,0.0016003546,0.000076012635],"about_ca_topic_score_codex":0.0014212108,"about_ca_topic_score_gemma":0.0013043389,"teacher_disagreement_score":0.0014212108,"about_ca_system_score_codex":0.00041611094,"about_ca_system_score_gemma":0.00020855315,"threshold_uncertainty_score":0.003019154},"labels":[],"label_agreement":null},{"id":"W1966930152","doi":"10.1061/(asce)ir.1943-4774.0000327","title":"Hydraulics of Stacked Drop Manholes","year":2011,"lang":"en","type":"article","venue":"Journal of Irrigation and Drainage Engineering","topic":"Water Systems and 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":"U.S. Army Corps of Engineers","keywords":"Inflow; Outflow; Hydraulics; Mechanics; Drop (telecommunication); Flow (mathematics); Momentum (technical analysis); Geology; Geotechnical engineering; Plane (geometry); Environmental science; Geometry; Engineering; Physics; Mechanical engineering; Mathematics; Thermodynamics","score_opus":0.009873977379515725,"score_gpt":0.15518019939679015,"score_spread":0.14530622201727442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966930152","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":[0.99364555,0.00003218485,0.0054462906,0.000025102046,0.000020579788,0.000036480338,0.00011124819,0.00009749573,0.0005850251],"genre_scores_gemma":[0.9976146,0.000016091331,0.0019356624,0.0000061763344,0.0000035098114,0.000013395971,0.0000590244,0.0000054986967,0.00034590732],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999426,0.000044866978,0.000025373813,0.000113600145,0.00019229836,0.00019787592],"domain_scores_gemma":[0.999445,0.000130688,0.000086607986,0.000097775745,0.000107689106,0.00013214406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029680962,0.00035291788,0.00042883158,0.00048383774,0.0004996698,0.0005890987,0.0005354877,0.0004239197,0.0013018332],"category_scores_gemma":[0.0010174575,0.0002425809,0.0003144116,0.00027137366,0.0009204742,0.0005067033,0.0010320393,0.0004096188,0.00016057062],"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":[0.0010419078,0.00034015396,0.030811772,0.00018574893,0.00004861445,0.0012965145,0.0006552851,0.06162407,0.8626365,0.0030583916,0.00063792494,0.037663087],"study_design_scores_gemma":[0.00022200964,0.00507301,0.093028784,0.000043193384,0.00009147546,0.0006343524,0.0015350371,0.25306675,0.6362407,0.0023066865,0.0075890957,0.00016890666],"about_ca_topic_score_codex":0.0015537877,"about_ca_topic_score_gemma":0.0016824892,"teacher_disagreement_score":0.0015537877,"about_ca_system_score_codex":0.0004281448,"about_ca_system_score_gemma":0.00068307976,"threshold_uncertainty_score":0.0043550134},"labels":[],"label_agreement":null},{"id":"W1967011044","doi":"10.5942/jawwa.2013.105.0157","title":"Life‐cycle energy analysis of performance‐ versus age‐based pipe replacement schedules","year":2013,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Payback period; Embodied energy; Leakage (economics); Environmental science; Energy consumption; Energy (signal processing); Operations management; Engineering; Reliability engineering; Production (economics); Economics; Electrical engineering; Mathematics","score_opus":0.005169624163420805,"score_gpt":0.1839310086911638,"score_spread":0.17876138452774298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967011044","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":[0.9729709,0.0004306223,0.014062637,0.00008937699,0.000013958092,0.000155608,0.0021109341,0.00009247109,0.010073461],"genre_scores_gemma":[0.99422246,0.00011550131,0.002214074,0.000017651453,0.0000022097597,0.000064918575,0.0013024847,0.000028195404,0.0020325459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995627,0.00013660066,0.000015938169,0.00004787636,0.00014215782,0.000094672054],"domain_scores_gemma":[0.9983417,0.000949207,0.00015841957,0.000074311174,0.0004363297,0.000040148054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015433545,0.00055589864,0.00045547518,0.0013671629,0.00024857992,0.0004181865,0.0005686231,0.00041604112,0.0022882714],"category_scores_gemma":[0.0023912145,0.00034984326,0.000933658,0.00095889095,0.00021267452,0.00058220676,0.00020609799,0.000297466,0.00028296004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004456784,0.00008374784,0.00991862,0.00008258346,0.00006786933,0.000066128225,0.000040146802,0.9679003,0.004572972,0.0012987823,0.00039802762,0.015125222],"study_design_scores_gemma":[0.000049244503,0.0012110872,0.040051922,0.000027277763,0.00013811437,0.000068714755,0.00013517795,0.9433146,0.010494715,0.0015225696,0.002936389,0.000050149814],"about_ca_topic_score_codex":0.012318543,"about_ca_topic_score_gemma":0.0128360335,"teacher_disagreement_score":0.012318543,"about_ca_system_score_codex":0.0021418403,"about_ca_system_score_gemma":0.0008196583,"threshold_uncertainty_score":0.024493694},"labels":[],"label_agreement":null},{"id":"W1967074450","doi":"10.1097/01.ede.0000362712.95144.94","title":"By-Products Associated with Structural Rehabilitation for Water Distribution Systems","year":2009,"lang":"en","type":"article","venue":"Epidemiology","topic":"Water Systems and 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 Santé Publique du Québec","funders":"","keywords":"Tap water; Triethylenetetramine; Chemistry; Detection limit; Environmental chemistry; Chromatography; Environmental science; Environmental engineering","score_opus":0.012766207277535085,"score_gpt":0.22847587551100834,"score_spread":0.21570966823347326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967074450","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":[0.98468024,0.0038251588,0.004605913,0.0002397502,0.000029065586,0.0002408286,0.0010631604,0.00005530148,0.005260554],"genre_scores_gemma":[0.9850328,0.003524748,0.005348455,0.00012793585,0.00003406143,0.00010770136,0.00095827755,0.000017057071,0.0048489925],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986565,0.0003569303,0.00016390641,0.00017748456,0.0005120552,0.00013303441],"domain_scores_gemma":[0.9979982,0.00023499198,0.0011550407,0.000064373555,0.00046039568,0.00008693278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095549016,0.00071745756,0.00021274449,0.0013865867,0.000435369,0.0006686944,0.0004051437,0.000646715,0.0032957585],"category_scores_gemma":[0.0009947873,0.00017731635,0.00036258472,0.0012753056,0.0004415037,0.00034427128,0.0005019392,0.00034499724,0.000706347],"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":[0.0004526702,0.00048282728,0.844872,0.0012006062,0.0001313161,0.0010235265,0.00066711736,0.00037943767,0.09569319,0.00032373783,0.0011830318,0.05359051],"study_design_scores_gemma":[0.000017550365,0.0017212699,0.90354514,0.00028100566,0.00021104659,0.003960448,0.0011809159,0.0009524574,0.07426003,0.00021877898,0.013630715,0.000020585367],"about_ca_topic_score_codex":0.005041784,"about_ca_topic_score_gemma":0.005895094,"teacher_disagreement_score":0.005041784,"about_ca_system_score_codex":0.0006882417,"about_ca_system_score_gemma":0.0005144888,"threshold_uncertainty_score":0.011025369},"labels":[],"label_agreement":null},{"id":"W1967514035","doi":"10.1002/cjce.20112","title":"Flow regime determination in horizontal hydrotransport using non‐intrusive acoustic probes","year":2008,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Flow (mathematics); Slurry; Acoustics; Materials science; Mechanics; Pipe flow; Geology; Physics; Composite material; Turbulence","score_opus":0.007716560470767804,"score_gpt":0.16531167355139037,"score_spread":0.15759511308062257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967514035","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":[0.9748489,0.00011227879,0.024256663,0.000023404224,0.000008120049,0.000030355735,0.00007741044,0.00007250377,0.00057020225],"genre_scores_gemma":[0.97851515,0.0001134635,0.020508539,0.000012647518,0.000007184791,0.00002994768,0.00006490215,0.000009631896,0.0007384778],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997943,0.00003088457,0.0000069320286,0.00004583253,0.00010942987,0.000012615093],"domain_scores_gemma":[0.9995554,0.0001572545,0.00014639068,0.00002723583,0.000092498856,0.000021172746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022955792,0.0001620302,0.00015349037,0.00019136212,0.000118787066,0.0002278766,0.00021529276,0.00016970208,0.00046240518],"category_scores_gemma":[0.00047101427,0.00017524758,0.000058423604,0.00015611017,0.0003577452,0.000267871,0.0002704415,0.00023395751,0.00014185473],"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":[0.00004270631,0.000011525883,0.0036475004,0.000026591457,0.0000026747657,0.000025175346,0.000054423992,0.00027910227,0.98926544,0.00005882381,0.00003525447,0.0065507214],"study_design_scores_gemma":[0.00001673936,0.0004304537,0.040393125,0.00000779697,0.000014604116,0.00011905292,0.00013426303,0.01772354,0.9396198,0.00011976111,0.0014046283,0.000016325368],"about_ca_topic_score_codex":0.00081544364,"about_ca_topic_score_gemma":0.0011412133,"teacher_disagreement_score":0.00081544364,"about_ca_system_score_codex":0.0001516946,"about_ca_system_score_gemma":0.00016467807,"threshold_uncertainty_score":0.0016214252},"labels":[],"label_agreement":null},{"id":"W1968237967","doi":"10.1080/15732479.2014.887122","title":"Benefits of using basic, imprecise or uncertain data for elaborating sewer inspection programmes","year":2014,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Water Systems and 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":"GDG Environnement","funders":"Agence Nationale de la Recherche","keywords":"Pascal (unit); Computer science; Order (exchange); Asset (computer security); Risk analysis (engineering); Key (lock); Operations research; Data mining; Reliability engineering; Engineering; Business; Computer security","score_opus":0.011628306761513017,"score_gpt":0.2203498512557694,"score_spread":0.20872154449425637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968237967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13240935,0.0009085509,0.85547733,0.0006866204,0.000079944824,0.00010146049,0.0017366215,0.0012375823,0.0073625557],"genre_scores_gemma":[0.78560233,0.000645008,0.21098298,0.00009591514,0.00007352918,0.00005236084,0.0011426166,0.00015187755,0.0012534835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978904,0.0009417928,0.00019907705,0.00031559137,0.0005590596,0.000094029536],"domain_scores_gemma":[0.983431,0.011546373,0.0009992144,0.0019970846,0.0018270393,0.00019927954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004118039,0.0009727848,0.0011403305,0.0011988145,0.00037041097,0.0023270196,0.0011339303,0.0013429264,0.0040023704],"category_scores_gemma":[0.02067642,0.00043150396,0.0009858359,0.0011611178,0.00050340564,0.003260061,0.0012922604,0.0009177375,0.0006872686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012826201,0.00017833477,0.0205175,0.0007321155,0.00022746803,0.00024255866,0.00033776573,0.7146558,0.014324029,0.005429746,0.0012224942,0.24084958],"study_design_scores_gemma":[0.00005336687,0.00040703837,0.012842432,0.00019659472,0.00021534822,0.00016109007,0.00042189984,0.9452161,0.015677238,0.019449724,0.00527394,0.00008510988],"about_ca_topic_score_codex":0.0042403447,"about_ca_topic_score_gemma":0.0043884492,"teacher_disagreement_score":0.0042403447,"about_ca_system_score_codex":0.00050623843,"about_ca_system_score_gemma":0.00094974635,"threshold_uncertainty_score":0.021778524},"labels":[],"label_agreement":null},{"id":"W1968576513","doi":"","title":"An Experimental Determination of the Variation With Temperature of the Critical Velocity of Flow of Water in Pipes","year":2010,"lang":"en","type":"book","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Mechanics; Flow (mathematics); Critical ionization velocity; Variation (astronomy); Statistical physics; Mathematics; Thermodynamics; Physics","score_opus":0.0026619175602393117,"score_gpt":0.15994818575524702,"score_spread":0.1572862681950077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968576513","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":[0.5945213,0.014516977,0.16903758,0.002412165,0.002335046,0.0002752057,0.006823761,0.0031520568,0.20692585],"genre_scores_gemma":[0.9498062,0.004967945,0.018769873,0.0003569262,0.00029736306,0.00017596465,0.0013330866,0.00034510315,0.02394745],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99938595,0.00005101579,0.000020846304,0.00017073823,0.00034206235,0.00002941068],"domain_scores_gemma":[0.997843,0.0013014026,0.00023704291,0.00033445185,0.00025379533,0.000030182931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064466515,0.0004494894,0.00030503643,0.0010501127,0.00036289645,0.00045970804,0.0012266611,0.000841926,0.0047053723],"category_scores_gemma":[0.0039135814,0.0004766842,0.00021746065,0.0010921203,0.0018080935,0.0009054961,0.00044417605,0.0013298392,0.00091907714],"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":[0.00052439084,0.00027226875,0.0060897614,0.0012703844,0.00007052493,0.00034336364,0.0011999389,0.009456876,0.8336127,0.028801398,0.011131647,0.1072267],"study_design_scores_gemma":[0.00005022837,0.0010426232,0.04135957,0.00018226702,0.00006616968,0.00077857537,0.00034304542,0.020743387,0.8962789,0.008180679,0.030830294,0.00014429334],"about_ca_topic_score_codex":0.00090029655,"about_ca_topic_score_gemma":0.0008820339,"teacher_disagreement_score":0.0047053723,"about_ca_system_score_codex":0.00045400878,"about_ca_system_score_gemma":0.00026597962,"threshold_uncertainty_score":0.01574105},"labels":[],"label_agreement":null},{"id":"W1970401602","doi":"10.1016/j.watres.2006.10.027","title":"Using Bayesian statistics to estimate the coefficients of a two- component second-order chlorine bulk decay model for a water distribution system","year":2006,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":42,"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 Guelph","funders":"Canada Research Chairs; U.S. Environmental Protection Agency","keywords":"Chlorine; Trihalomethane; Markov chain Monte Carlo; Monte Carlo method; Gibbs sampling; Bayesian probability; Chemistry; Statistics; Applied mathematics; Mathematics","score_opus":0.04631166633574756,"score_gpt":0.335903964607223,"score_spread":0.2895922982714754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970401602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1373462,0.00013805972,0.8608807,0.0003442867,0.000017784801,0.000038866696,0.00014917995,0.00031364418,0.0007712436],"genre_scores_gemma":[0.9401576,0.0002119599,0.056289367,0.00010237109,0.000029962299,0.00013237372,0.000474976,0.00012329647,0.0024781001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920505,0.0003824197,0.000033770426,0.00012706402,0.00015057012,0.00010107981],"domain_scores_gemma":[0.98859686,0.009510901,0.0007446218,0.00019673514,0.0008000306,0.0001509058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042649605,0.0012088543,0.0016778805,0.0011470392,0.00058495364,0.0014924073,0.0013641112,0.0017887597,0.00096559763],"category_scores_gemma":[0.014121279,0.0014470106,0.0010405877,0.00077554066,0.0012773016,0.0017743702,0.0008487849,0.0021807337,0.00020654316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004269447,0.000019103201,0.00053214177,0.000013769482,0.000022120954,0.000008789463,0.000010586062,0.9945261,0.00035935093,0.0022787245,0.00012272374,0.0020637827],"study_design_scores_gemma":[0.000005759359,0.0000043850714,0.000109427594,9.537456e-7,0.0000038743756,0.0000013745389,0.0000015540594,0.9988834,0.0000941812,0.0008696348,0.000020570506,0.000004736688],"about_ca_topic_score_codex":0.035480905,"about_ca_topic_score_gemma":0.024393003,"teacher_disagreement_score":0.035480905,"about_ca_system_score_codex":0.0018972487,"about_ca_system_score_gemma":0.0027370162,"threshold_uncertainty_score":0.07054877},"labels":[],"label_agreement":null},{"id":"W1970743258","doi":"10.1061/(asce)1076-0342(2004)10:3(119)","title":"Life-Cycle Energy Analysis of a Water Distribution System","year":2004,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and Optimization","field":"Engineering","cited_by":164,"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":"U.S. Department of Commerce","keywords":"Dispose pattern; Energy (signal processing); Service life; Engineering; Environmental science; Energy recovery; Reliability engineering; Process engineering; Waste management; Statistics; Mathematics","score_opus":0.0021289649152352852,"score_gpt":0.1603540088120682,"score_spread":0.15822504389683292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970743258","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":[0.6969498,0.0009030736,0.26465654,0.00037966162,0.000028995,0.0005062017,0.0030405258,0.000288135,0.033247117],"genre_scores_gemma":[0.9811593,0.00023152861,0.014162985,0.000027288113,0.000004467042,0.00018059363,0.0007581243,0.000051100942,0.0034245835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995177,0.00019341461,0.000013911861,0.000048570502,0.00014973158,0.00007663946],"domain_scores_gemma":[0.9994634,0.00030343398,0.00004554434,0.000025845933,0.00014692573,0.000014928351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092032284,0.0007457582,0.0004670746,0.0015834309,0.00043728235,0.00075990264,0.00047514972,0.00053788995,0.0032677846],"category_scores_gemma":[0.0017472378,0.00038636127,0.0007509889,0.0011715713,0.0003922706,0.0011032504,0.0004201689,0.00040802796,0.0002373717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039095175,0.000023636607,0.0013876957,0.00003998224,0.000027143242,0.000042459073,0.000019790075,0.98722863,0.0009834988,0.003221542,0.00015349811,0.0068330714],"study_design_scores_gemma":[0.0000064318338,0.000051926454,0.0014882387,0.0000092366045,0.000013728954,0.00001215344,0.00003098681,0.99392223,0.0012186692,0.002367434,0.0008695407,0.000009364066],"about_ca_topic_score_codex":0.029906815,"about_ca_topic_score_gemma":0.020779196,"teacher_disagreement_score":0.029906815,"about_ca_system_score_codex":0.002651493,"about_ca_system_score_gemma":0.0013453715,"threshold_uncertainty_score":0.059465468},"labels":[],"label_agreement":null},{"id":"W1971599007","doi":"10.1115/ipc2010-31489","title":"Pipeline Integrity Evaluation of Oil Pipelines Using Free-Swimming Acoustic Technology","year":2010,"lang":"en","type":"article","venue":"2010 8th International Pipeline Conference, Volume 1","topic":"Water Systems and 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":"Pure North","funders":"","keywords":"Pipeline transport; Pipeline (software); Leak; Integrity management; Petroleum engineering; Marine engineering; Hazardous waste; Oil field; Engineering; Environmental science; Mechanical engineering; Waste management; Environmental engineering","score_opus":0.035141230541864264,"score_gpt":0.2717693353779666,"score_spread":0.23662810483610233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971599007","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":[0.92566913,0.0003959211,0.07087455,0.0000712769,0.000024379418,0.00015926818,0.00046444524,0.0004261099,0.0019149253],"genre_scores_gemma":[0.98283,0.00019018631,0.01550384,0.000013320996,0.00000450758,0.00003738036,0.00027666142,0.00003500536,0.0011091662],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979663,0.0002409898,0.000095076866,0.0002313577,0.0013581589,0.00010821977],"domain_scores_gemma":[0.99752575,0.0005151388,0.00046681197,0.00024884814,0.0011550357,0.000088356646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011134492,0.00057430455,0.00048371762,0.0011177273,0.00040620408,0.0006541238,0.00082163984,0.0007985617,0.0013311418],"category_scores_gemma":[0.0032651254,0.00031713245,0.00044830018,0.0006001035,0.00047017544,0.0013567493,0.00066197244,0.0002692814,0.00047445545],"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":[0.0008593209,0.00031344753,0.04085518,0.00047893095,0.000094840216,0.00028949304,0.00043566938,0.02417268,0.8442364,0.0003018473,0.00044332113,0.087518975],"study_design_scores_gemma":[0.000052173564,0.0044126613,0.06072915,0.000039653754,0.00011069154,0.0004117002,0.00034113022,0.14148219,0.7895401,0.0002936136,0.002511014,0.00007585376],"about_ca_topic_score_codex":0.0025978694,"about_ca_topic_score_gemma":0.00408339,"teacher_disagreement_score":0.0025978694,"about_ca_system_score_codex":0.0006142049,"about_ca_system_score_gemma":0.0005947741,"threshold_uncertainty_score":0.0058885217},"labels":[],"label_agreement":null},{"id":"W1971742200","doi":"10.1139/l04-067","title":"Pressure transients in a Canadian sewage force main","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":true,"ca_institutions":"","funders":"","keywords":"Water hammer; Head (geology); Marine engineering; Cavitation; Surge tank; Engineering; Hydraulic head; Environmental science; Surge; Hydraulics; Hammer; Geotechnical engineering; Mechanical engineering; Mechanics; Geology; Electrical engineering","score_opus":0.003358499476610905,"score_gpt":0.14484292419387565,"score_spread":0.14148442471726475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971742200","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":[0.9972319,0.000019162977,0.00061898184,0.000043635268,0.000007508124,0.000015059924,0.00024188412,0.0002074425,0.0016144158],"genre_scores_gemma":[0.99899095,0.000013458482,0.00022111305,0.000007704072,8.6714306e-7,0.000004551515,0.00013707686,0.0000073033893,0.00061698834],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998086,0.0000062772633,0.000004215714,0.00003085831,0.00008878879,0.00006127616],"domain_scores_gemma":[0.9995895,0.00010218389,0.000021744112,0.000018841232,0.00019961962,0.0000681978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017619408,0.00033269307,0.00033648364,0.00062708225,0.0012124503,0.0004552165,0.00059251144,0.0005905255,0.0025883117],"category_scores_gemma":[0.0010623594,0.00024592783,0.00017314845,0.0006455302,0.0005473306,0.00020887353,0.00039595924,0.0004927917,0.00025125864],"study_design_candidate":"observational","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":[0.0037501135,0.00096371846,0.17533492,0.00051017426,0.00010514725,0.010631022,0.004696849,0.13559958,0.55087215,0.002113559,0.012269181,0.103153676],"study_design_scores_gemma":[0.00019992075,0.0014566804,0.49904042,0.00003763338,0.000069772985,0.00077392976,0.002198726,0.38579884,0.102225825,0.00037174745,0.0076507586,0.00017574697],"about_ca_topic_score_codex":0.541486,"about_ca_topic_score_gemma":0.5776237,"teacher_disagreement_score":0.45851398,"about_ca_system_score_codex":0.0037113198,"about_ca_system_score_gemma":0.0028834655,"threshold_uncertainty_score":0.9224285},"labels":[],"label_agreement":null},{"id":"W1972644453","doi":"10.1016/j.nucengdes.2009.07.004","title":"A study of acoustic wave damping in water-filled pipes with zero flow and turbulent flow","year":2009,"lang":"en","type":"article","venue":"Nuclear Engineering and Design","topic":"Water Systems and 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 Manitoba","funders":"","keywords":"Turbulence; Flow (mathematics); Mechanics; Zero (linguistics); Acoustics; Mean flow; Materials science; Physics","score_opus":0.010397164228338954,"score_gpt":0.16575002897969235,"score_spread":0.1553528647513534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972644453","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":[0.90959215,0.0006171343,0.079969116,0.00037812837,0.000056723013,0.000052059317,0.000046321104,0.000076193166,0.009212272],"genre_scores_gemma":[0.9943368,0.00015892682,0.002738975,0.000013564392,0.000022515414,0.000012370511,0.000015871845,0.00002179675,0.0026792074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997955,0.0000826173,0.000005937535,0.00003549573,0.00004498727,0.00003547935],"domain_scores_gemma":[0.99832624,0.0013067537,0.00014019595,0.000045082226,0.00010137051,0.00008035335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006454305,0.00067829,0.0006906517,0.0005980383,0.0004096159,0.0010413071,0.00067463814,0.0010936575,0.0013032064],"category_scores_gemma":[0.0038165417,0.0004461584,0.000544666,0.00027910396,0.0020220003,0.001112787,0.0006326504,0.0005911996,0.00010295663],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087296515,0.00024276905,0.004598034,0.00042885193,0.00007468216,0.0010395343,0.00056724105,0.89524305,0.042253643,0.046188116,0.000773937,0.0077172965],"study_design_scores_gemma":[0.000039423972,0.0001148831,0.0009313859,0.000009424416,0.000014259066,0.00004939129,0.000071901944,0.9950814,0.0016125297,0.0018700126,0.00019403624,0.000011372277],"about_ca_topic_score_codex":0.002025501,"about_ca_topic_score_gemma":0.0009071866,"teacher_disagreement_score":0.002025501,"about_ca_system_score_codex":0.00025588344,"about_ca_system_score_gemma":0.00039536352,"threshold_uncertainty_score":0.004359603},"labels":[],"label_agreement":null},{"id":"W1972759888","doi":"10.1115/icone14-89259","title":"Experiments on Damping of Acoustic Waves in Water-Filled Pipes","year":2006,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Manitoba","funders":"","keywords":"Turbulence; Acoustics; Flow (mathematics); Mechanics; Zero (linguistics); Acoustic wave; Materials science; Geology; Physics","score_opus":0.00797435367376952,"score_gpt":0.19622743735015932,"score_spread":0.1882530836763898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972759888","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":[0.9954294,0.0000784309,0.0039357375,0.000026570953,0.0000125519,0.000049398805,0.000030842133,0.0000623307,0.00037476787],"genre_scores_gemma":[0.99614173,0.00009145188,0.0029798814,0.000012568432,0.0000070744436,0.000049211478,0.000028227993,0.000014349219,0.00067539176],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993511,0.00014564062,0.000038771683,0.00012850043,0.0002256776,0.000110449924],"domain_scores_gemma":[0.9976337,0.0014719475,0.00023821181,0.00017632477,0.00033308758,0.00014674908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006821667,0.00084546296,0.00081077276,0.00028393834,0.0006072515,0.0003990679,0.00071105344,0.00084107526,0.0020903095],"category_scores_gemma":[0.0021793356,0.00051396945,0.00029204474,0.00026740352,0.0011065181,0.00078880764,0.0008465548,0.000748126,0.00025613044],"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":[0.00066011236,0.00020811828,0.00052814325,0.00021075713,0.000009911917,0.00012275043,0.000575651,0.0025428773,0.99219304,0.00021632396,0.000048490183,0.0026838768],"study_design_scores_gemma":[0.0001771872,0.005170508,0.0028265724,0.000029511828,0.000023614484,0.000087011176,0.00020399841,0.012302356,0.9781999,0.00017703704,0.0007655731,0.000036776713],"about_ca_topic_score_codex":0.00040024956,"about_ca_topic_score_gemma":0.0003421556,"teacher_disagreement_score":0.0020903095,"about_ca_system_score_codex":0.00020589195,"about_ca_system_score_gemma":0.00021577756,"threshold_uncertainty_score":0.0069927573},"labels":[],"label_agreement":null},{"id":"W1973095166","doi":"10.1115/ipc2008-64092","title":"An Integrated Outflow-Spill Modeling Approach for Risk-Based Valve Placement of Liquid Transmission Pipelines","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Dynamic Systems Analysis (Canada)","funders":"","keywords":"Outflow; Weighting; Reduction (mathematics); Pipeline transport; Computer science; Block (permutation group theory); Pipeline (software); Reliability engineering; Environmental science; Engineering; Mathematics; Geology; Environmental engineering","score_opus":0.02291301906227107,"score_gpt":0.2179637514720862,"score_spread":0.19505073240981513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973095166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058623575,0.00018790457,0.92999303,0.00024899558,0.000036087004,0.00009501767,0.00042217047,0.0007146225,0.009678543],"genre_scores_gemma":[0.8421593,0.0002591402,0.15150498,0.00009020774,0.000036786787,0.00017664916,0.0003964901,0.00020719491,0.005169325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972993,0.000087854896,0.00001525071,0.000050991126,0.00007822424,0.000037826012],"domain_scores_gemma":[0.99960965,0.00017866284,0.000065190216,0.00002560837,0.0000922506,0.000028702507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006180456,0.0008429921,0.00068242324,0.001088624,0.0005723466,0.0015571091,0.0014046243,0.0011840144,0.0025193514],"category_scores_gemma":[0.0013298006,0.000708046,0.0012045371,0.00057812204,0.00052154035,0.00073387387,0.0011810675,0.0008778036,0.00024931523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000680411,0.000010004075,0.00026943302,0.0000067562355,0.0000060909456,0.000014599051,0.000013062664,0.9963141,0.00035252058,0.0014105559,0.000074840114,0.0015211843],"study_design_scores_gemma":[0.0000012703609,0.000005352925,0.000047248835,0.0000016631827,0.0000032607168,0.000004237154,0.0000039260212,0.999175,0.00012898348,0.00044568098,0.00018096341,0.0000023902826],"about_ca_topic_score_codex":0.023250008,"about_ca_topic_score_gemma":0.015795166,"teacher_disagreement_score":0.023250008,"about_ca_system_score_codex":0.0011791819,"about_ca_system_score_gemma":0.001793914,"threshold_uncertainty_score":0.046229362},"labels":[],"label_agreement":null},{"id":"W1974230855","doi":"10.1109/jsyst.2011.2173622","title":"Robust Positioning Systems in the Presence of Outliers Under Weak GPS Signal Conditions","year":2011,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Outlier; Global Positioning System; Robustness (evolution); Ranging; Computer science; Anomaly detection; GPS signals; Algorithm; Assisted GPS; Real-time computing; Data mining; Artificial intelligence; Telecommunications","score_opus":0.039055751054470476,"score_gpt":0.21028672062863513,"score_spread":0.17123096957416464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974230855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020289585,0.0003570464,0.97850525,0.000094803065,0.000040225837,0.000012178986,0.000021212958,0.00029040573,0.00038918844],"genre_scores_gemma":[0.7247183,0.0006566727,0.27278727,0.00008597135,0.00013911369,0.00006988461,0.0001373804,0.00009246921,0.0013129986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847835,0.00047268133,0.00011463799,0.00025854527,0.00055890746,0.00011688609],"domain_scores_gemma":[0.9958687,0.0019968704,0.0009890336,0.0005679677,0.0005109709,0.000066529276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016910762,0.00084353244,0.0012222385,0.00066624716,0.00038498713,0.00090880704,0.0010315375,0.0012666174,0.00054347795],"category_scores_gemma":[0.009545477,0.0003843881,0.0004772191,0.0009416618,0.0007602039,0.0014114489,0.0015300842,0.0010301243,0.00039008755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041572162,0.00003475885,0.0018546946,0.0002287436,0.00018439058,0.00053332996,0.00022552267,0.7990973,0.024508037,0.01744471,0.0008160093,0.15465674],"study_design_scores_gemma":[0.00003060126,0.00016907611,0.00090553274,0.000015389716,0.00004157959,0.00024786065,0.00004034362,0.97520524,0.012388603,0.008827941,0.0020861137,0.00004180998],"about_ca_topic_score_codex":0.000814627,"about_ca_topic_score_gemma":0.00048622317,"teacher_disagreement_score":0.0016910762,"about_ca_system_score_codex":0.00029869482,"about_ca_system_score_gemma":0.0003983306,"threshold_uncertainty_score":0.008943379},"labels":[],"label_agreement":null},{"id":"W1974724610","doi":"10.1115/imece2008-67718","title":"Investigation of Dynamics and Vibration of a Three Unit PIG in Oil and Gas Pipelines","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Waterloo","funders":"","keywords":"Pipeline transport; Pipeline (software); Compressibility; Vibration; Mechanics; Compressible flow; Equations of motion; Transient (computer programming); Differential equation; Position (finance); Momentum (technical analysis); Engineering; Mechanical engineering; Physics; Computer science; Acoustics; Classical mechanics","score_opus":0.017555621197198248,"score_gpt":0.17483981354348813,"score_spread":0.15728419234628987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974724610","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":[0.9831419,0.00002368183,0.016077168,0.0000307914,0.0000036082233,0.0000063464263,0.000010901764,0.00003436386,0.0006712527],"genre_scores_gemma":[0.9984792,0.0000105054105,0.0012591785,0.0000032925225,5.7136504e-7,0.0000022085544,0.0000063694533,0.0000025370634,0.00023617425],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999521,0.000009247368,0.0000013898733,0.000010264721,0.000016359641,0.00001069915],"domain_scores_gemma":[0.999915,0.000034832607,0.000020615487,0.0000067979026,0.000011178854,0.000011482566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017253109,0.00013681244,0.00020183573,0.00017215806,0.00014624528,0.00013745933,0.00018467766,0.00032262746,0.0006422948],"category_scores_gemma":[0.00028938038,0.000094368705,0.0001416871,0.0001068143,0.00040005677,0.0001914826,0.00021045482,0.00016260907,0.0000614433],"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":[0.00069157506,0.00016720896,0.043674093,0.00015533609,0.00004541846,0.0023572193,0.0015721024,0.27237192,0.6291194,0.0025999825,0.00040917806,0.04683658],"study_design_scores_gemma":[0.000016813427,0.00077471725,0.04368033,0.000016413422,0.000016793669,0.00040546645,0.0007532744,0.9270566,0.02585149,0.00053724233,0.0008629443,0.000027998536],"about_ca_topic_score_codex":0.0009841176,"about_ca_topic_score_gemma":0.0008139338,"teacher_disagreement_score":0.0009841176,"about_ca_system_score_codex":0.00014332477,"about_ca_system_score_gemma":0.00013119783,"threshold_uncertainty_score":0.0021486878},"labels":[],"label_agreement":null},{"id":"W1974966940","doi":"10.4296/cwrj111","title":"'Brink DepthDischarge' Relationship for SpatiallyVaried Flows in Pipes","year":2004,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Water Systems and 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":"University of Tehran","keywords":"Mechanics; Supercritical flow; Open-channel flow; Constant (computer programming); Channel (broadcasting); Flow (mathematics); Supercritical fluid; Pressure drop; Pipe flow; Drop (telecommunication); Geology; Physics; Geometry; Engineering; Mathematics; Computer science; Mechanical engineering; Thermodynamics; Turbulence; Telecommunications","score_opus":0.014145195371352073,"score_gpt":0.1831854557352089,"score_spread":0.16904026036385683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974966940","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":[0.9703701,0.0004908101,0.027836347,0.00004358011,0.0000029965129,0.000009171813,0.0000892782,0.00009084348,0.0010669185],"genre_scores_gemma":[0.99802905,0.00009255238,0.001693454,0.000005569239,9.2460186e-7,0.0000025586892,0.00003349632,0.0000034323784,0.00013890938],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998598,0.000014836181,0.000009628316,0.00003185826,0.000057550813,0.000026267968],"domain_scores_gemma":[0.99912566,0.00042546657,0.0002780883,0.00006344792,0.000086718144,0.000020624864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002743079,0.0001132402,0.0001483928,0.0005646465,0.00010497863,0.0002496235,0.00030506,0.00027317,0.00042432602],"category_scores_gemma":[0.0016838057,0.00011544413,0.00014160106,0.00029420835,0.0003770149,0.00041857795,0.00032830183,0.00030159758,0.000067080604],"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":[0.00055054255,0.00011995226,0.09271371,0.00031092932,0.00004874683,0.00057665503,0.0006622423,0.18293951,0.6146086,0.006366162,0.0003565441,0.10074645],"study_design_scores_gemma":[0.00002649855,0.00043885992,0.19322978,0.000028474324,0.000031519077,0.00092690036,0.00023984692,0.42385134,0.37533003,0.003147411,0.0026641209,0.00008533207],"about_ca_topic_score_codex":0.0014504436,"about_ca_topic_score_gemma":0.0013727118,"teacher_disagreement_score":0.0014504436,"about_ca_system_score_codex":0.00048191886,"about_ca_system_score_gemma":0.00014923155,"threshold_uncertainty_score":0.0034965873},"labels":[],"label_agreement":null},{"id":"W1975107556","doi":"10.14796/jwmm.c374","title":"Understanding Stormwater Pipe Deterioration Through Data Mining","year":2014,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Guelph","keywords":"Stormwater; Closed circuit; Task (project management); Stormwater management; Water pipe; Computer science; Environmental science; Civil engineering; Hydrology (agriculture); Engineering; Geotechnical engineering; Telecommunications; Surface runoff; Systems engineering","score_opus":0.13109227420580674,"score_gpt":0.2394594851395886,"score_spread":0.10836721093378185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975107556","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":[0.6370106,0.0009888484,0.33622554,0.0021944472,0.000051850056,0.0006688178,0.016593121,0.0025014991,0.003765282],"genre_scores_gemma":[0.84690684,0.000555933,0.13742258,0.00011017213,0.000031978278,0.00028069844,0.013667534,0.00003792144,0.0009862613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991486,0.00016519471,0.00012587225,0.00022449196,0.00025153585,0.0000842419],"domain_scores_gemma":[0.99720734,0.001565177,0.00045185728,0.0002540602,0.00045422924,0.00006735559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015756425,0.0008029852,0.00071940885,0.0030710076,0.00045566692,0.0012810163,0.0009936326,0.0008538679,0.00049238413],"category_scores_gemma":[0.005743471,0.00031848502,0.0008636692,0.0021659166,0.0003551514,0.0014530676,0.00071430334,0.0006956049,0.00024148938],"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":[0.00036953803,0.0009011486,0.2633923,0.00062154175,0.00026112044,0.0010485497,0.00096633413,0.3230123,0.0079843,0.0038003398,0.007859479,0.3897831],"study_design_scores_gemma":[0.000015213396,0.00014264532,0.044634458,0.00008061036,0.000056720855,0.0001759789,0.0007006209,0.9358346,0.0053373524,0.008505748,0.0044878684,0.000028177577],"about_ca_topic_score_codex":0.013552724,"about_ca_topic_score_gemma":0.016691074,"teacher_disagreement_score":0.013552724,"about_ca_system_score_codex":0.00083298894,"about_ca_system_score_gemma":0.0011003688,"threshold_uncertainty_score":0.026947677},"labels":[],"label_agreement":null},{"id":"W1975613720","doi":"10.1061/40994(321)82","title":"''Carpet Center of the World'' Is Guaranteed a Long Term Water Supply","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Research Canada","funders":"","keywords":"Pipeline transport; Pipeline (software); Mile; Environmental science; Transformer; Water supply; Computer science; Engineering; Civil engineering; Electrical engineering; Mechanical engineering; Environmental engineering; Geology","score_opus":0.007556644752712223,"score_gpt":0.1739171485664921,"score_spread":0.16636050381377987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975613720","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":[0.11661011,0.0024103434,0.012739115,0.015392252,0.0017895701,0.00024479203,0.0033306861,0.0024348458,0.84504825],"genre_scores_gemma":[0.25434092,0.000932955,0.007739616,0.0018866854,0.000107507636,0.00008020481,0.0016733307,0.00026809695,0.7329707],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994301,0.000030088395,0.000020531459,0.00010268335,0.0002302973,0.00018633936],"domain_scores_gemma":[0.9993475,0.000028657021,0.000052305433,0.00007381934,0.00032075416,0.0001769669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034819558,0.00040642582,0.00028135756,0.0006678212,0.0030304096,0.0034170793,0.000693935,0.00092213583,0.08241261],"category_scores_gemma":[0.00091842806,0.00028917714,0.00015410395,0.00090299716,0.00083455053,0.002096188,0.00211278,0.0011127817,0.0095596835],"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":[0.00042025794,0.0002041223,0.013466299,0.00044490624,0.000024952828,0.00096662366,0.0010461776,0.0009384532,0.012935481,0.072546735,0.5105171,0.3864888],"study_design_scores_gemma":[0.000011793592,0.00005029234,0.006540475,0.000027069662,0.0000031441436,0.0002112153,0.0003401761,0.00031142007,0.0017963807,0.000739794,0.9899464,0.000021696702],"about_ca_topic_score_codex":0.04496125,"about_ca_topic_score_gemma":0.12016394,"teacher_disagreement_score":0.08241261,"about_ca_system_score_codex":0.0037549995,"about_ca_system_score_gemma":0.0075673116,"threshold_uncertainty_score":0.2756976},"labels":[],"label_agreement":null},{"id":"W1975791233","doi":"10.1016/j.envint.2006.06.001","title":"A commentary on recent water safety initiatives in the context of water utility risk management","year":2006,"lang":"en","type":"review","venue":"Environment International","topic":"Water Systems and Optimization","field":"Engineering","cited_by":54,"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":"Cranfield University; University of Alberta","keywords":"Context (archaeology); Risk analysis (engineering); Water safety; Risk management; Business; Hazard; Hazard analysis; Environmental planning; Water quality; Risk assessment; Environmental resource management; Engineering; Environmental science; Computer science; Computer security","score_opus":0.018206828451117072,"score_gpt":0.23122091648901594,"score_spread":0.21301408803789887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975791233","genre_codex":"commentary","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":[0.000083301915,0.22153625,0.00017266501,0.6828055,0.09357314,0.0000110277615,0.00010163277,0.000013726641,0.0017027576],"genre_scores_gemma":[0.0017003348,0.15291296,0.00037560833,0.73422194,0.10644397,0.000046643454,0.000084445244,0.000031708336,0.004182405],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944541,0.0016605478,0.0007508411,0.0005920301,0.0022101116,0.00033240803],"domain_scores_gemma":[0.96451765,0.02378562,0.002652174,0.00036558104,0.007256652,0.0014221303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008565455,0.0013678194,0.0027356811,0.002757166,0.002013818,0.004503279,0.00341107,0.02657175,0.0054920632],"category_scores_gemma":[0.034902,0.00072601065,0.0018652609,0.004662927,0.0044793575,0.0057688444,0.0025337166,0.023890307,0.0033571974],"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":[0.000032747983,0.00000685922,0.00003032682,0.0011600679,0.00002217019,0.00017899506,0.00014257237,0.000039658556,0.000068707,0.0032028651,0.97908336,0.016031679],"study_design_scores_gemma":[0.00004073004,0.00002373903,0.00025209566,0.0035027813,0.000048314643,0.0003128934,0.00025057886,0.000032112443,0.00005915232,0.0033291213,0.99212694,0.000021465681],"about_ca_topic_score_codex":0.010703599,"about_ca_topic_score_gemma":0.028489262,"teacher_disagreement_score":0.02657175,"about_ca_system_score_codex":0.004795766,"about_ca_system_score_gemma":0.011539321,"threshold_uncertainty_score":0.045298994},"labels":[],"label_agreement":null},{"id":"W1976264099","doi":"10.1243/0954406001523957","title":"Pressure wave attenuation in an air pipe flow","year":2000,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science","topic":"Water Systems and 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":"","keywords":"Flow measurement; Attenuation; Thermal mass flow meter; Acoustics; Flow (mathematics); Pipe flow; Ultrasonic flow meter; Mechanics; Volumetric flow rate; Pressure measurement; Fluidics; Engineering; Materials science; Mass flow meter; Mechanical engineering; Electrical engineering; Optics; Physics; Turbulence","score_opus":0.010754207404441437,"score_gpt":0.20060451264273008,"score_spread":0.18985030523828864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976264099","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":[0.89971966,0.00044735146,0.09655323,0.00010126879,0.00002990361,0.00004408321,0.000054787826,0.0005182745,0.0025313783],"genre_scores_gemma":[0.99120796,0.00019242037,0.0075330166,0.000013260669,0.000015261523,0.000025568923,0.00004095511,0.000019245479,0.00095227204],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911326,0.00017038606,0.00001736367,0.00014151752,0.0004953684,0.00006212123],"domain_scores_gemma":[0.9984036,0.0009406058,0.0002971339,0.00008002194,0.00024547032,0.00003320335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075426226,0.00045258433,0.000468556,0.00036438627,0.00023010334,0.00034965118,0.00052055845,0.00045246206,0.0012719544],"category_scores_gemma":[0.002406169,0.0002464757,0.00018972554,0.0002820362,0.0010632662,0.0011284911,0.0004735299,0.00057584036,0.00019076021],"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":[0.000411989,0.00008743889,0.0021628954,0.00015308335,0.0000074120403,0.00015323599,0.00026777948,0.011941403,0.95931804,0.0013868038,0.00018969222,0.023920137],"study_design_scores_gemma":[0.000059888167,0.0017515055,0.0064098975,0.000028414517,0.000022678565,0.00018800663,0.000077714874,0.1346702,0.8547456,0.00063750567,0.0013778977,0.000030597923],"about_ca_topic_score_codex":0.00053972803,"about_ca_topic_score_gemma":0.00020833692,"teacher_disagreement_score":0.0012719544,"about_ca_system_score_codex":0.00034228936,"about_ca_system_score_gemma":0.00021999827,"threshold_uncertainty_score":0.004255116},"labels":[],"label_agreement":null},{"id":"W1977109094","doi":"10.5942/jawwa.2012.104.0055","title":"How severe can transients be after a sudden depressurization?","year":2012,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and Optimization","field":"Engineering","cited_by":35,"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":"Engineering and Physical Sciences Research Council; University of Sheffield","keywords":"Cabin pressurization; Superposition principle; Cavitation; Transient (computer programming); Mechanics; Shock wave; Shock (circulatory); Overpressure; Reflection (computer programming); Materials science; Acoustics; Geology; Physics; Computer science; Thermodynamics; Composite material","score_opus":0.0034820219502231416,"score_gpt":0.16415288073960166,"score_spread":0.1606708587893785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977109094","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":[0.8566912,0.005080444,0.079524554,0.014657871,0.0013964786,0.00013294732,0.0008016583,0.0014475895,0.040267315],"genre_scores_gemma":[0.9953744,0.000783021,0.0017243796,0.0006425997,0.00021346341,0.000019751225,0.00012161293,0.0000695198,0.0010512786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987393,0.00017701647,0.00009502781,0.00021420914,0.00036902848,0.00040535242],"domain_scores_gemma":[0.994124,0.0024471167,0.001804747,0.00040415258,0.0006797382,0.00054018426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013524359,0.0006549597,0.0006404683,0.0005695595,0.0013198056,0.0025176553,0.0007608305,0.0021125516,0.0037149678],"category_scores_gemma":[0.009781289,0.0004095735,0.00049738225,0.0005428334,0.001652945,0.005027438,0.0013974075,0.0019663714,0.0010296995],"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":[0.002518716,0.0011161003,0.25747815,0.0021630023,0.0006934037,0.017182214,0.005615561,0.13653243,0.12981579,0.056977145,0.031685565,0.3582219],"study_design_scores_gemma":[0.00017578136,0.0032792715,0.43755618,0.0013519933,0.00064128335,0.017092966,0.03577547,0.076231554,0.13680384,0.1910931,0.099136025,0.00086264324],"about_ca_topic_score_codex":0.0011838762,"about_ca_topic_score_gemma":0.0012782412,"teacher_disagreement_score":0.0037149678,"about_ca_system_score_codex":0.00064108544,"about_ca_system_score_gemma":0.0005622773,"threshold_uncertainty_score":0.012427866},"labels":[],"label_agreement":null},{"id":"W1977130378","doi":"10.2166/hydro.2014.007","title":"Comparing the utility of decision trees and support vector machines when planning inspections of linear sewer infrastructure","year":2014,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Support vector machine; Decision tree; Leverage (statistics); Decision support system; Predictive analytics; Data mining; Computer science; Classifier (UML); Decision tree learning; Predictive modelling; Task (project management); Machine learning; Engineering; Artificial intelligence","score_opus":0.009952872047605844,"score_gpt":0.2159821103077458,"score_spread":0.20602923826013997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977130378","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":[0.8890578,0.0027225162,0.098802224,0.0011247367,0.0002473803,0.00028781604,0.0016612556,0.0012135056,0.004882783],"genre_scores_gemma":[0.9685708,0.0004942054,0.028603317,0.00006618937,0.000051111885,0.00009332427,0.001447956,0.000041322484,0.00063174486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969838,0.0015269495,0.00024662877,0.000371969,0.00059827004,0.0002723867],"domain_scores_gemma":[0.9665717,0.028676346,0.0010759443,0.00070635974,0.0023181858,0.00065141707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006605335,0.0015816491,0.001100828,0.0033555704,0.00044365975,0.0016384209,0.0009481636,0.0015179927,0.0009845664],"category_scores_gemma":[0.031703573,0.00036797873,0.00089079054,0.0022781563,0.00046481632,0.002180742,0.00088816916,0.001331173,0.00045321207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020523793,0.00047971934,0.025245193,0.00034731042,0.00020057546,0.00010632193,0.00017290405,0.81627387,0.0007652658,0.0015967801,0.0024933638,0.15026636],"study_design_scores_gemma":[0.00003767821,0.00028630262,0.0039842986,0.000028680064,0.000026311907,0.000015916405,0.00010337076,0.99312913,0.00059576717,0.0014877483,0.0002878717,0.000017008397],"about_ca_topic_score_codex":0.019331742,"about_ca_topic_score_gemma":0.012844343,"teacher_disagreement_score":0.019331742,"about_ca_system_score_codex":0.0013654513,"about_ca_system_score_gemma":0.0016600997,"threshold_uncertainty_score":0.03843844},"labels":[],"label_agreement":null},{"id":"W1977565935","doi":"10.1061/(asce)hy.1943-7900.0000918","title":"Profile-Induced Column Separation and Rejoining during Rapid Pipeline Filling","year":2014,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Separation (statistics); Column (typography); Pipeline (software); Water column; Mechanics; Flow (mathematics); Pipeline transport; Hydraulics; Geotechnical engineering; Geology; Petroleum engineering; Structural engineering; Engineering; Computer science; Physics; Mechanical engineering","score_opus":0.006123144826265288,"score_gpt":0.19418431854663,"score_spread":0.18806117372036474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977565935","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":[0.98399323,0.000054006523,0.014266871,0.000066210385,0.000012414228,0.000018565946,0.000060055598,0.00014061134,0.0013880644],"genre_scores_gemma":[0.99866843,0.000015249343,0.0009995768,0.0000046327436,8.617157e-7,0.000004893171,0.000015787127,0.000008298075,0.00028228876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998952,0.000011161291,0.0000040790014,0.000013205054,0.00002408961,0.000052241394],"domain_scores_gemma":[0.9996505,0.00015708404,0.000078088255,0.000026202828,0.000032913358,0.0000552527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022563545,0.00020247856,0.00037672836,0.00019113638,0.0004475472,0.0004989433,0.0002662158,0.0004732742,0.0010483634],"category_scores_gemma":[0.00088844466,0.00019368093,0.00031036895,0.00020723556,0.00066410133,0.00044819704,0.00049249915,0.00045320514,0.00010304266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006026356,0.00011419628,0.008296623,0.00014482619,0.000017420734,0.0012955137,0.00039992636,0.78978527,0.18477702,0.00334837,0.00044285358,0.0107753035],"study_design_scores_gemma":[0.000034219982,0.0003242858,0.004948285,0.000012633567,0.000009467484,0.00013758942,0.00027054918,0.9454228,0.04745883,0.00083159,0.0005205849,0.000029221592],"about_ca_topic_score_codex":0.0040598176,"about_ca_topic_score_gemma":0.0030405803,"teacher_disagreement_score":0.0040598176,"about_ca_system_score_codex":0.00045751562,"about_ca_system_score_gemma":0.00066144654,"threshold_uncertainty_score":0.008072376},"labels":[],"label_agreement":null},{"id":"W1977718812","doi":"10.1061/41109(373)67","title":"Towards Optimum Condition Assessment Policies for Water and Sewer Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Asset management; Asset (computer security); Value of information; Computer science; Decision support system; Sanitary sewer; Risk analysis (engineering); Reliability (semiconductor); Environmental science; Business; Computer security; Environmental engineering; Finance","score_opus":0.006037622164029883,"score_gpt":0.2299469857760405,"score_spread":0.2239093636120106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977718812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.101257406,0.00045932733,0.88988996,0.00066157075,0.00003189309,0.00040760642,0.0001989336,0.0007090968,0.006384215],"genre_scores_gemma":[0.9018064,0.00033784032,0.095825784,0.00009097932,0.00002882192,0.00019643121,0.00016668055,0.00007872815,0.0014683886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822813,0.00065174943,0.00007473416,0.0003406948,0.00031288044,0.00039184143],"domain_scores_gemma":[0.9924615,0.004636762,0.0013232261,0.00016355557,0.0010505102,0.00036448304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035115615,0.0014105935,0.0015123902,0.0021844099,0.0007102015,0.00270833,0.0013487687,0.001747924,0.0036325452],"category_scores_gemma":[0.013687779,0.0011207653,0.00047093775,0.0008760322,0.0013600658,0.0039441157,0.0013694565,0.0015230291,0.0003657301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013644541,0.000073188756,0.00078175357,0.000054674252,0.000020991058,0.000044404856,0.00007890358,0.9671851,0.0016212235,0.009752486,0.0008800396,0.019370891],"study_design_scores_gemma":[0.000021629983,0.000037306316,0.00024172867,0.000012993848,0.000007342869,0.0000069558955,0.000044391836,0.9934302,0.00057305914,0.005368673,0.00024858557,0.000007151311],"about_ca_topic_score_codex":0.010388564,"about_ca_topic_score_gemma":0.009398679,"teacher_disagreement_score":0.010388564,"about_ca_system_score_codex":0.0029669139,"about_ca_system_score_gemma":0.0033570777,"threshold_uncertainty_score":0.021526515},"labels":[],"label_agreement":null},{"id":"W1978740682","doi":"10.1139/l99-066","title":"Acoustic discharge measurements for the performance testing of low-head hydroelectric turbines under disturbed flow conditions","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Turbine; Acoustics; Traverse; Flow measurement; Head (geology); Marine engineering; Engineering; Mechanics; Mechanical engineering; Geology; Physics","score_opus":0.01596791128406748,"score_gpt":0.18598211234175127,"score_spread":0.17001420105768378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978740682","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":[0.8090524,0.0004937242,0.18712856,0.00010668068,0.000038961578,0.00016644236,0.00038415688,0.0007399255,0.0018890804],"genre_scores_gemma":[0.96464455,0.00015379408,0.034404512,0.000025613874,0.0000076272063,0.000063463,0.0002048433,0.000034284796,0.00046137522],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991609,0.00017362983,0.000056369136,0.00006403867,0.0005111867,0.00003391009],"domain_scores_gemma":[0.99788755,0.0008172229,0.0002940628,0.00020656503,0.0007078055,0.000086801534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009292747,0.0003142839,0.00032520105,0.00045571302,0.00037894267,0.00035563725,0.0005306883,0.00033846067,0.0012553356],"category_scores_gemma":[0.0031260045,0.00016950448,0.00013452426,0.00047230712,0.0004449488,0.0005864014,0.00039547792,0.0004211208,0.00042167678],"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":[0.00037289958,0.000272211,0.024453105,0.00033485563,0.000014589602,0.00014204542,0.0002644032,0.007924747,0.8876491,0.0003922881,0.0005818037,0.0775979],"study_design_scores_gemma":[0.00009934139,0.0032060167,0.09057626,0.00004049964,0.000056925066,0.00060241163,0.00029365387,0.07100444,0.8289551,0.0006458968,0.0044474513,0.000071948634],"about_ca_topic_score_codex":0.0010328743,"about_ca_topic_score_gemma":0.0023752418,"teacher_disagreement_score":0.0012553356,"about_ca_system_score_codex":0.00037227978,"about_ca_system_score_gemma":0.00032350002,"threshold_uncertainty_score":0.004914582},"labels":[],"label_agreement":null},{"id":"W1978909080","doi":"10.1061/(asce)is.1943-555x.0000252","title":"Framework for Modeling On-Site Productivity of Preventive Maintenance Activities for Wastewater Collection Systems","year":2015,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and 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":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Flushing; Duration (music); Scheduling (production processes); Engineering; Global Positioning System; Environmental science; Operations research; Operations management","score_opus":0.016095706082805146,"score_gpt":0.23146262934204498,"score_spread":0.21536692325923984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978909080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040432736,0.00055069313,0.9480827,0.0006456251,0.000085741034,0.0001661783,0.0014150235,0.00063202064,0.007989391],"genre_scores_gemma":[0.8562978,0.0013014561,0.12394654,0.00015381743,0.0001575154,0.0011640966,0.0013474132,0.00022922247,0.0154021615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999003,0.00035809763,0.00004786658,0.00023045721,0.00017756967,0.00018298265],"domain_scores_gemma":[0.99838316,0.0010247697,0.00023481734,0.000046694215,0.00022159835,0.000088901565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002479033,0.0018949863,0.0015080258,0.0015571143,0.0007159109,0.0020153315,0.0031709836,0.0029322878,0.0048895613],"category_scores_gemma":[0.004322976,0.001058011,0.0021710806,0.0014624456,0.00086406514,0.001403318,0.0013395669,0.0018743506,0.0007619442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071743852,0.000024697714,0.00034856496,0.000012836867,0.000015093279,0.000020793515,0.000012084979,0.9944026,0.00010770465,0.0036418957,0.00014624116,0.0012603789],"study_design_scores_gemma":[0.0000033633878,0.000007917931,0.00010510287,0.0000023476111,0.0000047815556,0.0000032769074,0.0000060268676,0.9984546,0.000025031015,0.0011892709,0.00019489053,0.000003327026],"about_ca_topic_score_codex":0.07938817,"about_ca_topic_score_gemma":0.043828107,"teacher_disagreement_score":0.07938817,"about_ca_system_score_codex":0.0028830266,"about_ca_system_score_gemma":0.0040100417,"threshold_uncertainty_score":0.15785217},"labels":[],"label_agreement":null},{"id":"W1979344695","doi":"10.1243/1748006xjrr160","title":"Single-objective deterministic versus multi-objective stochastic water network design: Practical considerations for the water industry","year":2008,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Robustness (evolution); Pareto principle; Computer science; Mathematical optimization; Network planning and design; Multi-objective optimization; Pareto analysis; Flexibility (engineering); Operations research; Engineering; Mathematics","score_opus":0.03972253696518675,"score_gpt":0.24410622523600503,"score_spread":0.20438368827081826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979344695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030326791,0.0009921085,0.96434027,0.0006765438,0.000038508235,0.0000822401,0.000047511385,0.00009579604,0.003400246],"genre_scores_gemma":[0.7076273,0.001590981,0.28745428,0.00019551204,0.00005277599,0.00031100606,0.000076509525,0.00007487273,0.0026167356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832445,0.0009391424,0.00006727253,0.00015904778,0.00043567477,0.00007443834],"domain_scores_gemma":[0.99716264,0.0021489365,0.00022907028,0.00009904088,0.0002929767,0.00006725669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003913178,0.00066360907,0.001158957,0.0006066238,0.00037827838,0.0012002891,0.0010039997,0.0011757951,0.0016464078],"category_scores_gemma":[0.005904897,0.0007333789,0.0009122798,0.00063901965,0.0008532058,0.0013655494,0.0007694139,0.0009455073,0.00016981183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020619998,0.000010227137,0.00026669214,0.00007114656,0.000015495345,0.000022015493,0.000011896078,0.97972566,0.00045617006,0.005891109,0.00010659107,0.013402336],"study_design_scores_gemma":[0.000009719424,0.000057994424,0.00017512617,0.000015790045,0.000007842317,0.000015241765,0.00001348522,0.991667,0.00033737964,0.0070444625,0.00064804876,0.000007927922],"about_ca_topic_score_codex":0.0026172628,"about_ca_topic_score_gemma":0.0045696525,"teacher_disagreement_score":0.003913178,"about_ca_system_score_codex":0.001159042,"about_ca_system_score_gemma":0.0016860667,"threshold_uncertainty_score":0.02069509},"labels":[],"label_agreement":null},{"id":"W1979381021","doi":"10.1080/10643380500531171","title":"Risk Analysis Strategies in the Water Utility Sector: An Inventory of Applications for Better and More Credible Decision Making","year":2006,"lang":"en","type":"article","venue":"Critical Reviews in Environmental Science and Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":65,"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":"Canadian Water Network; American Water Works Association Research Foundation","keywords":"Restructuring; Risk management; Risk analysis (engineering); Business; Asset (computer security); Competition (biology); Water utility; Finance; Water supply; Computer science; Engineering","score_opus":0.012853650030827337,"score_gpt":0.272396778059564,"score_spread":0.2595431280287367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979381021","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":[0.009657456,0.88935244,0.059949573,0.014117783,0.00030227582,0.00022341071,0.000069826565,0.00012645754,0.026200864],"genre_scores_gemma":[0.05996307,0.84250855,0.09236368,0.0012361069,0.0005219854,0.00021698416,0.000105485524,0.00006166781,0.0030225427],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98813826,0.0047812774,0.0013092145,0.00071328523,0.0046235477,0.00043440732],"domain_scores_gemma":[0.965808,0.02135928,0.0030173466,0.0014379197,0.007843329,0.0005342206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01963793,0.002268371,0.0016933945,0.0120601235,0.0011770518,0.008854629,0.0017977392,0.0025639238,0.0025524015],"category_scores_gemma":[0.025603335,0.0010868908,0.001309393,0.010039261,0.0028388007,0.011619913,0.0023757762,0.0036553405,0.0007636029],"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":[0.00007089112,0.00013280136,0.0016965949,0.004139513,0.0000924964,0.00017269135,0.000880524,0.013956182,0.0015319536,0.08390444,0.007052947,0.88636905],"study_design_scores_gemma":[0.000066851586,0.00092514703,0.007445419,0.02020462,0.00032314603,0.0011588694,0.0052767335,0.036962774,0.006798289,0.300098,0.62033397,0.00040619937],"about_ca_topic_score_codex":0.004516014,"about_ca_topic_score_gemma":0.005077331,"teacher_disagreement_score":0.01963793,"about_ca_system_score_codex":0.005355434,"about_ca_system_score_gemma":0.0068172333,"threshold_uncertainty_score":0.103856504},"labels":[],"label_agreement":null},{"id":"W1979474788","doi":"10.1016/s1350-6307(99)00005-9","title":"A preliminary analysis of failures in grey cast iron water pipes","year":2000,"lang":"en","type":"article","venue":"Engineering Failure Analysis","topic":"Water Systems and Optimization","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":false,"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; University of Toronto; American Water Works Association Research Foundation","keywords":"Failure causes; Water pipe; Forensic engineering; Failure mechanism; Failure mode and effects analysis; Engineering; Structural engineering; Reliability engineering; Mechanical engineering","score_opus":0.002552204835194741,"score_gpt":0.16201102157508626,"score_spread":0.1594588167398915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979474788","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":[0.7833327,0.000798865,0.20166,0.00034323847,0.000042472482,0.00006957065,0.00027112654,0.0002912444,0.013190762],"genre_scores_gemma":[0.986402,0.00021614411,0.0064574117,0.000018395565,0.000019588124,0.000016820137,0.00011990133,0.000027836488,0.006721959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99985635,0.000036190504,0.0000040665614,0.00001582076,0.000050464034,0.00003716119],"domain_scores_gemma":[0.9991541,0.0006212079,0.000037211877,0.00004804822,0.000114317714,0.000025097786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000436935,0.0007047951,0.00058266753,0.00077214226,0.0004996682,0.00050341187,0.0006813594,0.0007550612,0.0037182588],"category_scores_gemma":[0.0021859934,0.00029487838,0.00064032065,0.00048129246,0.0011112717,0.0005586657,0.00029024467,0.00046065883,0.0002312393],"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":[0.0004189645,0.00008898135,0.0030060294,0.00021865095,0.000029710376,0.00060738175,0.0002333063,0.9233955,0.025394293,0.019288184,0.0018337755,0.02548513],"study_design_scores_gemma":[0.000012611205,0.00017044866,0.0055461405,0.000012177391,0.000020916006,0.00009447478,0.00009694957,0.97983,0.005040349,0.008336063,0.0008265963,0.000013195561],"about_ca_topic_score_codex":0.0110124955,"about_ca_topic_score_gemma":0.0108052045,"teacher_disagreement_score":0.0110124955,"about_ca_system_score_codex":0.0004890663,"about_ca_system_score_gemma":0.0005133195,"threshold_uncertainty_score":0.02189678},"labels":[],"label_agreement":null},{"id":"W1980898257","doi":"10.1016/j.jup.2012.08.001","title":"Global catastrophes and trends: Assessing the applications to the electric utility sector","year":2012,"lang":"en","type":"article","venue":"Utilities Policy","topic":"Water Systems and 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":"Domtar (Canada)","funders":"","keywords":"Water sector; Regional science; Economic sector; Business; Environmental planning; Economics; Economic geography; Economy; Engineering; Geography; Water supply; Environmental engineering","score_opus":0.01806621535269224,"score_gpt":0.26735176057919274,"score_spread":0.2492855452265005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980898257","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":[0.98137295,0.0012889173,0.0051492047,0.0018049631,0.000044305365,0.00005753226,0.0032103383,0.00006184124,0.0070099547],"genre_scores_gemma":[0.9960335,0.0012114831,0.0012850323,0.000060184066,0.000032011987,0.000026571464,0.00083007885,0.000009707353,0.00051142013],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993236,0.00032505422,0.000045829303,0.00006824484,0.00014833253,0.0000889528],"domain_scores_gemma":[0.9951584,0.0021088796,0.001081592,0.00018225603,0.0011267348,0.0003420964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001850567,0.0006563575,0.0003998276,0.004614423,0.00022645466,0.0013876435,0.00039722674,0.00064452074,0.0011876158],"category_scores_gemma":[0.009466629,0.00014573606,0.00077231333,0.008205319,0.00064580864,0.00216066,0.00092975737,0.00060740835,0.00012528924],"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":[0.00048262323,0.00020355853,0.7021405,0.00037932687,0.0005156969,0.00041886614,0.0005667496,0.19883299,0.0007303754,0.013722996,0.005247569,0.076758735],"study_design_scores_gemma":[0.00002995259,0.00070528756,0.65075696,0.00015679449,0.0002023946,0.00025175713,0.0052659605,0.31994924,0.0011515102,0.015194166,0.006270222,0.00006571981],"about_ca_topic_score_codex":0.022328591,"about_ca_topic_score_gemma":0.028762672,"teacher_disagreement_score":0.022328591,"about_ca_system_score_codex":0.0015237232,"about_ca_system_score_gemma":0.0010109874,"threshold_uncertainty_score":0.044397235},"labels":[],"label_agreement":null},{"id":"W1981194772","doi":"10.1021/es011099s","title":"Interfacial Mass Transfer in Randomly Packed Towers:  A Confident Correlation for Environmental Applications","year":2001,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":32,"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":"Dimensionless quantity; Schmidt number; Froude number; Mass transfer; Sherwood number; Reynolds number; Range (aeronautics); Chemistry; Mass transfer coefficient; Standard deviation; Transfer (computing); Mechanics; Thermodynamics; Tower; Correlation; Statistics; Mathematics; Chromatography; Physics; Computer science; Geometry; Engineering; Structural engineering; Aerospace engineering","score_opus":0.003885598803216087,"score_gpt":0.18032578001849406,"score_spread":0.17644018121527796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981194772","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":[0.78485525,0.000715099,0.20992678,0.00006349213,0.000038078844,0.0001028623,0.001442208,0.0009347878,0.0019215121],"genre_scores_gemma":[0.96163803,0.00013916905,0.03680665,0.000013393606,0.00000790694,0.00004564977,0.0011113008,0.00002491454,0.00021297116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986278,0.00032608272,0.00012565809,0.00030557305,0.00055511884,0.000059766622],"domain_scores_gemma":[0.9951336,0.0023503208,0.00049412367,0.00076074357,0.0012056036,0.000055675715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003026898,0.0007001327,0.00062999065,0.002181425,0.0003575319,0.0008124893,0.0008513093,0.00068405137,0.0008068844],"category_scores_gemma":[0.010010579,0.00033511905,0.00051380513,0.0018324648,0.00023244359,0.000894101,0.00050372194,0.0004858993,0.0004491857],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078409014,0.00062013394,0.19305032,0.0004718468,0.00027387985,0.00042659015,0.00015059154,0.47856417,0.08802,0.0014597112,0.0017208603,0.2344577],"study_design_scores_gemma":[0.000010444028,0.00024069396,0.051090714,0.000021378437,0.000047108177,0.00015677857,0.00004247873,0.9176659,0.029391393,0.0004408582,0.0008495288,0.000042718293],"about_ca_topic_score_codex":0.0030893688,"about_ca_topic_score_gemma":0.0033695642,"teacher_disagreement_score":0.0030893688,"about_ca_system_score_codex":0.00052833284,"about_ca_system_score_gemma":0.00052033423,"threshold_uncertainty_score":0.01600796},"labels":[],"label_agreement":null},{"id":"W1983402293","doi":"10.1080/15730620412331289995","title":"Fluid transients and pipeline optimization using GA and PSO: the diameter connection","year":2004,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":45,"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":"Particle swarm optimization; Pipeline (software); Transient (computer programming); Mathematical optimization; Computer science; Genetic algorithm; Selection (genetic algorithm); Pipeline transport; Pipe network analysis; Process (computing); Nonlinear system; Optimization problem; Engineering; Mathematics; Artificial intelligence; Mechanical engineering; Physics","score_opus":0.008926827393763787,"score_gpt":0.1747783576892776,"score_spread":0.1658515302955138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983402293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056090664,0.00036640762,0.9369268,0.00022070344,0.000037142403,0.000030641568,0.000017579783,0.00018662015,0.0061235405],"genre_scores_gemma":[0.80752784,0.0005358381,0.18836857,0.00005728217,0.00003168863,0.000089427114,0.0000376525,0.00005967627,0.0032920903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998903,0.000045929082,0.0000048018806,0.000018870409,0.00002765324,0.000012423492],"domain_scores_gemma":[0.99980336,0.00013242108,0.000021842257,0.000010894732,0.00002367719,0.000007820018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028382437,0.00044592243,0.00040180093,0.00040928644,0.00019912615,0.00047516785,0.00032089,0.0007313849,0.00060541206],"category_scores_gemma":[0.0013076589,0.00022170179,0.00040073236,0.00040163126,0.0005266884,0.0005418866,0.00029907352,0.00035143507,0.00010139886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026790976,0.000010645379,0.00038349032,0.000024693672,0.0000098516775,0.000039180264,0.000025111622,0.9653234,0.0023445098,0.008346157,0.00015796769,0.023308078],"study_design_scores_gemma":[0.000004609621,0.000025154348,0.000109337016,0.0000027367003,0.0000037600287,0.000013554169,0.0000053040535,0.99670404,0.0006011897,0.0020552657,0.00047203043,0.000002957242],"about_ca_topic_score_codex":0.0020305484,"about_ca_topic_score_gemma":0.0014747572,"teacher_disagreement_score":0.0020305484,"about_ca_system_score_codex":0.00036545654,"about_ca_system_score_gemma":0.00039995855,"threshold_uncertainty_score":0.00403744},"labels":[],"label_agreement":null},{"id":"W1983792072","doi":"10.1109/ccece.2012.6335031","title":"Optimal pump operation for water distribution systems using a new multi-agent Particle Swarm Optimization technique with EPANET","year":2012,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"","keywords":"Particle swarm optimization; Mathematical optimization; Computer science; Scheduling (production processes); Mathematics","score_opus":0.025297769741008853,"score_gpt":0.22431771425399294,"score_spread":0.19901994451298408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983792072","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":[0.025554731,0.000116170435,0.97115535,0.00009674372,0.000035684374,0.00007317979,0.000052501076,0.00018452747,0.0027311293],"genre_scores_gemma":[0.4266582,0.00019344693,0.57028097,0.00005403071,0.000030662708,0.00032818678,0.00014383944,0.000048500297,0.0022620629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998776,0.00003981503,0.000006870716,0.000018656212,0.000045878023,0.000011162243],"domain_scores_gemma":[0.9997943,0.000116225165,0.000027670603,0.00001123243,0.000041402214,0.000009221781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042430378,0.0005449402,0.0004619021,0.00036902772,0.00030127628,0.00053691724,0.0004908583,0.0005653821,0.0012522235],"category_scores_gemma":[0.0009794029,0.00032921735,0.0004540962,0.00038708348,0.00023330351,0.000588666,0.00041004975,0.0004955265,0.00013570272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027999624,0.000022723407,0.0003696671,0.000038751074,0.000015476655,0.000021805672,0.000015969734,0.97386396,0.0011737152,0.0024020565,0.0003689761,0.02167893],"study_design_scores_gemma":[0.0000053800973,0.00001167435,0.00005706396,0.0000017042814,0.0000025440938,0.0000036255926,0.000002341973,0.9989656,0.00025263042,0.00037463455,0.00032105506,0.0000016943197],"about_ca_topic_score_codex":0.0058653164,"about_ca_topic_score_gemma":0.0074463952,"teacher_disagreement_score":0.0058653164,"about_ca_system_score_codex":0.00038204782,"about_ca_system_score_gemma":0.0009460849,"threshold_uncertainty_score":0.011662304},"labels":[],"label_agreement":null},{"id":"W1984040899","doi":"10.1016/s1462-0758(01)00033-4","title":"Comprehensive review of structural deterioration of water mains: statistical models","year":2001,"lang":"en","type":"article","venue":"Urban Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":553,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Mains electricity; Probabilistic logic; Statistical model; Computer science; Identification (biology); Random variate; Data mining; Probability distribution; Reliability engineering; Engineering; Machine learning; Artificial intelligence; Random variable; Statistics; Mathematics","score_opus":0.015407109488102892,"score_gpt":0.209762095646671,"score_spread":0.1943549861585681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984040899","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":[0.013937766,0.35869065,0.6064581,0.0027336497,0.00072287826,0.000063378786,0.0016443213,0.0005121877,0.015236969],"genre_scores_gemma":[0.25247967,0.64619136,0.08151222,0.00089615537,0.003878462,0.00022699904,0.0026325355,0.00041984973,0.011762733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999424,0.00016917268,0.00005277876,0.00011408968,0.00019382633,0.00004613369],"domain_scores_gemma":[0.99850106,0.00086890825,0.0001512181,0.000112622634,0.00033086483,0.000035298504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014354036,0.0013531923,0.00281312,0.0017338763,0.0004076106,0.0014557553,0.0024369922,0.0015101497,0.0019073235],"category_scores_gemma":[0.0036167847,0.00081460644,0.0014616715,0.004113245,0.000697912,0.0024018679,0.0009116226,0.0010740346,0.000930332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002851868,0.00008629028,0.0017744453,0.0014528078,0.00018769718,0.00009221776,0.000051279017,0.7530502,0.0005531293,0.071477495,0.018190227,0.15305567],"study_design_scores_gemma":[0.000010762113,0.00006553547,0.0017920587,0.0003179085,0.00016143978,0.00014663166,0.000056988778,0.82489604,0.0005175119,0.12048259,0.051493295,0.000059172206],"about_ca_topic_score_codex":0.009804048,"about_ca_topic_score_gemma":0.009106681,"teacher_disagreement_score":0.009804048,"about_ca_system_score_codex":0.0011844622,"about_ca_system_score_gemma":0.0016678959,"threshold_uncertainty_score":0.019493937},"labels":[],"label_agreement":null},{"id":"W1984476818","doi":"10.1002/aic.11639","title":"Impact of sampling method and scale on the measurement of mixing and the coefficient of variance","year":2008,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":33,"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":"Mixing (physics); Striation; Micromixer; Transect; Laminar flow; Turbulence; Sampling (signal processing); Dispersion (optics); Scale (ratio); Statistics; Reynolds number; Mechanics; Mathematics; Materials science; Optics; Physics; Geology","score_opus":0.03672552947090545,"score_gpt":0.254463440833744,"score_spread":0.21773791136283854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984476818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4697848,0.0009344868,0.52575547,0.00030364122,0.00010662646,0.00015027609,0.00027451574,0.0008915766,0.0017985557],"genre_scores_gemma":[0.9340383,0.00009941058,0.06524313,0.000054751454,0.00001868691,0.00008771296,0.00012638113,0.0001589824,0.00017274548],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9637096,0.026175382,0.001615347,0.0021796697,0.0058643506,0.0004557069],"domain_scores_gemma":[0.6779563,0.2885484,0.0064273113,0.01852717,0.008036767,0.00050404924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032877717,0.00092051044,0.000792148,0.0010784226,0.00052144413,0.0012053192,0.00076750194,0.0008632956,0.000494868],"category_scores_gemma":[0.16885383,0.00040269803,0.00079070835,0.001490808,0.0014701218,0.0011931321,0.0010241488,0.0009687601,0.00014115984],"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":[0.0029455859,0.00047970196,0.29857102,0.00074488786,0.001684411,0.00079185143,0.0012373104,0.2896091,0.09516303,0.012494527,0.0018625732,0.294416],"study_design_scores_gemma":[0.00015041197,0.001683321,0.1520047,0.00012249427,0.0003649125,0.00078851916,0.0003224575,0.71560425,0.118995175,0.0074060187,0.0022864612,0.00027127034],"about_ca_topic_score_codex":0.0027143091,"about_ca_topic_score_gemma":0.0019417292,"teacher_disagreement_score":0.032877717,"about_ca_system_score_codex":0.0005507575,"about_ca_system_score_gemma":0.0007513132,"threshold_uncertainty_score":0.17387605},"labels":[],"label_agreement":null},{"id":"W1984622512","doi":"10.1115/ipc2006-10007","title":"Large Pipeline Network Optimization: Summary and Conclusions of TransCanada Research Effort","year":2006,"lang":"en","type":"article","venue":"Volume 3: Materials and Joining; Pipeline Automation and Measurement; Risk and Reliability, Parts A and B","topic":"Water Systems and 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":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Optimization problem; Block (permutation group theory); Mathematical optimization; Range (aeronautics); Compressor station; Stochastic optimization; Gas compressor; Stability (learning theory); Set (abstract data type); Convergence (economics); Engineering; Algorithm","score_opus":0.016269275443705898,"score_gpt":0.22834282737528716,"score_spread":0.21207355193158126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984622512","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":[0.305944,0.18111667,0.24484177,0.015433129,0.0012829854,0.00028095927,0.00081003667,0.0009905336,0.24929997],"genre_scores_gemma":[0.65692973,0.10735674,0.18646513,0.0013536232,0.0007039016,0.00017558699,0.0017508377,0.0004671722,0.04479733],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999406,0.00016031008,0.000034257275,0.00012532763,0.00022609868,0.000047965],"domain_scores_gemma":[0.9988343,0.00045522884,0.0000421748,0.0001031813,0.0005063812,0.00005871406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001781144,0.0006316247,0.0006952497,0.00094955316,0.0003796112,0.001342544,0.00093036663,0.0005609291,0.0034035326],"category_scores_gemma":[0.0025551987,0.0001786867,0.00057944626,0.0014771052,0.00045963505,0.001000984,0.0007615627,0.0008012922,0.0005577623],"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":[0.0005453053,0.00040507538,0.0031546203,0.0016084344,0.00012961774,0.00041368132,0.00025793206,0.2935845,0.008224549,0.03601619,0.015722776,0.6399373],"study_design_scores_gemma":[0.0001880797,0.0011119867,0.010346526,0.001190133,0.00020069195,0.00060930796,0.00075658696,0.67702127,0.026668027,0.034612823,0.24714346,0.000151125],"about_ca_topic_score_codex":0.010162191,"about_ca_topic_score_gemma":0.008500457,"teacher_disagreement_score":0.010162191,"about_ca_system_score_codex":0.0016538777,"about_ca_system_score_gemma":0.0015133108,"threshold_uncertainty_score":0.020206094},"labels":[],"label_agreement":null},{"id":"W1986012101","doi":"10.1061/(asce)0887-3828(2009)23:1(40)","title":"Study of the Suitability of Existing Deterioration Models for Water Mains","year":2009,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Mains electricity; Electricity; Engineering; Water supply; Civil engineering; Categorization; Forensic engineering; Environmental science; Environmental engineering; Computer science; Electrical engineering","score_opus":0.03164256470716157,"score_gpt":0.2256373751275276,"score_spread":0.19399481042036604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986012101","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":[0.7358659,0.0010038434,0.25356132,0.0005922569,0.00009208127,0.0002573887,0.001582225,0.00085874856,0.0061861807],"genre_scores_gemma":[0.9830199,0.00024031366,0.015286188,0.000024058341,0.00001904653,0.00006886618,0.0006510467,0.00003545015,0.00065508217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875724,0.0005488435,0.00010321851,0.00022927104,0.00022326797,0.00013823008],"domain_scores_gemma":[0.9902388,0.0071829474,0.0008521664,0.0005123578,0.0010626563,0.00015103267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039029205,0.00087426644,0.00075354957,0.0014195072,0.00038499833,0.0010005992,0.0017474348,0.00096366525,0.0008666852],"category_scores_gemma":[0.014507629,0.0003099482,0.0012570592,0.0010961295,0.00045329565,0.0013630508,0.0005755775,0.0010576446,0.00026799896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009807058,0.000089666086,0.013766486,0.0000741025,0.00005573513,0.000038057457,0.0000810557,0.97359395,0.0003081033,0.0014035536,0.000308522,0.010182742],"study_design_scores_gemma":[0.0000050708663,0.00004232266,0.0018878147,0.000009195281,0.000010163387,0.000012813073,0.000036983973,0.99719703,0.00014957582,0.00048703863,0.00015644154,0.0000055416754],"about_ca_topic_score_codex":0.030240873,"about_ca_topic_score_gemma":0.017666295,"teacher_disagreement_score":0.030240873,"about_ca_system_score_codex":0.0020355433,"about_ca_system_score_gemma":0.0014053769,"threshold_uncertainty_score":0.060129702},"labels":[],"label_agreement":null},{"id":"W1986660428","doi":"10.1016/j.watres.2012.05.006","title":"Sensitivity analysis of some critical factors affecting simulated intrusion volumes during a low pressure transient event in a full-scale water distribution system","year":2012,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and 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":"Polytechnique Montréal; Natural Sciences and Engineering Research Council","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Body orifice; Intrusion; Leakage (economics); Transient (computer programming); Simulation; Mechanics; Environmental science; Engineering; Geology; Computer science; Physics; Mechanical engineering","score_opus":0.015322714070313869,"score_gpt":0.272908654188488,"score_spread":0.2575859401181741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986660428","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":[0.9911178,0.00006574369,0.0064788014,0.00012363584,0.00001884183,0.000044905533,0.0002214269,0.00008883591,0.0018400202],"genre_scores_gemma":[0.9994678,0.000009360692,0.00034821243,0.000008126446,0.0000012683128,0.000007734714,0.00003939511,0.0000058598166,0.00011218053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950564,0.0001945014,0.000023304034,0.00006943035,0.00007527457,0.00013172287],"domain_scores_gemma":[0.99441504,0.0048836987,0.00019688418,0.00012751843,0.00027991214,0.00009699611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013346552,0.0007959677,0.0007426615,0.000809796,0.00064401573,0.0009071917,0.00070919946,0.0013838427,0.0013640294],"category_scores_gemma":[0.0051023206,0.00062960916,0.001208232,0.0004918348,0.00079263054,0.0007437613,0.0006014741,0.00090786145,0.000063759486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018868607,0.000054390784,0.0022088583,0.00003504433,0.00004276594,0.0002010638,0.00002399691,0.9928306,0.003363263,0.00020671533,0.00009834389,0.0007462999],"study_design_scores_gemma":[0.000023215469,0.00016849386,0.0047438443,0.0000053740464,0.000046722795,0.00003764414,0.00006892444,0.98951054,0.0050866287,0.00021671384,0.00007215123,0.000019754756],"about_ca_topic_score_codex":0.014189025,"about_ca_topic_score_gemma":0.005765853,"teacher_disagreement_score":0.014189025,"about_ca_system_score_codex":0.0016166369,"about_ca_system_score_gemma":0.0006445484,"threshold_uncertainty_score":0.028212905},"labels":[],"label_agreement":null},{"id":"W1987865261","doi":"10.1115/fedsm2009-78300","title":"Experimental Investigation of the Two-Phase Phenomena in a Small Discharge Installed on a Large Pipe With Stratified Gas-Liquid Flow","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Entrainment (biomusicology); Mechanics; Stratified flow; Inlet; Two-phase flow; Materials science; Volumetric flow rate; Flow (mathematics); Flow visualization; Adiabatic process; Flow measurement; Air entrainment; Environmental science; Thermodynamics; Turbulence; Mechanical engineering; Engineering; Physics; Acoustics","score_opus":0.011892388117954861,"score_gpt":0.21301421194606185,"score_spread":0.20112182382810698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987865261","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":[0.9976043,0.000047915713,0.0019854726,0.00002338593,0.000005869914,0.00002326658,0.0000771427,0.000043290886,0.00018950454],"genre_scores_gemma":[0.99708766,0.000067086236,0.0024646167,0.000008940864,0.000006389638,0.000030801224,0.000059013088,0.000008288559,0.00026726077],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976915,0.00003805821,0.000012577009,0.000056705107,0.000071472205,0.000051978448],"domain_scores_gemma":[0.9994709,0.00024673124,0.000062961924,0.00005599501,0.000083202765,0.00008032826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004392781,0.0004185582,0.00073291536,0.00020931593,0.00056209235,0.00025354166,0.00049788004,0.0005535207,0.00091726147],"category_scores_gemma":[0.00063238275,0.00021398219,0.0002452993,0.00036109332,0.00066357624,0.0003995289,0.00059553504,0.00064282055,0.00013434583],"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":[0.00040889654,0.00022941358,0.0021691306,0.00013524409,0.000007810411,0.000337418,0.00023018502,0.0020653831,0.9917779,0.00015746117,0.00009696387,0.0023841285],"study_design_scores_gemma":[0.00016548653,0.006801931,0.020195488,0.000014609658,0.00003331369,0.00032190423,0.00028260087,0.025821641,0.9452306,0.00018009759,0.0009109658,0.000041184016],"about_ca_topic_score_codex":0.0006188036,"about_ca_topic_score_gemma":0.00052904635,"teacher_disagreement_score":0.00091726147,"about_ca_system_score_codex":0.0003136326,"about_ca_system_score_gemma":0.00032403524,"threshold_uncertainty_score":0.0030685067},"labels":[],"label_agreement":null},{"id":"W1989391057","doi":"10.1007/s10669-013-9464-3","title":"Water distribution system failure: a framework for forensic analysis","year":2013,"lang":"en","type":"article","venue":"Environment Systems & Decisions","topic":"Water Systems and 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":"Université Laval; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quality (philosophy); Reliability engineering; Computer science; Water quality; Risk analysis (engineering); Forensic engineering; Environmental science; Engineering; Business","score_opus":0.007960230015515048,"score_gpt":0.18068980571120943,"score_spread":0.1727295756956944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989391057","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":[0.0016353532,0.00023060317,0.99233717,0.0005931115,0.00004189707,0.00007549604,0.00014151026,0.00024240429,0.0047024763],"genre_scores_gemma":[0.24023144,0.0014612845,0.74747854,0.00035477974,0.00038540445,0.00040522064,0.0005894124,0.00023778657,0.008856097],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977906,0.0009102959,0.00016336326,0.00027522352,0.000671117,0.00018944089],"domain_scores_gemma":[0.9947877,0.0025506944,0.00057347317,0.0009135559,0.00094425486,0.00023031727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046454095,0.0015411165,0.001019128,0.0039903866,0.0017564093,0.0058980286,0.0034579835,0.0030931851,0.0050579268],"category_scores_gemma":[0.010890368,0.0005820728,0.0017108928,0.0020002984,0.004025519,0.005680472,0.004362751,0.0026192276,0.0010388473],"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":[0.000033505927,0.00005234812,0.00078243105,0.00011481894,0.000049064223,0.0004290266,0.00030299474,0.09211983,0.00084268,0.86357456,0.005340096,0.03635868],"study_design_scores_gemma":[0.00001536898,0.000033819288,0.0002725477,0.00014449288,0.00004309071,0.0003974204,0.0002128872,0.49554703,0.0009150233,0.4863415,0.016035227,0.00004161954],"about_ca_topic_score_codex":0.0062384973,"about_ca_topic_score_gemma":0.003971221,"teacher_disagreement_score":0.0062384973,"about_ca_system_score_codex":0.0017071464,"about_ca_system_score_gemma":0.0035927438,"threshold_uncertainty_score":0.024567544},"labels":[],"label_agreement":null},{"id":"W1991066010","doi":"10.5942/jawwa.2014.106.0144","title":"Survey of pressure management in water distribution systems","year":2014,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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":"American Water (Canada)","funders":"Water Research Foundation","keywords":"Environmental science; Pressure system; Sampling interval; Interval (graph theory); Pressure measurement; Statistics; Mathematics; Meteorology; Geography","score_opus":0.0039796564920522035,"score_gpt":0.17795211223458648,"score_spread":0.17397245574253428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991066010","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":[0.9965222,0.00014948586,0.00062087574,0.00015424633,0.0000033177316,0.00006248839,0.0005293445,0.000018022143,0.0019400842],"genre_scores_gemma":[0.99700314,0.00032684917,0.00092028134,0.00013996367,0.00000636579,0.000048433576,0.00054666906,0.0000032141472,0.001004964],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99895394,0.00034320672,0.00013514167,0.000116902964,0.00031919332,0.00013158478],"domain_scores_gemma":[0.99642146,0.0007109061,0.0012566554,0.0001228458,0.0011099903,0.0003781702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093415496,0.000114382325,0.00014895292,0.0010830794,0.00040007717,0.00045201412,0.00020426097,0.00025442842,0.0013768137],"category_scores_gemma":[0.00280741,0.00016100726,0.0001037117,0.0018023856,0.00016991777,0.00057147146,0.0003382236,0.00024140834,0.00028694462],"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":[0.00005437174,0.00023357167,0.94863373,0.00017404302,0.00002126533,0.00018897379,0.0028180396,0.000342257,0.0041028247,0.00013481027,0.0014248235,0.041871406],"study_design_scores_gemma":[0.0000028162935,0.00035969797,0.98659015,0.000033317756,0.0000065026256,0.00017579836,0.004302041,0.0006324019,0.0009370691,0.00003170652,0.0069175614,0.000010904066],"about_ca_topic_score_codex":0.0051700016,"about_ca_topic_score_gemma":0.0053663547,"teacher_disagreement_score":0.0051700016,"about_ca_system_score_codex":0.0005875522,"about_ca_system_score_gemma":0.00065950525,"threshold_uncertainty_score":0.010279775},"labels":[],"label_agreement":null},{"id":"W1991170241","doi":"10.1115/ipc2008-64106","title":"Modeling Multi-Product Pipeline Hydraulics With a Spreadsheet","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Optech (Canada)","funders":"","keywords":"Pipeline transport; Pipeline (software); Transient (computer programming); Throughput; Computer science; Hydraulics; Computation; Hydraulic machinery; Simulation; Steady state (chemistry); Engineering; Mechanical engineering; Algorithm","score_opus":0.02249507428317028,"score_gpt":0.18718177841084216,"score_spread":0.1646867041276719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991170241","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":[0.043377712,0.000096764576,0.93977094,0.00012588013,0.000037242226,0.00030671348,0.0010563476,0.0043637054,0.010864692],"genre_scores_gemma":[0.3980416,0.0005393393,0.57954955,0.000096512245,0.000023792645,0.0012387461,0.0016958583,0.00063064805,0.018183952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963737,0.000073041265,0.00003387673,0.00006387984,0.0001620516,0.00002976286],"domain_scores_gemma":[0.99868375,0.0007336717,0.00009723456,0.00012629408,0.0003256603,0.000033422246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010311621,0.0007147987,0.00055714016,0.0006432866,0.00044615436,0.0011194246,0.0019754355,0.0011072055,0.0073508974],"category_scores_gemma":[0.0019229401,0.0006203762,0.000633682,0.0007024642,0.00039311158,0.0014648499,0.0004554257,0.00079837267,0.0010430024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038073747,0.000053700274,0.00040042753,0.000060720762,0.000011966802,0.000034753102,0.00004918105,0.97926,0.00229575,0.0046082577,0.0007359783,0.0124511095],"study_design_scores_gemma":[0.000009908079,0.000018974202,0.00007749965,0.000007785155,0.0000051209854,0.000008842212,0.000004750205,0.994975,0.0020230766,0.0008429063,0.0020192887,0.0000069176135],"about_ca_topic_score_codex":0.00853467,"about_ca_topic_score_gemma":0.0070596263,"teacher_disagreement_score":0.00853467,"about_ca_system_score_codex":0.0010089197,"about_ca_system_score_gemma":0.0017779354,"threshold_uncertainty_score":0.024591208},"labels":[],"label_agreement":null},{"id":"W1991650832","doi":"10.1061/40745(146)73","title":"Modeling Pipe Deterioration using Soil Properties - An Application of Fuzzy Logic Expert System","year":2004,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Fuzzy logic; Expert system; Corrosion; Computer science; Environmental science; Geotechnical engineering; Data mining; Engineering; Artificial intelligence; Materials science; Metallurgy","score_opus":0.03393659929887642,"score_gpt":0.21507701161518752,"score_spread":0.18114041231631112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991650832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06665178,0.00010211082,0.9295303,0.00006655418,0.000010793494,0.00011110536,0.00009432924,0.00049626385,0.0029367474],"genre_scores_gemma":[0.83996516,0.00014916726,0.1578988,0.000028083778,0.000011460103,0.00019039995,0.00011500598,0.000018149276,0.0016237525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975294,0.00006557339,0.000023314004,0.00005758227,0.00007175078,0.000028850323],"domain_scores_gemma":[0.9995035,0.00028707602,0.000052872838,0.000020548636,0.00011873674,0.000017135235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064390834,0.00049341307,0.00058319204,0.0005897515,0.00042365774,0.0008505799,0.0007200251,0.0009697275,0.001248393],"category_scores_gemma":[0.0014430858,0.00035651357,0.0005823755,0.00035891007,0.0002391479,0.000556073,0.00041381243,0.0003753436,0.0002056673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028933528,0.000038474886,0.0011257255,0.000035526045,0.000018775425,0.00010962638,0.00005905846,0.97610646,0.003102133,0.0010854286,0.00011834282,0.018171426],"study_design_scores_gemma":[0.0000019310116,0.000008940291,0.000116168456,0.0000026653095,0.0000036776923,0.0000066849175,0.0000045394213,0.9991654,0.0003724834,0.00023407576,0.00008108697,0.0000022956715],"about_ca_topic_score_codex":0.012549185,"about_ca_topic_score_gemma":0.009395643,"teacher_disagreement_score":0.012549185,"about_ca_system_score_codex":0.00065464014,"about_ca_system_score_gemma":0.00083099667,"threshold_uncertainty_score":0.024952292},"labels":[],"label_agreement":null},{"id":"W1991738242","doi":"10.2166/wst.2012.568","title":"An approximate solution for two-dimensional groundwater infiltration in sewer systems","year":2012,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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":"","keywords":"Infiltration (HVAC); Groundwater; Aquifer; Hydraulic conductivity; Water table; Groundwater model; Hydraulic head; Geotechnical engineering; Environmental science; Hydrology (agriculture); Geology; Groundwater flow; Soil science; Materials science; Soil water","score_opus":0.011117417228688139,"score_gpt":0.22682651714060673,"score_spread":0.21570909991191858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991738242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12612373,0.00045837834,0.8664917,0.00027583353,0.000035690184,0.00004270737,0.00012404747,0.00016886905,0.006279068],"genre_scores_gemma":[0.9142083,0.00041794448,0.07979715,0.00007041274,0.000026446816,0.00014157142,0.000106683176,0.000049573122,0.0051818937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998722,0.000038696864,0.0000056206895,0.000021967227,0.00003760758,0.000023806502],"domain_scores_gemma":[0.99974304,0.00015905064,0.00002677486,0.000011409492,0.000047732352,0.000011884344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035893384,0.0003397807,0.000578937,0.0003492546,0.00027408075,0.00060430664,0.00046212494,0.0012600039,0.0011176057],"category_scores_gemma":[0.0014522583,0.00029060687,0.0003840886,0.0004096762,0.00057065854,0.0006410794,0.00040566328,0.00041936158,0.00016344195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001871925,0.0000102001895,0.0005332667,0.00004110692,0.0000068534664,0.00005677799,0.000035480272,0.9855972,0.0018127324,0.00590483,0.00016865895,0.0058142575],"study_design_scores_gemma":[0.0000021501228,0.000005047152,0.00007218961,0.0000019309064,0.0000010826385,0.0000048561737,0.0000069693683,0.998869,0.00013736557,0.0007618861,0.00013583941,0.000001674744],"about_ca_topic_score_codex":0.0066745025,"about_ca_topic_score_gemma":0.00480475,"teacher_disagreement_score":0.0066745025,"about_ca_system_score_codex":0.0005446756,"about_ca_system_score_gemma":0.0007124348,"threshold_uncertainty_score":0.013271272},"labels":[],"label_agreement":null},{"id":"W1991841268","doi":"10.1016/j.proeng.2014.11.200","title":"Water Distribution System Performance Metrics","year":2014,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Redundancy (engineering); Resilience (materials science); Range (aeronautics); Computer science; Efficient energy use; Metric (unit); Reliability engineering; Environmental science; Distributed computing; Engineering; Materials science","score_opus":0.0029870589023328316,"score_gpt":0.13137120589312357,"score_spread":0.12838414699079073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991841268","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":[0.8084783,0.0029228884,0.101181984,0.00053722906,0.00016681352,0.0008843491,0.014034258,0.0015996689,0.07019453],"genre_scores_gemma":[0.9853324,0.00041438942,0.0069225105,0.000026028876,0.000019697041,0.00014395632,0.0035060255,0.00009294536,0.0035419888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970042,0.0006287321,0.00023911158,0.00023986964,0.0015709335,0.00031706004],"domain_scores_gemma":[0.9956061,0.0015148817,0.0006172056,0.00026506884,0.001831548,0.00016519513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022508672,0.0011074581,0.0005885863,0.0029500774,0.00039549128,0.001400473,0.0005295529,0.0005442664,0.00419123],"category_scores_gemma":[0.006287065,0.00011300203,0.0003980897,0.0032953764,0.00033688295,0.0014015964,0.00066233374,0.000367819,0.0007774449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004946098,0.00035477162,0.046680927,0.000918412,0.00039625898,0.00033844216,0.0003080925,0.78084093,0.021794325,0.011517286,0.009499846,0.12685607],"study_design_scores_gemma":[0.000060181956,0.0033499168,0.12268173,0.00026475114,0.0003196644,0.0010295827,0.0012795212,0.73613465,0.06150259,0.012169009,0.0609665,0.00024186925],"about_ca_topic_score_codex":0.0030893525,"about_ca_topic_score_gemma":0.0022209266,"teacher_disagreement_score":0.00419123,"about_ca_system_score_codex":0.0018790157,"about_ca_system_score_gemma":0.00072449667,"threshold_uncertainty_score":0.014021039},"labels":[],"label_agreement":null},{"id":"W1992536820","doi":"10.1080/19443994.2014.980326","title":"Operation and maintenance of sewerage systems: present challenges and possible solutions—an Indian experience","year":2014,"lang":"en","type":"article","venue":"Desalination and Water Treatment","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"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":"Sewerage; Government (linguistics); Quarter (Canadian coin); Sewage; Population; Training (meteorology); Business; Environmental planning; Engineering; Resource (disambiguation); Natural resource; Operations management; Environmental economics; Environmental engineering; Economics; Geography; Political science; Computer science","score_opus":0.0209950386078368,"score_gpt":0.21533105443763736,"score_spread":0.19433601582980056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992536820","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":[0.93332016,0.008901625,0.0067983544,0.006465523,0.00015157081,0.000084556705,0.00022395972,0.00012449696,0.04392971],"genre_scores_gemma":[0.97420985,0.010613805,0.0030739796,0.00038361273,0.000089527166,0.000022962762,0.00012781842,0.00003302597,0.011445437],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99938595,0.00014459886,0.000053771917,0.00008378658,0.00019204305,0.0001398468],"domain_scores_gemma":[0.9989465,0.00031327494,0.00010306601,0.00011863075,0.00032634207,0.00019234931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010393234,0.00030302771,0.00023636738,0.0008347259,0.0015072785,0.0019510327,0.0013473115,0.000640621,0.0031209027],"category_scores_gemma":[0.0010814097,0.00018865835,0.00029914567,0.002035715,0.0012181748,0.0012850306,0.0010428483,0.0009989595,0.0004832738],"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":[0.000823965,0.0020064146,0.08062322,0.0011805366,0.00008899081,0.0032981054,0.037066218,0.0070532905,0.031412076,0.015038104,0.010162517,0.8112465],"study_design_scores_gemma":[0.00021669707,0.0038438176,0.43004817,0.0008125826,0.0005858599,0.011899637,0.101920776,0.010453242,0.03942687,0.010473636,0.38985312,0.00046567427],"about_ca_topic_score_codex":0.03182564,"about_ca_topic_score_gemma":0.04279276,"teacher_disagreement_score":0.03182564,"about_ca_system_score_codex":0.001655587,"about_ca_system_score_gemma":0.0031510692,"threshold_uncertainty_score":0.06328076},"labels":[],"label_agreement":null},{"id":"W1992633665","doi":"10.5006/1.3278187","title":"<i>Technical Note:</i> Conditions for Similarity of Mass-Transfer Coefficients and Fluid Shear Stresses Between the Rotating Cylinder Electrode and Pipe","year":2005,"lang":"en","type":"article","venue":"CORROSION","topic":"Water Systems and 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":"Emergent BioSolutions (Canada)","funders":"","keywords":"Mechanics; Cylinder; Mass transfer; Mass transfer coefficient; Shear stress; Materials science; Shear (geology); Rotation (mathematics); Similarity (geometry); Similarity solution; Physics; Composite material; Geometry; Mathematics","score_opus":0.010903204867243992,"score_gpt":0.2359579919609416,"score_spread":0.2250547870936976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992633665","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":[0.024958277,0.0027015891,0.85225785,0.034482848,0.01998387,0.00041198166,0.003073207,0.0030773615,0.059053015],"genre_scores_gemma":[0.4343641,0.0048749633,0.3954528,0.0070715654,0.010444356,0.0009102937,0.003589787,0.0015454264,0.14174676],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99774545,0.0006136121,0.00020471582,0.00032824278,0.0009863826,0.0001216266],"domain_scores_gemma":[0.9914323,0.0031782654,0.0007447164,0.0014729586,0.003077971,0.00009374944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024958742,0.0008363553,0.00071798055,0.00066802395,0.0011365694,0.0015886258,0.0017458777,0.0011512564,0.028410811],"category_scores_gemma":[0.017713891,0.00044554682,0.0008234677,0.00063189474,0.0016541745,0.0024059205,0.0013262307,0.0018624603,0.011252065],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043166766,0.0001490393,0.003446623,0.0014766892,0.00009206188,0.00336554,0.0007923185,0.018792622,0.13867943,0.38226253,0.35832995,0.09218155],"study_design_scores_gemma":[0.000065649736,0.00030628373,0.0057082376,0.00017126402,0.000083009625,0.005209259,0.0004053338,0.096420124,0.25581047,0.12463004,0.51095223,0.00023811535],"about_ca_topic_score_codex":0.0029923383,"about_ca_topic_score_gemma":0.004069154,"teacher_disagreement_score":0.028410811,"about_ca_system_score_codex":0.001120964,"about_ca_system_score_gemma":0.0011440412,"threshold_uncertainty_score":0.09504366},"labels":[],"label_agreement":null},{"id":"W1993052706","doi":"10.1115/pvp2003-2087","title":"Use of Redundant Sensors to Determine the Acoustic Transfer Matrix of a Pump","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Atomic Energy (Canada)","funders":"","keywords":"Centrifugal pump; Acoustics; Matrix (chemical analysis); SIGNAL (programming language); Frequency domain; Time domain; Control theory (sociology); Engineering; Computer science; Materials science; Mechanical engineering; Physics; Impeller","score_opus":0.020789354908293506,"score_gpt":0.2063237669257257,"score_spread":0.1855344120174322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993052706","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":[0.77011305,0.00017174866,0.22839208,0.00006131233,0.000033202472,0.00005547984,0.00005709473,0.0004890005,0.00062694674],"genre_scores_gemma":[0.9030583,0.000072295916,0.09627234,0.00002249796,0.000007949439,0.000043217282,0.000046641977,0.000026505688,0.0004501863],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991885,0.0001578322,0.0000451157,0.00017913477,0.00037703614,0.000052388306],"domain_scores_gemma":[0.99827015,0.0007227872,0.000238442,0.00028700219,0.00042204862,0.000059506958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010309018,0.0006154367,0.0004788748,0.00050800806,0.00023188333,0.00034926008,0.0005499104,0.00043842866,0.00066180975],"category_scores_gemma":[0.002971007,0.0003545296,0.00019794737,0.0002900563,0.00042371408,0.0008311777,0.0004685491,0.0004391345,0.00018298652],"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":[0.00015382029,0.000053554857,0.001206237,0.000041406605,0.000012216283,0.0000398934,0.00007592742,0.003010736,0.97759414,0.0002780103,0.000051106003,0.01748293],"study_design_scores_gemma":[0.00002079079,0.00066052866,0.0037328114,0.000005470833,0.00002947073,0.00016915718,0.000037268637,0.0665148,0.92818063,0.00019863313,0.00042301416,0.000027434238],"about_ca_topic_score_codex":0.00043525052,"about_ca_topic_score_gemma":0.0005392267,"teacher_disagreement_score":0.0010309018,"about_ca_system_score_codex":0.00019464325,"about_ca_system_score_gemma":0.00024508798,"threshold_uncertainty_score":0.005452037},"labels":[],"label_agreement":null},{"id":"W1994198993","doi":"10.1016/j.measurement.2012.05.032","title":"State of the art review of inspection technologies for condition assessment of water pipes","year":2012,"lang":"en","type":"article","venue":"Measurement","topic":"Water Systems and Optimization","field":"Engineering","cited_by":462,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"Battelle; U.S. Environmental Protection Agency","keywords":"Mains electricity; Structural health monitoring; Engineering; Emerging technologies; State (computer science); Construction engineering; Forensic engineering; Reliability engineering; Computer science; Civil engineering; Systems engineering; Risk analysis (engineering); Electrical engineering; Artificial intelligence","score_opus":0.027511329196753687,"score_gpt":0.24101795793841818,"score_spread":0.21350662874166448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994198993","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":[0.0020083461,0.97970927,0.014255784,0.00031313338,0.0002682178,0.000020578418,0.00012089557,0.000058890473,0.0032449155],"genre_scores_gemma":[0.017687283,0.96879655,0.010812416,0.0002083192,0.00070393534,0.000026638978,0.00027296634,0.000022719649,0.0014690132],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820197,0.00030038884,0.00023187249,0.0003934689,0.00079337496,0.00007887965],"domain_scores_gemma":[0.99470645,0.0031204864,0.00051248865,0.00019772326,0.0013826997,0.00008007155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022289231,0.0010613697,0.0018602604,0.0038351733,0.00034312136,0.0021840783,0.0019513643,0.0013058883,0.0030341952],"category_scores_gemma":[0.003738264,0.000640999,0.0009849599,0.004771251,0.0005535096,0.0019327244,0.0006639494,0.000638886,0.0013740734],"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":[0.00014804502,0.00008353447,0.0009868726,0.021790883,0.00016586186,0.000112511196,0.00006966545,0.0019923353,0.008104975,0.0028354127,0.008496161,0.95521367],"study_design_scores_gemma":[0.00003587191,0.00092437235,0.013760576,0.017568642,0.0016946414,0.0021946253,0.00038226464,0.014943106,0.03543665,0.008397047,0.90444005,0.0002223103],"about_ca_topic_score_codex":0.0018687361,"about_ca_topic_score_gemma":0.0021828567,"teacher_disagreement_score":0.0038351733,"about_ca_system_score_codex":0.0006699686,"about_ca_system_score_gemma":0.0015554522,"threshold_uncertainty_score":0.011787832},"labels":[],"label_agreement":null},{"id":"W1995761515","doi":"10.1061/(asce)0733-9496(2009)135:6(547)","title":"Fuzzy-Logic Modeling of Risk Assessment for a Small Drinking-Water Supply System","year":2009,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":true,"ca_institutions":"University of Guelph","funders":"Canada Research Chairs","keywords":"Fault tree analysis; Fuzzy logic; Risk analysis (engineering); Computer science; Fuzzy set; Set (abstract data type); Water supply; Reliability engineering; Sensitivity (control systems); Process (computing); Decision tree; Engineering; Data mining; Business; Artificial intelligence; Environmental engineering","score_opus":0.013432722550465724,"score_gpt":0.21459902309631865,"score_spread":0.2011663005458529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995761515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10840161,0.000414768,0.8669033,0.0005070114,0.000058893806,0.0001845079,0.0005253793,0.00024083421,0.022763759],"genre_scores_gemma":[0.93524384,0.00039492128,0.055426776,0.00005344059,0.000027221398,0.00020579885,0.00018276741,0.000029191657,0.008436134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995958,0.00015943967,0.000019449808,0.0000497016,0.00011259288,0.00006302381],"domain_scores_gemma":[0.99926263,0.00049074006,0.00007715901,0.000013411602,0.00011288936,0.000043049935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007847052,0.00092666905,0.0007465631,0.001036025,0.0009891414,0.0015821499,0.0012859115,0.0013585627,0.004658195],"category_scores_gemma":[0.0017234259,0.0004647822,0.0008603499,0.0010652265,0.0009444093,0.001268965,0.0007747421,0.000963093,0.0002982805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020068439,0.000014383895,0.00031624478,0.000019491332,0.000010803999,0.00007626901,0.000042503165,0.9861356,0.00025834396,0.010704389,0.00014125992,0.0022605264],"study_design_scores_gemma":[0.0000052656937,0.000013667761,0.0000837156,0.000003836837,0.0000062499944,0.000011110063,0.000019695244,0.9953296,0.00006260102,0.0042365775,0.00022304304,0.000004564907],"about_ca_topic_score_codex":0.04491804,"about_ca_topic_score_gemma":0.033873197,"teacher_disagreement_score":0.04491804,"about_ca_system_score_codex":0.0023900773,"about_ca_system_score_gemma":0.0020650632,"threshold_uncertainty_score":0.08931315},"labels":[],"label_agreement":null},{"id":"W1996283947","doi":"10.1139/s07-032","title":"Improving the evaluation of filtration robustness","year":2008,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Robustness (evolution); Turbidity; Computer science; Environmental science; Chemistry; Geology","score_opus":0.012296975384104958,"score_gpt":0.18006225425729422,"score_spread":0.16776527887318926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996283947","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":[0.13772771,0.00082637265,0.85840714,0.00009478692,0.00004372422,0.00010096908,0.00016106262,0.00073112396,0.0019070997],"genre_scores_gemma":[0.81343967,0.00036010286,0.18477233,0.00004769685,0.000050825834,0.00008442065,0.00037812776,0.00010743823,0.00075936934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953707,0.0013343375,0.0003675021,0.0006283045,0.0021151805,0.00018404507],"domain_scores_gemma":[0.9870105,0.0076601487,0.0017313116,0.0010752783,0.0023454442,0.0001774297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005753732,0.0012040656,0.0008746548,0.0025538101,0.00024445413,0.0012584458,0.0007383047,0.0010484633,0.00081937324],"category_scores_gemma":[0.021135174,0.00028465767,0.00083393964,0.0010750367,0.00056227646,0.0018745047,0.0011229791,0.0008177111,0.00023522774],"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":[0.0005370987,0.000208573,0.020091295,0.0005034362,0.00045167984,0.00014133794,0.00012580672,0.39310578,0.18031095,0.0064051626,0.00095946004,0.3971595],"study_design_scores_gemma":[0.000017569188,0.00058655377,0.016417623,0.000033882297,0.00009554967,0.00015377166,0.000030415866,0.8361785,0.14049947,0.0035662418,0.0022919767,0.000128361],"about_ca_topic_score_codex":0.0012566096,"about_ca_topic_score_gemma":0.00085917115,"teacher_disagreement_score":0.005753732,"about_ca_system_score_codex":0.0008688888,"about_ca_system_score_gemma":0.00060021394,"threshold_uncertainty_score":0.030428946},"labels":[],"label_agreement":null},{"id":"W1996663476","doi":"10.1139/s03-069","title":"Application of back-propagation neural network modeling for free residual chlorine, total trihalomethanes and trihalomethanes speciation","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey","keywords":"Chlorine; Trihalomethane; Residual; Environmental chemistry; Tributary; Chemistry; Chloramine; Environmental science; Water treatment; Environmental engineering; Computer science; Algorithm","score_opus":0.006940976681541805,"score_gpt":0.17681745310337277,"score_spread":0.16987647642183096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996663476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44136986,0.0005792722,0.5538959,0.00029444956,0.00008329389,0.000105502615,0.0002744912,0.0008207546,0.002576517],"genre_scores_gemma":[0.95486635,0.00019502651,0.043313507,0.000036380385,0.000011772175,0.00008417652,0.00015578346,0.000019422976,0.0013175362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980694,0.00006547876,0.0000146719585,0.00004322276,0.000048361144,0.000021327794],"domain_scores_gemma":[0.9991234,0.00058823376,0.000060748996,0.000032541753,0.00017879985,0.000016359729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009664706,0.00086893304,0.00034616631,0.00031483814,0.00018708306,0.00051847467,0.0005328902,0.0008254943,0.0006988142],"category_scores_gemma":[0.002520489,0.00027666838,0.00044445472,0.0002892516,0.0002147937,0.0006407217,0.00029741297,0.0007056335,0.00016667233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006701306,0.00005484291,0.0020787988,0.0000377297,0.000043116128,0.000021867512,0.000016409731,0.97427046,0.0027757937,0.0001856255,0.00010804839,0.02034027],"study_design_scores_gemma":[0.0000014281139,0.000017359142,0.00024154475,0.0000016030172,0.000003878645,0.0000019637018,0.0000017647186,0.998806,0.00081009546,0.00008372478,0.000028658817,0.0000020361379],"about_ca_topic_score_codex":0.018350242,"about_ca_topic_score_gemma":0.012464642,"teacher_disagreement_score":0.018350242,"about_ca_system_score_codex":0.0008115221,"about_ca_system_score_gemma":0.00057789125,"threshold_uncertainty_score":0.036486864},"labels":[],"label_agreement":null},{"id":"W1997419256","doi":"10.1061/(asce)wr.1943-5452.0000158","title":"Evaluating the Impact of Climate Change Mitigation Strategies on the Optimal Design and Expansion of the Amherstview, Ontario, Water Network: Canadian Case Study","year":2011,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada","keywords":"Greenhouse gas; Discounting; Climate change; Environmental science; Carbon price; Carbon tax; Energy consumption; Environmental economics; Natural resource economics; Environmental engineering; Economics; Engineering","score_opus":0.08005781922217048,"score_gpt":0.2751574910139664,"score_spread":0.19509967179179594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997419256","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":[0.98125,0.00021608597,0.0058498215,0.00028057178,0.000009958105,0.00026191352,0.00047606017,0.000054213437,0.011601457],"genre_scores_gemma":[0.9913031,0.00017988168,0.0065221377,0.000023858182,0.0000026110602,0.00006667267,0.0001814307,0.0000106406005,0.0017097318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992812,0.00018070568,0.000014984963,0.00006405384,0.00019751208,0.00026148735],"domain_scores_gemma":[0.9992513,0.00035343648,0.00006407386,0.000025897389,0.0002185904,0.000086701104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011492119,0.0008821835,0.0005189637,0.00078278995,0.0014702702,0.001296255,0.0012062714,0.00094184576,0.0014054955],"category_scores_gemma":[0.0022059232,0.00047938403,0.0006437764,0.0011363974,0.0011545632,0.0005601472,0.00047408493,0.0005715624,0.00007421843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109867695,0.000072220784,0.004827697,0.00007093984,0.00002406383,0.00020715997,0.000058920443,0.9850433,0.0012608732,0.0012782764,0.00045924916,0.006587478],"study_design_scores_gemma":[0.00020548601,0.00036884064,0.01642879,0.000025040867,0.000091785965,0.000052601983,0.0008944043,0.97405994,0.003876148,0.0009828709,0.002955503,0.00005860083],"about_ca_topic_score_codex":0.93197405,"about_ca_topic_score_gemma":0.95381516,"teacher_disagreement_score":0.06802595,"about_ca_system_score_codex":0.028641125,"about_ca_system_score_gemma":0.019523652,"threshold_uncertainty_score":0.20780689},"labels":[],"label_agreement":null},{"id":"W1998885828","doi":"10.1115/ipc2008-64065","title":"SmartBall™: A New Approach in Pipeline Leak Detection","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Pure North","funders":"","keywords":"Pipeline transport; Pipeline (software); Leak; Hazardous waste; Leak detection; Line (geometry); Computer science; Engineering; Waste management; Mechanical engineering","score_opus":0.016916114062112252,"score_gpt":0.1732986557679287,"score_spread":0.15638254170581645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998885828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061094966,0.0039905766,0.9687773,0.0015377938,0.0008687222,0.00016083375,0.00019937745,0.004264486,0.014091437],"genre_scores_gemma":[0.13789839,0.009218687,0.8076754,0.002316581,0.001073146,0.0003045521,0.0007369145,0.0006670328,0.04010922],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99684936,0.00032779854,0.00011965567,0.00078088837,0.0017198019,0.00020254315],"domain_scores_gemma":[0.9984237,0.00026692555,0.00016625393,0.00025977846,0.00077186665,0.0001114942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012789591,0.0015844473,0.0012250626,0.0036173745,0.0008407904,0.002350218,0.0024490766,0.0023517993,0.005185121],"category_scores_gemma":[0.00284361,0.00094920665,0.00119131,0.0026266437,0.0018566516,0.004920252,0.0026334082,0.0021985467,0.0044583054],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032798597,0.00014635491,0.0029865925,0.0005811872,0.00009434066,0.00038541295,0.00046103192,0.0068228957,0.08873938,0.046257485,0.019653806,0.83354354],"study_design_scores_gemma":[0.000099792254,0.00114966,0.005435482,0.0004700401,0.0002736143,0.0055352235,0.0008313536,0.29607794,0.11575557,0.068833224,0.5049424,0.0005956541],"about_ca_topic_score_codex":0.0026494132,"about_ca_topic_score_gemma":0.0020086772,"teacher_disagreement_score":0.005185121,"about_ca_system_score_codex":0.0013836932,"about_ca_system_score_gemma":0.0010262942,"threshold_uncertainty_score":0.017345965},"labels":[],"label_agreement":null},{"id":"W1999066085","doi":"10.1061/41114(371)421","title":"Using Life-Cycle Assessment to Evaluate the Environmental Impacts of Water Network Design and Optimization","year":2010,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Queen's University; University of Toronto","funders":"","keywords":"Life-cycle assessment; Sorting; Index (typography); Multi-objective optimization; Genetic algorithm; Production (economics); Computer science; Electricity; Capital cost; Energy (signal processing); Optimization problem; Environmental economics; Environmental science; Mathematical optimization; Engineering; Economics; Mathematics; Statistics","score_opus":0.015476561372423345,"score_gpt":0.2321686957193298,"score_spread":0.21669213434690646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999066085","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":[0.76597273,0.001413764,0.21313849,0.00025284293,0.000060154398,0.00063968624,0.0011089143,0.0003252164,0.017088128],"genre_scores_gemma":[0.9622851,0.0004162722,0.035015546,0.000032825068,0.0000052719834,0.000302546,0.0005790174,0.000035485053,0.0013278993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983771,0.00085967564,0.000062013445,0.00008348381,0.00051963126,0.00009809619],"domain_scores_gemma":[0.9969764,0.0018976177,0.00027889968,0.00017262812,0.00059374317,0.00008072357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036196369,0.0013537568,0.0006163427,0.0024841074,0.00041159612,0.0012239488,0.00063721975,0.00084408035,0.001527413],"category_scores_gemma":[0.0061381413,0.00037573668,0.0009092145,0.0014881975,0.00040550382,0.0016893147,0.00058039365,0.0005054691,0.00018983214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008974362,0.00009808182,0.0050346293,0.000071037335,0.00012655853,0.000047299094,0.000019790723,0.9709071,0.0016250323,0.0012438161,0.00015515994,0.020581719],"study_design_scores_gemma":[0.000010999404,0.0003019012,0.0023888352,0.000017422775,0.000045424043,0.000017127126,0.000032446755,0.9903224,0.004064115,0.0019825127,0.000795944,0.000020944866],"about_ca_topic_score_codex":0.008823644,"about_ca_topic_score_gemma":0.013092313,"teacher_disagreement_score":0.008823644,"about_ca_system_score_codex":0.002433434,"about_ca_system_score_gemma":0.0013069336,"threshold_uncertainty_score":0.019142747},"labels":[],"label_agreement":null},{"id":"W1999940827","doi":"10.1080/07011784.2013.774153","title":"Forecasting watermain failure using artificial neural network modelling","year":2013,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Water Systems and Optimization","field":"Engineering","cited_by":63,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Life expectancy; Humanities; Engineering; Geography; Cartography; Demography; Art; Sociology","score_opus":0.027207029981340832,"score_gpt":0.17281440972856713,"score_spread":0.1456073797472263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999940827","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":[0.9172432,0.0005566085,0.072859906,0.00042920184,0.000101477264,0.00010643453,0.0011747598,0.00047655878,0.0070518907],"genre_scores_gemma":[0.9946272,0.00012443407,0.0030451352,0.000015924643,0.00000929048,0.00003152,0.0004574311,0.0000069566095,0.0016822728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999778,0.00003932617,0.000019041681,0.000050790022,0.000060919665,0.000051962103],"domain_scores_gemma":[0.9992749,0.00039411103,0.000108415115,0.000017264198,0.00016898412,0.000036316076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051738316,0.00092573935,0.00056751014,0.0008197852,0.0003934739,0.0009343103,0.00081495894,0.0009810452,0.001311546],"category_scores_gemma":[0.0019927865,0.00038021238,0.00040287216,0.00095098634,0.0003282699,0.0005314709,0.00044629793,0.00068060495,0.0002346664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039209073,0.000036770343,0.005177102,0.000021249265,0.000017978447,0.00006070232,0.000013615555,0.98829633,0.00028603786,0.00012767565,0.00025213283,0.0056711305],"study_design_scores_gemma":[0.0000010478371,0.000003893202,0.00069957384,0.00000112331,0.00000128881,0.0000012447526,0.0000043086748,0.99916613,0.0000398462,0.00005162204,0.000028135559,0.000001749318],"about_ca_topic_score_codex":0.22967598,"about_ca_topic_score_gemma":0.18183385,"teacher_disagreement_score":0.22967598,"about_ca_system_score_codex":0.0019917341,"about_ca_system_score_gemma":0.0011769162,"threshold_uncertainty_score":0.4566782},"labels":[],"label_agreement":null},{"id":"W2000284000","doi":"10.1016/s0952-1976(00)00022-1","title":"An integrated expert system/operations research approach for the optimization of natural gas pipeline operations","year":2000,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Water Systems and 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":"University of Regina","funders":"","keywords":"Computer science; Pipeline (software); Expert system; Pipeline transport; Horsepower; Decision support system; Compressor station; Operations research; Natural gas; Systems engineering; Industrial engineering; Data mining; Artificial intelligence; Operating system","score_opus":0.03408718647637132,"score_gpt":0.2935022485723917,"score_spread":0.2594150620960204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000284000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008122975,0.00012132708,0.98755705,0.00012070686,0.0000229296,0.0000796153,0.000037627156,0.0003779088,0.003559846],"genre_scores_gemma":[0.35874775,0.00031939227,0.63661295,0.00020481966,0.00010933429,0.00039770894,0.00018233639,0.0000798081,0.0033458765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991523,0.0002992542,0.000071437236,0.00011030744,0.0003117618,0.000054909022],"domain_scores_gemma":[0.99872845,0.0007526193,0.00007229055,0.000056362693,0.00034607828,0.000044227098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016373168,0.0009391243,0.0011450578,0.0009880018,0.0003757012,0.0015098793,0.0013432231,0.001338386,0.00400527],"category_scores_gemma":[0.0035674213,0.0005873655,0.0007010771,0.0006878328,0.00040417258,0.0010878905,0.00097360497,0.0010633466,0.00044353947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008591201,0.00019703431,0.0003592314,0.000114953764,0.00010552302,0.000087690976,0.000054289027,0.86382455,0.0019860298,0.0065686093,0.001021701,0.12559444],"study_design_scores_gemma":[0.000019576692,0.000024382973,0.000104012535,0.0000066683247,0.000022755688,0.000012004644,0.000008180666,0.99610186,0.0003442363,0.00283069,0.0005207131,0.000004914401],"about_ca_topic_score_codex":0.0064684553,"about_ca_topic_score_gemma":0.007947666,"teacher_disagreement_score":0.0064684553,"about_ca_system_score_codex":0.0006668678,"about_ca_system_score_gemma":0.0018739786,"threshold_uncertainty_score":0.013399005},"labels":[],"label_agreement":null},{"id":"W2000548656","doi":"10.1061/9780784413692.088","title":"A Quasi-Two-Phase Flow Model for Calculating Filling in Pipelines","year":2014,"lang":"en","type":"article","venue":"Pipelines 2014","topic":"Water Systems and 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":"Water hammer; Mechanics; Flow (mathematics); Two-phase flow; Transient (computer programming); Transient flow; Pipe flow; Airflow; Materials science; Pipeline transport; Engineering; Environmental science; Surge; Mechanical engineering; Computer science; Physics; Electrical engineering; Turbulence","score_opus":0.019084508233771603,"score_gpt":0.26284216417386785,"score_spread":0.24375765594009624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000548656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027128765,0.000133456,0.9675386,0.0001499282,0.000048417838,0.000076902514,0.00023473756,0.0003711038,0.004318152],"genre_scores_gemma":[0.79043376,0.00041653102,0.19967104,0.00010692974,0.000036327485,0.00042792465,0.00035200454,0.00019412009,0.00836136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998902,0.000030988977,0.0000061250626,0.0000196426,0.000037774105,0.000015240969],"domain_scores_gemma":[0.999783,0.0001022293,0.00002928508,0.0000192482,0.000052500774,0.000013659135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027183228,0.00042968476,0.00042484773,0.00037645927,0.00038519417,0.0005982681,0.0010722122,0.0012684102,0.0022950592],"category_scores_gemma":[0.0008703152,0.00041867673,0.0006176605,0.0004099096,0.0005244836,0.0007845186,0.0004585281,0.0006475464,0.0003383939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010515732,0.000010630682,0.00019302595,0.000025623622,0.000003152365,0.00003082727,0.000018275581,0.99161524,0.001873767,0.003885202,0.0001486481,0.002185063],"study_design_scores_gemma":[0.000002004203,0.0000049111623,0.000027541966,8.4373784e-7,5.9787254e-7,0.0000040370273,0.0000017412056,0.99930894,0.000118539305,0.0002928819,0.00023652994,0.0000014643681],"about_ca_topic_score_codex":0.01430261,"about_ca_topic_score_gemma":0.007309484,"teacher_disagreement_score":0.01430261,"about_ca_system_score_codex":0.00067253126,"about_ca_system_score_gemma":0.0013772662,"threshold_uncertainty_score":0.028438747},"labels":[],"label_agreement":null},{"id":"W2001988860","doi":"10.1139/l06-001","title":"Discussion of \"Explicit solutions of the Manning equation for partially filled circular pipes\"","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"Sanitary sewer; Flow (mathematics); Turbulence; Flow conditions; Mathematics; Potential flow; Mechanics; Environmental science; Geometry; Physics; Environmental engineering","score_opus":0.011001237492604093,"score_gpt":0.16519237556780378,"score_spread":0.15419113807519969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001988860","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01193073,0.029866274,0.745962,0.023163162,0.010910142,0.00017947609,0.00049525645,0.000405244,0.1770877],"genre_scores_gemma":[0.3032322,0.048599884,0.3772154,0.015660286,0.017625703,0.0009773165,0.0008601304,0.0008980457,0.23493104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995246,0.00016752952,0.000024190336,0.00006832812,0.00017654747,0.000038798386],"domain_scores_gemma":[0.9994524,0.00026410827,0.00007771155,0.000037408077,0.00013751895,0.00003087219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011775689,0.0010548246,0.0007104229,0.00074528257,0.0007525539,0.001415382,0.0021124934,0.0035682267,0.010159021],"category_scores_gemma":[0.003884316,0.00047263695,0.0014953905,0.0008873347,0.0015380335,0.0027530575,0.0018799073,0.003123392,0.001738813],"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":[0.000010338253,0.000018743898,0.00015760848,0.00019404411,0.0000129070995,0.00025032437,0.00016790077,0.01427814,0.000748379,0.9523878,0.022622585,0.009151138],"study_design_scores_gemma":[0.000024381934,0.00005314646,0.00043879967,0.0002720594,0.000024362223,0.00053095905,0.000116887044,0.15673418,0.00083844323,0.5803816,0.26052457,0.000060700837],"about_ca_topic_score_codex":0.0027018148,"about_ca_topic_score_gemma":0.0013184142,"teacher_disagreement_score":0.010159021,"about_ca_system_score_codex":0.00086962676,"about_ca_system_score_gemma":0.0007546588,"threshold_uncertainty_score":0.033985317},"labels":[],"label_agreement":null},{"id":"W2002340805","doi":"10.1016/j.watres.2011.01.008","title":"Reply to comment on “Using Bayesian Statistics to Estimate the Coefficients Of a Two-Component Second-order Chlorine Bulk Decay Model for a Water Distribution System” by Huang, J.J., McBean E.A. Water Res. (2007)","year":2011,"lang":"en","type":"letter","venue":"Water Research","topic":"Water Systems and 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 Guelph","funders":"","keywords":"Relevance (law); Order (exchange); Potable water; Component (thermodynamics); Public opinion; Bayesian probability; Population; Distribution (mathematics); Statistics; Public information; Sociology; Econometrics; Data science; Computer science; Politics; Mathematics; Environmental science; Environmental engineering; Political science; Demography; Business; Internet privacy; Law; Thermodynamics; Physics","score_opus":0.04605228543583038,"score_gpt":0.3176721578705044,"score_spread":0.271619872434674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002340805","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021538555,0.00055473513,0.00023423466,0.9894777,0.008370662,0.0000111371,0.00017250855,0.000038575497,0.0009251257],"genre_scores_gemma":[0.0013465199,0.00018665646,0.00017578938,0.98977405,0.006223953,0.000027989618,0.000029445426,0.000018866147,0.0022167866],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952382,0.0012806639,0.0006761902,0.00072507444,0.0015141848,0.00056574476],"domain_scores_gemma":[0.98605865,0.009046856,0.0009499971,0.00033713065,0.0028347315,0.0007725661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067884275,0.0011846483,0.0015243022,0.00082632375,0.0039059678,0.0028713974,0.0028994128,0.06347752,0.005157335],"category_scores_gemma":[0.03576701,0.0011912612,0.0017515045,0.00091890205,0.0034091237,0.0034812798,0.0016343528,0.05022437,0.008686635],"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":[0.00005926919,0.00001542543,0.0003597806,0.000030445597,0.000012251867,0.0003240974,0.00009740593,0.00007121866,0.00014629713,0.0016510393,0.9945479,0.002684813],"study_design_scores_gemma":[0.00020828746,0.000118722084,0.004324258,0.0003940557,0.00006613344,0.0010547787,0.00064845564,0.0011441781,0.0009637692,0.017396566,0.973498,0.00018279336],"about_ca_topic_score_codex":0.020750107,"about_ca_topic_score_gemma":0.02797602,"teacher_disagreement_score":0.06347752,"about_ca_system_score_codex":0.0056113503,"about_ca_system_score_gemma":0.0045482465,"threshold_uncertainty_score":0.041258633},"labels":[],"label_agreement":null},{"id":"W2002608923","doi":"10.1061/(asce)wr.1943-5452.0000450","title":"Water Distribution System Rehabilitation under Climate Change Mitigation Scenarios in Canada","year":2014,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":true,"ca_institutions":"Bentley (Canada)","funders":"","keywords":"Greenhouse gas; Discounting; Carbon tax; Leakage (economics); Natural resource economics; Climate change; Rehabilitation; Carbon leakage; Environmental science; Business; Environmental economics; Environmental engineering; Economics; Emissions trading; Finance","score_opus":0.0059649672416895415,"score_gpt":0.17328087835345776,"score_spread":0.16731591111176822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002608923","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":[0.9864027,0.00021874238,0.001906881,0.0006643695,0.000012409333,0.0001156531,0.0013470872,0.00014153258,0.009190574],"genre_scores_gemma":[0.9949772,0.00013458649,0.0017031201,0.000040852,0.0000017067572,0.00001673194,0.0004974764,0.0000152948,0.0026131507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929464,0.000101464975,0.000014640323,0.00007384529,0.00015033424,0.00036503884],"domain_scores_gemma":[0.99932194,0.00014265072,0.000048566504,0.000023323897,0.00032104892,0.00014247207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007411312,0.00069650507,0.0005468133,0.0007847721,0.0017390548,0.0014179371,0.0009990642,0.00080229953,0.0017796279],"category_scores_gemma":[0.0017578447,0.0003886633,0.0006877191,0.0017629756,0.0009841842,0.0006538265,0.00057703315,0.00065501867,0.000102700695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001767401,0.00008336908,0.012310891,0.000038650276,0.00004476457,0.00019112002,0.0000651066,0.97636175,0.00088446395,0.0018885424,0.0016372942,0.0063174083],"study_design_scores_gemma":[0.00012409488,0.00011411427,0.028097596,0.000015253649,0.000066112145,0.000031380136,0.0007942352,0.9648497,0.0016060794,0.0011004712,0.0031438281,0.000057113237],"about_ca_topic_score_codex":0.9881464,"about_ca_topic_score_gemma":0.9905207,"teacher_disagreement_score":0.05465573,"about_ca_system_score_codex":0.05465573,"about_ca_system_score_gemma":0.024871605,"threshold_uncertainty_score":0.3965569},"labels":[],"label_agreement":null},{"id":"W2002613037","doi":"10.3126/njst.v14i1.8932","title":"Groundwater Extraction: Implications on Local Water Security of Peri-urban Area of Kathmandu Valley","year":2013,"lang":"en","type":"article","venue":"Nepal Journal of Science and Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"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":"International Development Research Centre","keywords":"Groundwater; Water table; Urbanization; Water resource management; Water security; Geography; Hydrology (agriculture); Environmental science; Water resources; Geology; Ecology","score_opus":0.0069082996135253405,"score_gpt":0.20118934712427491,"score_spread":0.19428104751074957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002613037","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":[0.9929074,0.00029833257,0.00020308323,0.0011837219,0.000004265188,0.000013998023,0.0000408334,0.0000024197775,0.005345917],"genre_scores_gemma":[0.9992149,0.00015747463,0.00012701658,0.0000463111,0.0000018962286,0.000006724349,0.0000103815855,5.9869825e-7,0.00043473998],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99972874,0.0001373234,0.000011908611,0.000020552601,0.000025866531,0.00007559081],"domain_scores_gemma":[0.99978215,0.000081259524,0.000055496424,0.000008987531,0.000030439212,0.000041669282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029753605,0.00007579749,0.00008373948,0.00029480163,0.0010790516,0.0010750132,0.00027493184,0.00032342138,0.0027426213],"category_scores_gemma":[0.0007884026,0.00007481027,0.000074768686,0.0006086012,0.0007128994,0.0006745015,0.0012699298,0.00020991551,0.00009065756],"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":[0.0004400818,0.00045057866,0.6138665,0.001189749,0.000103704275,0.021695748,0.13841605,0.004768302,0.025363872,0.059700772,0.005858286,0.12814638],"study_design_scores_gemma":[0.000026681837,0.00039636178,0.6007338,0.00044520188,0.00005432992,0.0034333952,0.3338122,0.0031792435,0.0026110054,0.013950847,0.041293476,0.00006350091],"about_ca_topic_score_codex":0.005379368,"about_ca_topic_score_gemma":0.015004886,"teacher_disagreement_score":0.005379368,"about_ca_system_score_codex":0.0007248054,"about_ca_system_score_gemma":0.0011147525,"threshold_uncertainty_score":0.010696113},"labels":[],"label_agreement":null},{"id":"W2003791375","doi":"10.1002/eqe.118","title":"Continuous ambient‐vibration monitoring of the arch dam of Mauvoisin","year":2001,"lang":"en","type":"article","venue":"Earthquake Engineering & Structural Dynamics","topic":"Water Systems and 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":"Université de Sherbrooke","funders":"","keywords":"Ambient vibration; Arch dam; Vibration; Arch; Environmental science; Hydrostatic equilibrium; Hydrostatic pressure; Structural engineering; Stiffening; Engineering; Subharmonic; Geotechnical engineering; Acoustics; Physics; Mechanics","score_opus":0.004595831090794546,"score_gpt":0.17952655856071256,"score_spread":0.17493072746991803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003791375","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":[0.999113,0.00000939904,0.0006542018,0.0000042212023,0.0000012794663,0.000004604074,0.000033196342,0.000021051655,0.00015900741],"genre_scores_gemma":[0.9993043,0.000006557444,0.0004941371,0.000001683659,0.0000011685956,0.0000055053583,0.000042713364,0.0000026071773,0.00014131636],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998184,0.000030361862,0.0000042843208,0.000042710155,0.00006852946,0.000035734574],"domain_scores_gemma":[0.999858,0.000034677294,0.000018898543,0.000015018456,0.00003823987,0.000035026722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024392492,0.00024952402,0.00032795867,0.0003396955,0.0003087193,0.00023647668,0.00031067064,0.00031485522,0.00048651616],"category_scores_gemma":[0.0003153028,0.00012319964,0.00011287259,0.00021084485,0.0003075014,0.0001507973,0.00029807948,0.00023728548,0.000102415994],"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":[0.0015040798,0.0004225094,0.0716966,0.00012078758,0.000061553095,0.0010068177,0.0017584373,0.011392436,0.86194354,0.00021927989,0.00037034144,0.049503632],"study_design_scores_gemma":[0.00015490505,0.00393682,0.7918843,0.000014374322,0.000063359854,0.0002825135,0.00078212674,0.030990085,0.16878255,0.00015132505,0.0028929962,0.000064716194],"about_ca_topic_score_codex":0.003532815,"about_ca_topic_score_gemma":0.0053808913,"teacher_disagreement_score":0.003532815,"about_ca_system_score_codex":0.00019870946,"about_ca_system_score_gemma":0.00020405912,"threshold_uncertainty_score":0.0070245266},"labels":[],"label_agreement":null},{"id":"W2003997789","doi":"10.2166/aqua.2011.031","title":"Diminishing marginal returns for sensor networks in a water distribution system","year":2011,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Copula (linguistics); Pareto principle; Warning system; Redundancy (engineering); Real-time computing; False positive paradox; Marginal distribution; Point (geometry); Generalized Pareto distribution; Event (particle physics); Early warning system; Econometrics; Data mining; Statistics; Mathematics; Telecommunications; Machine learning","score_opus":0.029554113826933653,"score_gpt":0.23928371663725012,"score_spread":0.20972960281031647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003997789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106573835,0.0009560047,0.882649,0.00087391946,0.00004261073,0.00012315874,0.000122010264,0.00018988297,0.0084694745],"genre_scores_gemma":[0.9268368,0.0011277196,0.06559734,0.00013527952,0.00003540634,0.00016804336,0.000077765064,0.000056425062,0.005965213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715865,0.0016692987,0.00006433345,0.0002539533,0.0004786461,0.00037514008],"domain_scores_gemma":[0.9881948,0.0098093655,0.00073169096,0.00038764524,0.0006289004,0.0002475006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007301976,0.0013200791,0.001145623,0.0011878862,0.0005935265,0.0018427339,0.0014635343,0.0009990019,0.002772456],"category_scores_gemma":[0.023478407,0.0005412782,0.0008238672,0.00094153883,0.002327624,0.0023857153,0.0014999898,0.0015665442,0.00029966002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090741596,0.000022185985,0.0007189356,0.000088214285,0.00003967561,0.00010235144,0.00008403573,0.94055283,0.0010360742,0.04793439,0.0002870093,0.009043598],"study_design_scores_gemma":[0.00001038238,0.000081055136,0.000451293,0.000016436896,0.000016694323,0.00004540827,0.00006593879,0.9622307,0.0006277569,0.035588816,0.0008507558,0.000014777097],"about_ca_topic_score_codex":0.004999791,"about_ca_topic_score_gemma":0.002928146,"teacher_disagreement_score":0.007301976,"about_ca_system_score_codex":0.0032849167,"about_ca_system_score_gemma":0.0013100435,"threshold_uncertainty_score":0.038616955},"labels":[],"label_agreement":null},{"id":"W2004751953","doi":"10.2166/ws.2009.020","title":"Prediction of watermain failure frequencies using multiple and Poisson regression","year":2009,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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 Guelph","funders":"","keywords":"Poisson regression; Poisson distribution; Regression analysis; Regression; Statistics; Econometrics; Linear regression; Mathematics; Computer science; Population","score_opus":0.009670190482314144,"score_gpt":0.19043886532460924,"score_spread":0.18076867484229509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004751953","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":[0.7956535,0.00024173078,0.20233826,0.00015831983,0.000042550902,0.000064989516,0.00039567752,0.0003414104,0.0007634984],"genre_scores_gemma":[0.9847548,0.00011261984,0.014196135,0.0000066989624,0.000014335427,0.000037221835,0.00030276334,0.000014286308,0.0005612785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985057,0.0005602132,0.00010102351,0.00029764645,0.00039239004,0.00014293003],"domain_scores_gemma":[0.9900624,0.0067262235,0.0015672875,0.0004910729,0.0009910574,0.00016195596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037426208,0.00071429147,0.0009780048,0.0018016822,0.00022650736,0.00052262616,0.0013165603,0.0007024415,0.0010787402],"category_scores_gemma":[0.011671274,0.0003522663,0.0011396033,0.0013288921,0.00030459792,0.0007518621,0.00041654138,0.0008172134,0.00028883354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012394182,0.00014249366,0.06753128,0.00006037769,0.00010375873,0.0001831743,0.000061554914,0.895554,0.0011029413,0.0008971673,0.0004213678,0.03381799],"study_design_scores_gemma":[0.0000037799396,0.000061163875,0.0067649125,0.0000035765775,0.000009971434,0.000027534694,0.00002401827,0.99207336,0.0004565073,0.00048555015,0.000079078825,0.000010459894],"about_ca_topic_score_codex":0.0075661726,"about_ca_topic_score_gemma":0.005631771,"teacher_disagreement_score":0.0075661726,"about_ca_system_score_codex":0.0006096552,"about_ca_system_score_gemma":0.00049838383,"threshold_uncertainty_score":0.019793093},"labels":[],"label_agreement":null},{"id":"W2005290938","doi":"10.2478/eces-2013-0028","title":"Effect of Development of the town of Przemysl on Operation of its Sewerage System","year":2013,"lang":"en","type":"article","venue":"Ecological Chemistry and Engineering S","topic":"Water Systems and 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":"Sewerage; Stormwater; Sewage; Storm Water Management Model; Drainage; Environmental science; Drainage system (geomorphology); Sanitary sewer; Quarter (Canadian coin); Water resource management; Civil engineering; Combined sewer; Plan (archaeology); Environmental engineering; Environmental planning; Engineering; Geography; Surface runoff","score_opus":0.002847935711636334,"score_gpt":0.1506352863111843,"score_spread":0.14778735059954798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005290938","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":[0.9982072,0.000034928027,0.00010869681,0.000028990753,0.0000029844211,0.0000062556364,0.000102010476,0.000010033039,0.0014988721],"genre_scores_gemma":[0.99949205,0.00003519555,0.00004714984,0.0000027878918,0.0000011380804,0.000002859606,0.000063085216,0.0000017609157,0.0003539038],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997621,0.00006664812,0.0000066790044,0.000034029785,0.000027941382,0.00010269513],"domain_scores_gemma":[0.99944013,0.00018143919,0.00008680784,0.00003417478,0.00008294123,0.00017443697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017336206,0.0002023238,0.00018345943,0.0003230756,0.0003269207,0.0006628737,0.00021326178,0.00018750077,0.0039159874],"category_scores_gemma":[0.0009758538,0.00012450939,0.00030843774,0.00034234548,0.00043064347,0.00019752166,0.00060269836,0.00027594814,0.00020478222],"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":[0.0045327456,0.0006631967,0.74102634,0.00036946338,0.00044091718,0.00943425,0.001605982,0.091528624,0.0952013,0.004354383,0.0027344436,0.048108384],"study_design_scores_gemma":[0.000049803315,0.00080448616,0.9754925,0.000019532743,0.00010808142,0.00047069378,0.0021527421,0.0061369296,0.011375246,0.00018562362,0.0031834994,0.00002082516],"about_ca_topic_score_codex":0.016078219,"about_ca_topic_score_gemma":0.02287947,"teacher_disagreement_score":0.016078219,"about_ca_system_score_codex":0.0010230532,"about_ca_system_score_gemma":0.0008543855,"threshold_uncertainty_score":0.03196931},"labels":[],"label_agreement":null},{"id":"W2007338884","doi":"10.1061/(asce)1076-0342(2010)16:1(58)","title":"Methodology for Bayesian Belief Network Development to Facilitate Compliance with Water Quality Regulations","year":2010,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and 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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Water Network","keywords":"Bayesian network; Decision support system; Quality (philosophy); Expert system; Risk analysis (engineering); Engineering; Computer science; Data mining; Artificial intelligence; Business","score_opus":0.06193563783537241,"score_gpt":0.2759375267648691,"score_spread":0.21400188892949668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007338884","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":[0.0008540204,0.000026959782,0.99804085,0.00008722541,0.00000609371,0.00012641166,0.000038985443,0.000114329174,0.00070515904],"genre_scores_gemma":[0.04420066,0.00009497596,0.9540074,0.00006141643,0.000018334711,0.0009101651,0.0001849294,0.000038734048,0.00048342056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9909524,0.005866907,0.0006255871,0.000745281,0.001561785,0.00024803766],"domain_scores_gemma":[0.97052944,0.024204705,0.0012654389,0.0005368481,0.0031724917,0.0002910734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014345139,0.0014147554,0.0011730175,0.0033089642,0.0010876154,0.0022031676,0.0027687526,0.0016133784,0.0055810898],"category_scores_gemma":[0.044966772,0.0010740524,0.0015013316,0.0018582641,0.0011457502,0.002164773,0.0027316695,0.0026462914,0.000786768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111145935,0.00015129765,0.0015021627,0.00042650965,0.00018705317,0.00021785947,0.0006819887,0.7017875,0.0012490778,0.1181472,0.0019629505,0.17357531],"study_design_scores_gemma":[0.000033526856,0.000033816792,0.000116816635,0.00006977475,0.00003216145,0.000032434815,0.00006222103,0.950782,0.0008284464,0.045637105,0.0023538382,0.00001787226],"about_ca_topic_score_codex":0.006702311,"about_ca_topic_score_gemma":0.007387344,"teacher_disagreement_score":0.014345139,"about_ca_system_score_codex":0.0026226481,"about_ca_system_score_gemma":0.004310558,"threshold_uncertainty_score":0.07586527},"labels":[],"label_agreement":null},{"id":"W2007473159","doi":"10.1061/(asce)cf.1943-5509.0000011","title":"Condition Assessment of Water Treatment Plant Components","year":2009,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Water Systems and 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":"Concordia University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Australian Government","keywords":"Index (typography); Water resources; Water supply; Water quality; Quality (philosophy); Environmental science; Potable water; Engineering; Civil engineering; Environmental engineering; Water resource management; Computer science","score_opus":0.010161024874182207,"score_gpt":0.2065407646871926,"score_spread":0.19637973981301038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007473159","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":[0.6948383,0.00016168958,0.28209674,0.00017805997,0.000031888856,0.00035245524,0.0014155413,0.0006259185,0.020299478],"genre_scores_gemma":[0.99029166,0.00005861388,0.0071268454,0.000009509078,0.000003034068,0.0000731011,0.00036482676,0.000015242094,0.0020571514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991167,0.00015587795,0.00004872018,0.00021893294,0.00033443575,0.00012527977],"domain_scores_gemma":[0.9988061,0.0004531397,0.00022522073,0.000088876644,0.00038492234,0.000041672854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010791457,0.00074372377,0.00042177248,0.0009341678,0.00038236077,0.0016482233,0.0007580666,0.00083875714,0.003223103],"category_scores_gemma":[0.0029876556,0.00027112954,0.00091494713,0.00080573454,0.00046184685,0.0015042359,0.00073662645,0.0005957829,0.00044749753],"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":[0.00010088259,0.000097306576,0.014122208,0.000055677392,0.000030171808,0.00007127281,0.00009052014,0.95298654,0.004164999,0.0022318466,0.00051072205,0.025537785],"study_design_scores_gemma":[0.000004502519,0.00007994602,0.008516868,0.000008482826,0.000015912401,0.000027265869,0.00008675079,0.9871171,0.0023023349,0.001279797,0.00054671656,0.000014380522],"about_ca_topic_score_codex":0.014127262,"about_ca_topic_score_gemma":0.008952365,"teacher_disagreement_score":0.014127262,"about_ca_system_score_codex":0.0022728853,"about_ca_system_score_gemma":0.0010913751,"threshold_uncertainty_score":0.02809006},"labels":[],"label_agreement":null},{"id":"W2008058234","doi":"10.1090/s0033-569x-2011-01258-9","title":"Multiple scales analysis of water hammer attenuation","year":2011,"lang":"en","type":"article","venue":"Quarterly of Applied Mathematics","topic":"Water Systems and 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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Water hammer; Attenuation; Hammer; Generalization; Mechanics; Acoustics; Mathematical analysis; Mathematics; Geotechnical engineering; Physics; Geology; Structural engineering; Optics; Engineering","score_opus":0.015926227785396433,"score_gpt":0.18602228831422093,"score_spread":0.1700960605288245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008058234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055943325,0.0003186781,0.9378364,0.00014506608,0.000081874765,0.000022255814,0.000022856764,0.0001737698,0.0054558353],"genre_scores_gemma":[0.93910897,0.00051655865,0.052962787,0.00004428486,0.0000926272,0.0000401688,0.00002771958,0.000110653,0.007096253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999814,0.000036990183,0.0000059672884,0.00002635529,0.000092920796,0.0000238209],"domain_scores_gemma":[0.99954236,0.00018112942,0.00008291372,0.000050649887,0.000108825705,0.0000341815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035704774,0.0004831996,0.00034926308,0.0005567131,0.00021636348,0.00046491693,0.00037254303,0.0003318147,0.0016265498],"category_scores_gemma":[0.0016956192,0.0001966394,0.000444009,0.00029568383,0.0007082637,0.00084849744,0.0005740607,0.00096083473,0.00024639253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012258929,0.000057576246,0.0018157195,0.00017429999,0.0000588659,0.0005030719,0.0002496962,0.544591,0.07476749,0.30582747,0.002109353,0.06972281],"study_design_scores_gemma":[0.0000030643596,0.000024185094,0.00036481384,0.000004109997,0.0000049976916,0.000026851487,0.000011545046,0.98838276,0.0020949407,0.008294027,0.0007812258,0.0000075266325],"about_ca_topic_score_codex":0.0012643954,"about_ca_topic_score_gemma":0.00092274044,"teacher_disagreement_score":0.0016265498,"about_ca_system_score_codex":0.00049144984,"about_ca_system_score_gemma":0.00033554123,"threshold_uncertainty_score":0.0054413676},"labels":[],"label_agreement":null},{"id":"W2008282970","doi":"10.1504/ijnest.2011.045099","title":"Reliability analysis of digital feedwater regulating valve controller system using a semi-Markov process model","year":2011,"lang":"en","type":"article","venue":"International Journal of Nuclear Energy Science and Technology","topic":"Water Systems and 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 Waterloo","funders":"","keywords":"Boiler feedwater; Reliability (semiconductor); Process (computing); Reliability engineering; Computer science; Control valves; Markov process; Control theory (sociology); Controller (irrigation); Markov chain; Safety valve; Control (management); Control engineering; Boiler (water heating); Power (physics); Mathematics; Mechanical engineering; Statistics; Thermodynamics","score_opus":0.009475243374798478,"score_gpt":0.20815046520768685,"score_spread":0.19867522183288838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008282970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11517745,0.000890549,0.878142,0.00034766973,0.000037324764,0.00009213728,0.00024553193,0.00037496033,0.0046924204],"genre_scores_gemma":[0.99007225,0.00042804083,0.0071304874,0.00002386374,0.000021072776,0.00009323636,0.00013602927,0.000020992542,0.002073994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992347,0.00020464302,0.000041075036,0.00012054907,0.00023760521,0.00016136233],"domain_scores_gemma":[0.9977223,0.0014907565,0.00029493804,0.00006749753,0.00036867888,0.00005568194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013917199,0.00075041014,0.0011951734,0.0007784272,0.0004037632,0.00092780293,0.0010040738,0.00083010836,0.0022503715],"category_scores_gemma":[0.002281561,0.0005469686,0.0012477296,0.00044249342,0.00078988593,0.00081543624,0.0005987467,0.0009746779,0.00024760465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005108396,0.000015770955,0.00060180854,0.000045680055,0.000031541716,0.000109670356,0.000051784013,0.9875113,0.001414276,0.0073545966,0.0001514892,0.0026609735],"study_design_scores_gemma":[0.000002968495,0.000011414925,0.00013912315,0.0000028022446,0.000007849591,0.000008286505,0.0000030926044,0.99878746,0.00014174836,0.0008545309,0.00003723272,0.0000034926456],"about_ca_topic_score_codex":0.01744241,"about_ca_topic_score_gemma":0.008568824,"teacher_disagreement_score":0.01744241,"about_ca_system_score_codex":0.0013585046,"about_ca_system_score_gemma":0.0015924695,"threshold_uncertainty_score":0.034681737},"labels":[],"label_agreement":null},{"id":"W2009947569","doi":"10.5942/jawwa.2013.105.0001","title":"Contaminant intrusion in water distribution systems: An ingress model","year":2013,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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 British Columbia, Okanagan Campus; University of British Columbia","funders":"University of British Columbia","keywords":"Intrusion; Dimensionless quantity; Geotechnical engineering; Factorial experiment; Environmental science; Pipe network analysis; Geology; Soil science; Mathematics; Statistics; Mechanics","score_opus":0.0034823618960714087,"score_gpt":0.17525491521086678,"score_spread":0.17177255331479538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009947569","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":[0.53816885,0.0009981483,0.40066352,0.0016508218,0.00016168397,0.00035079205,0.0015451133,0.0008763719,0.055584732],"genre_scores_gemma":[0.9606515,0.00043764108,0.008116407,0.00010166027,0.000034629153,0.00023711949,0.00029935688,0.00007714009,0.03004466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965584,0.000077239,0.00001509643,0.000081982784,0.000079882346,0.000089980975],"domain_scores_gemma":[0.99948114,0.00024303849,0.00009369039,0.000019558987,0.00011224292,0.000050421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005458955,0.0011299148,0.0011303067,0.000742429,0.00089960586,0.0014789603,0.0020410856,0.0024365275,0.0041361437],"category_scores_gemma":[0.001504112,0.00073144515,0.0010733012,0.0007391242,0.00096983416,0.0013838613,0.0013032902,0.0014141579,0.00065686373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044268232,0.000033147095,0.0004514137,0.000025849618,0.000009844644,0.000084845706,0.00004160623,0.9934362,0.0009812027,0.0031873723,0.00020348052,0.0015006396],"study_design_scores_gemma":[0.000010394236,0.000021618847,0.000087179236,0.0000024269607,0.000007851895,0.0000066676876,0.000010580035,0.9990195,0.00015280585,0.00042099613,0.0002559912,0.0000038263724],"about_ca_topic_score_codex":0.03238588,"about_ca_topic_score_gemma":0.010789564,"teacher_disagreement_score":0.03238588,"about_ca_system_score_codex":0.0015650786,"about_ca_system_score_gemma":0.00144903,"threshold_uncertainty_score":0.06439477},"labels":[],"label_agreement":null},{"id":"W2010177699","doi":"10.1139/s05-023","title":"Computational fluid dynamics application in modeling and improving the performance of a storage reservoir used as a contact chamber for microorganism inactivation","year":2006,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Baffle; Computational fluid dynamics; Inlet; TRACER; Storage tank; Mechanics; Hydraulics; Environmental science; Fluid dynamics; Marine engineering; Petroleum engineering; Mechanical engineering; Geology; Engineering; Physics; Aerospace engineering","score_opus":0.003105882845880101,"score_gpt":0.1616296625774396,"score_spread":0.1585237797315595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010177699","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":[0.6383909,0.00043782598,0.3575957,0.00025358362,0.000053615968,0.000105085644,0.00016490612,0.00045304725,0.00254542],"genre_scores_gemma":[0.95600027,0.00019337275,0.042894606,0.000012322394,0.0000046169025,0.00006753837,0.00007357781,0.000020134497,0.0007334872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998783,0.00003812565,0.000008846047,0.000019488469,0.000042406642,0.000012884274],"domain_scores_gemma":[0.999666,0.00021941522,0.000026060387,0.000027993052,0.000049043527,0.000011383485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003560849,0.00048100099,0.00034333536,0.00023060279,0.0002960655,0.0005388188,0.00038171996,0.000615576,0.00059465726],"category_scores_gemma":[0.0010517256,0.00024785314,0.0004430408,0.00018244541,0.00024404915,0.0005613851,0.00024735832,0.0003617921,0.00010485679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012416118,0.000081845974,0.0022899937,0.00007166734,0.000017682078,0.00008031224,0.00007033361,0.9223208,0.061952826,0.0012480681,0.000103138394,0.011639273],"study_design_scores_gemma":[0.0000066381485,0.00004792589,0.00030204942,0.000002632746,0.000005498953,0.000012735202,0.0000069795533,0.9788947,0.02027881,0.00010695461,0.00032935088,0.0000057739276],"about_ca_topic_score_codex":0.0057194172,"about_ca_topic_score_gemma":0.003522143,"teacher_disagreement_score":0.0057194172,"about_ca_system_score_codex":0.0005353584,"about_ca_system_score_gemma":0.00093759724,"threshold_uncertainty_score":0.011372268},"labels":[],"label_agreement":null},{"id":"W2012402812","doi":"10.1088/1755-1315/12/1/012045","title":"Steady and unsteady flow computation in an elbow dr aft tube with experimental validation","year":2010,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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; TransCanada (Canada)","funders":"National Science Council; Kommission für Technologie und Innovation","keywords":"Draft tube; Computation; Reynolds-averaged Navier–Stokes equations; Steady state (chemistry); Diffuser (optics); Flow (mathematics); Turbulence; Computational fluid dynamics; Range (aeronautics); Hull; Mechanics; Computer science; Francis turbine; Simulation; Turbine; Engineering; Mechanical engineering; Marine engineering; Algorithm; Aerospace engineering; Physics","score_opus":0.00850364176211549,"score_gpt":0.19216426013239468,"score_spread":0.1836606183702792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012402812","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":[0.9847765,0.000048629925,0.013387888,0.00003693961,0.00000924926,0.000040494924,0.00018556493,0.00028479364,0.0012298136],"genre_scores_gemma":[0.9924132,0.00003398778,0.0068885637,0.0000048035126,0.000001623761,0.000029635397,0.00013937234,0.000018129771,0.00047063408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995326,0.00008838609,0.000041682964,0.000055934204,0.0002235114,0.00005783702],"domain_scores_gemma":[0.99889,0.00046602578,0.00012264708,0.00015792473,0.00031485208,0.00004863485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011166837,0.000537003,0.00066117925,0.0006736427,0.00055207073,0.0006756146,0.00079912733,0.0007952902,0.001051703],"category_scores_gemma":[0.002475585,0.0002610265,0.00039181722,0.00062921445,0.000871935,0.0008257449,0.00051780447,0.0005441199,0.00017534637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012268897,0.0010496461,0.022663811,0.00028000813,0.00002979976,0.00057649164,0.0008329759,0.72107357,0.19759744,0.0031690446,0.00081047456,0.05068989],"study_design_scores_gemma":[0.000040285777,0.0011953973,0.008103515,0.000016292439,0.000010759232,0.0000850385,0.00016108318,0.8880501,0.1014523,0.00022970689,0.0006250646,0.000030545154],"about_ca_topic_score_codex":0.0045158956,"about_ca_topic_score_gemma":0.003117294,"teacher_disagreement_score":0.0045158956,"about_ca_system_score_codex":0.0006510076,"about_ca_system_score_gemma":0.0005728899,"threshold_uncertainty_score":0.008979201},"labels":[],"label_agreement":null},{"id":"W2013650127","doi":"10.1016/j.eswa.2010.07.004","title":"Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water Systems and Optimization","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Program for New Century Excellent Talents in University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Department of Science and Technology, Ministry of Science and Technology, India; Shanghai Rising-Star Program; Natural Science Foundation of Chongqing; National Science Foundation","keywords":"Computer science; Flexibility (engineering); Intrusion; Similarity (geometry); Data mining; Process (computing); Intrusion detection system; Mathematical optimization; Artificial intelligence; Statistics; Mathematics","score_opus":0.007357480738357826,"score_gpt":0.2226710859223034,"score_spread":0.21531360518394557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013650127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056633665,0.000107631,0.99328107,0.000072982446,0.000029987948,0.000028707907,0.0000323556,0.00012291093,0.0006609818],"genre_scores_gemma":[0.46062002,0.0005161585,0.5326522,0.00023098009,0.00022717635,0.0002913338,0.00029641268,0.00024187467,0.004923888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948645,0.00014918158,0.000042605272,0.000105644336,0.00018710598,0.000028993652],"domain_scores_gemma":[0.99880266,0.0005367503,0.00014529718,0.00010333066,0.00033639942,0.000075614626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010956941,0.0007279071,0.0013862633,0.0012565264,0.0004211441,0.0009642357,0.0018754633,0.0016627476,0.0016711202],"category_scores_gemma":[0.0031256508,0.0005428528,0.0011028012,0.0012476988,0.00065323757,0.0018838546,0.0011267703,0.00095742254,0.0004373465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004716344,0.000041298834,0.0005012774,0.000058615078,0.000056561654,0.000040493473,0.00002561922,0.94676,0.002109179,0.005859429,0.000495964,0.04400437],"study_design_scores_gemma":[0.0000023469079,0.000004764485,0.000016514678,8.154361e-7,0.000003138366,0.000004498272,9.047445e-7,0.99924314,0.00013953923,0.00046083596,0.000122037425,0.0000015381507],"about_ca_topic_score_codex":0.006397183,"about_ca_topic_score_gemma":0.0032334188,"teacher_disagreement_score":0.006397183,"about_ca_system_score_codex":0.0007328228,"about_ca_system_score_gemma":0.0011444401,"threshold_uncertainty_score":0.012719929},"labels":[],"label_agreement":null},{"id":"W2014067250","doi":"10.1139/l02-058","title":"Nouvelle approche pour déterminer la distribution des temps de séjour dans les réservoirs d'eau potable en régime non permanent","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"Residence time distribution; TRACER; Water flow; Flow (mathematics); Environmental science; Environmental engineering; Physics; Mechanics","score_opus":0.006950961017048244,"score_gpt":0.16523889745555234,"score_spread":0.15828793643850408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014067250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008133701,0.00023534779,0.99083203,0.00010312516,0.000029449251,0.000030260304,0.00004778618,0.00021048932,0.00037768087],"genre_scores_gemma":[0.27991813,0.0010684675,0.7126217,0.0001290908,0.000117301315,0.00033319232,0.0002550429,0.00013413605,0.0054229395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910307,0.00022312428,0.00003883717,0.0002768648,0.00032145524,0.00003674232],"domain_scores_gemma":[0.99875987,0.00071614597,0.000114147246,0.00010730712,0.00027292766,0.000029641456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010672453,0.0015478411,0.0012687201,0.0012467282,0.00036085176,0.0014306369,0.0017043225,0.002294998,0.0017745087],"category_scores_gemma":[0.0037467144,0.0007605691,0.0010733488,0.00072702504,0.0009250397,0.001533552,0.0005060727,0.0015585946,0.0009101025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032563164,0.00031434806,0.006187354,0.00058828114,0.00028805184,0.00019715627,0.00034081342,0.48720002,0.11248654,0.010661183,0.0014631158,0.37994754],"study_design_scores_gemma":[0.00003379801,0.00015268581,0.001820316,0.000024719797,0.000059338854,0.00016410551,0.00004120475,0.97260207,0.018889897,0.003631209,0.0025126243,0.00006805602],"about_ca_topic_score_codex":0.0034187506,"about_ca_topic_score_gemma":0.0048832246,"teacher_disagreement_score":0.0034187506,"about_ca_system_score_codex":0.0008162884,"about_ca_system_score_gemma":0.0008972641,"threshold_uncertainty_score":0.0067976713},"labels":[],"label_agreement":null},{"id":"W2014144590","doi":"10.1016/s0262-1762(05)70634-9","title":"Low flow does not have to mean low reliability","year":2005,"lang":"en","type":"article","venue":"World Pumps","topic":"Water Systems and 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":"Reliability (semiconductor); Flow (mathematics); Reliability engineering; Range (aeronautics); Engineering; Computer science; Mechanics; Physics; Thermodynamics; Aerospace engineering","score_opus":0.005473338207631119,"score_gpt":0.1912289990475347,"score_spread":0.18575566083990358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014144590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10001684,0.024332305,0.33088404,0.22239263,0.01234884,0.00016167828,0.00090307114,0.0028266967,0.30613393],"genre_scores_gemma":[0.853287,0.0065533114,0.058639538,0.02577655,0.003228635,0.00023317256,0.00026659455,0.000954629,0.05106067],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99496114,0.00048949505,0.00032184235,0.0006287699,0.0030856158,0.0005132404],"domain_scores_gemma":[0.9870144,0.0056154183,0.0013186446,0.0015294657,0.003933077,0.00058904866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025138836,0.00062523346,0.0009937386,0.00067727076,0.0021782967,0.0032737711,0.0011403804,0.0030881632,0.0068925554],"category_scores_gemma":[0.015828721,0.00037821545,0.00047522897,0.0005027439,0.0092174625,0.00598641,0.0015343116,0.004023185,0.003589594],"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":[0.00022018272,0.00015339586,0.0056093098,0.0012458222,0.000111712645,0.00057664496,0.0013827408,0.006969022,0.015315847,0.7309658,0.07674833,0.16070123],"study_design_scores_gemma":[0.00006300232,0.0005157976,0.0061306865,0.0005124435,0.000088512024,0.0015472269,0.00072281825,0.003631369,0.014584251,0.6342947,0.3376906,0.00021868548],"about_ca_topic_score_codex":0.004132029,"about_ca_topic_score_gemma":0.0032802708,"teacher_disagreement_score":0.0068925554,"about_ca_system_score_codex":0.002104241,"about_ca_system_score_gemma":0.0025060186,"threshold_uncertainty_score":0.023057938},"labels":[],"label_agreement":null},{"id":"W2014601079","doi":"10.1139/l05-098","title":"Standard data models for interoperability of municipal infrastructure asset management systems","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; University of British Columbia; U.S. Environmental Protection Agency","keywords":"Interoperability; Asset management; Computer science; IT asset management; Asset (computer security); Data exchange; Data management; Semantic interoperability; Process management; Database; Computer security; Business; Finance; World Wide Web","score_opus":0.012552239421233695,"score_gpt":0.1887067514309996,"score_spread":0.1761545120097659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014601079","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":[0.00774215,0.00048096917,0.96998334,0.0017573751,0.0001878053,0.0010648718,0.00457141,0.0023527192,0.011859326],"genre_scores_gemma":[0.10963678,0.0014158257,0.861615,0.0005413573,0.00009829952,0.0031818943,0.018591784,0.00082650164,0.004092557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97389597,0.0074697398,0.007728644,0.0015359328,0.00846262,0.00090721727],"domain_scores_gemma":[0.95012414,0.019314246,0.0034735552,0.014026865,0.012386979,0.00067424594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023445446,0.0010968983,0.001436064,0.006436172,0.0023533213,0.010802138,0.004576166,0.003241494,0.003950494],"category_scores_gemma":[0.06398368,0.0012239895,0.0031390078,0.008177627,0.0023371486,0.011771696,0.004502114,0.0039944914,0.0019274455],"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":[0.00015201657,0.0001866207,0.0049403086,0.0006531698,0.00012823146,0.00037178324,0.0012706877,0.10758369,0.0013870973,0.7792088,0.018685289,0.08543235],"study_design_scores_gemma":[0.00012870796,0.000115096685,0.0015772857,0.0011977177,0.00015902084,0.0004517071,0.0007937352,0.28791213,0.008019955,0.34729347,0.35215175,0.00019939072],"about_ca_topic_score_codex":0.020264965,"about_ca_topic_score_gemma":0.015692238,"teacher_disagreement_score":0.023445446,"about_ca_system_score_codex":0.0071769906,"about_ca_system_score_gemma":0.010033117,"threshold_uncertainty_score":0.12399286},"labels":[],"label_agreement":null},{"id":"W2014986918","doi":"10.1016/j.mcm.2006.01.005","title":"Application of two ant colony optimisation algorithms to water distribution system optimisation","year":2006,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":170,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Ant colony optimization algorithms; Ant colony; Computer science; Formicoidea; Mathematical optimization; Artificial bee colony algorithm; Algorithm; Artificial intelligence; Mathematics; Biology; Ecology; Aculeata","score_opus":0.008905156247217775,"score_gpt":0.18579248609278531,"score_spread":0.17688732984556754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014986918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0707376,0.0005483994,0.90818465,0.00057047745,0.00029265936,0.00012914391,0.00003632171,0.00045402118,0.01904686],"genre_scores_gemma":[0.6027494,0.00032655604,0.3868745,0.00012177623,0.00008360432,0.00013472742,0.000052034025,0.00017032133,0.009487067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934596,0.0002805379,0.000027329661,0.00005240226,0.0002489131,0.00004490319],"domain_scores_gemma":[0.99867487,0.00075019634,0.000078890014,0.00010875801,0.00033091643,0.000056349563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001036848,0.0007347917,0.0009189506,0.0007776557,0.0004940044,0.0012042824,0.0008381703,0.0011099111,0.0024197614],"category_scores_gemma":[0.004776981,0.00038681677,0.00046359748,0.0009521768,0.0006437288,0.00071924366,0.0010377369,0.0008567477,0.0003143031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023248896,0.00013189152,0.0005807722,0.00010468281,0.000061941646,0.00008117723,0.00012711347,0.84994364,0.0035718956,0.014638961,0.0012447692,0.1292806],"study_design_scores_gemma":[0.000026203603,0.000023367393,0.00009381276,0.000002591133,0.0000057188126,0.000016023809,0.0000068211425,0.9965056,0.000481596,0.0021639334,0.000670168,0.0000041553976],"about_ca_topic_score_codex":0.003915601,"about_ca_topic_score_gemma":0.0035311345,"teacher_disagreement_score":0.003915601,"about_ca_system_score_codex":0.00054668303,"about_ca_system_score_gemma":0.0008271048,"threshold_uncertainty_score":0.008094907},"labels":[],"label_agreement":null},{"id":"W2017596757","doi":"10.1115/ipc2004-0020","title":"Dynamic Modelling and Real-Time Leak Detection for NGL Pipelines","year":2004,"lang":"en","type":"article","venue":"2004 International Pipeline Conference, Volumes 1, 2, and 3","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Pipeline transport; Leak; Transient (computer programming); Computer science; Leak detection; Pipeline (software); Volume (thermodynamics); Simulation; Real-time computing; Engineering; Mechanical engineering","score_opus":0.009567724557949058,"score_gpt":0.20892336593627606,"score_spread":0.199355641378327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017596757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07235875,0.00013809872,0.9193238,0.00019736294,0.000024306786,0.0000646328,0.00016420723,0.0012998091,0.00642913],"genre_scores_gemma":[0.95454454,0.00024888397,0.03956756,0.000029475465,0.00000870568,0.00011630078,0.00019731236,0.00012878136,0.0051583136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997781,0.00005287864,0.000010892931,0.000040984458,0.00009227424,0.000025016743],"domain_scores_gemma":[0.99978787,0.000079104575,0.000041610947,0.000022637723,0.000056097124,0.000012698854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002795015,0.00052727584,0.00053101167,0.00034313116,0.0004358229,0.0008865462,0.000624096,0.00059203216,0.0018930547],"category_scores_gemma":[0.0008145583,0.00034986643,0.0004158621,0.00028269956,0.00064281106,0.0011071871,0.0005540237,0.00046404623,0.00027917366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029933262,0.0000096618705,0.00036468715,0.00002513552,0.0000035832716,0.00002886125,0.00003977034,0.988689,0.0042478675,0.0017290389,0.00014255474,0.0046898853],"study_design_scores_gemma":[0.000004505412,0.00000861211,0.000083772524,0.0000017933908,0.0000015437203,0.000006832735,0.000006443096,0.9974056,0.0012224156,0.0004324012,0.0008219783,0.0000040012274],"about_ca_topic_score_codex":0.019110879,"about_ca_topic_score_gemma":0.007942889,"teacher_disagreement_score":0.019110879,"about_ca_system_score_codex":0.0011227434,"about_ca_system_score_gemma":0.0013689597,"threshold_uncertainty_score":0.037999272},"labels":[],"label_agreement":null},{"id":"W2018291694","doi":"10.1061/(asce)0733-9496(2004)130:2(181)","title":"Closure to “Energy and Costs of Leaky Pipes: Toward Comprehensive Picture” by Andrew F. Colombo and Bryan W. Karney","year":2004,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Closure (psychology); Energy (signal processing); Engineering; Economics; Physics","score_opus":0.008166001304020886,"score_gpt":0.19862819913571847,"score_spread":0.19046219783169757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018291694","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009231417,0.21055679,0.014466352,0.6696471,0.047333237,0.000064264466,0.001285298,0.00012218641,0.04729334],"genre_scores_gemma":[0.25107816,0.37806612,0.011952852,0.11475082,0.11842998,0.00023022029,0.0019879504,0.00059669913,0.1229072],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984842,0.0005058606,0.00008101283,0.00021567008,0.0005579596,0.00015523513],"domain_scores_gemma":[0.99404,0.002800861,0.00030911271,0.00015183107,0.0024148284,0.0002833074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024262664,0.001083979,0.0012046288,0.0025293687,0.0017350502,0.004474164,0.0015990646,0.0038518526,0.009238811],"category_scores_gemma":[0.014009672,0.0006482447,0.0009177095,0.0043498,0.002535057,0.00658066,0.0016347045,0.007624792,0.0021178962],"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":[0.00006364377,0.000023506667,0.00092310226,0.00020852155,0.000032312146,0.00007513465,0.0003491666,0.0029735612,0.00010928598,0.06984851,0.88725775,0.038135473],"study_design_scores_gemma":[0.00001628886,0.00003392608,0.003471992,0.0010100729,0.00005059881,0.000116617855,0.0011971319,0.0049639577,0.00043589235,0.1127814,0.8758515,0.00007057679],"about_ca_topic_score_codex":0.043990627,"about_ca_topic_score_gemma":0.044579532,"teacher_disagreement_score":0.043990627,"about_ca_system_score_codex":0.0038064788,"about_ca_system_score_gemma":0.0038105475,"threshold_uncertainty_score":0.0874691},"labels":[],"label_agreement":null},{"id":"W2019011281","doi":"10.2166/hydro.2013.082","title":"Study on relationships between climate-related covariates and pipe bursts using evolutionary-based modelling","year":2013,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Covariate; Precipitation; Climate change; Climate model; Ecology; Climatology; Environmental science; Meteorology; Econometrics; Mathematics; Geography; Geology; Biology","score_opus":0.036311230024033386,"score_gpt":0.22209371487685092,"score_spread":0.18578248485281754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019011281","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":[0.8633851,0.00045138024,0.13222128,0.0005989614,0.00002292193,0.0000694639,0.00051656994,0.00013151595,0.002602679],"genre_scores_gemma":[0.9873914,0.00024480178,0.010870179,0.000020441015,0.000009614826,0.000041295687,0.0002798071,0.000016522692,0.0011259061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993913,0.00032325106,0.000027433658,0.00013623512,0.000051677318,0.00007013264],"domain_scores_gemma":[0.99481493,0.0043803067,0.00041460776,0.000086267784,0.00022286798,0.00008100183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002011973,0.0006859236,0.0007282055,0.0011348596,0.00041149216,0.0010790444,0.0011708406,0.000896086,0.0015564322],"category_scores_gemma":[0.008929122,0.00038190468,0.0008565072,0.0015655085,0.0004276441,0.00073711097,0.0007228078,0.00086579536,0.0001255903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005003253,0.00007822833,0.055391252,0.000050149756,0.00015753339,0.00015396002,0.0001687553,0.92932314,0.00042782622,0.0043888795,0.00019454984,0.009615728],"study_design_scores_gemma":[0.0000022079203,0.000015049251,0.0068483627,0.0000054824814,0.000015282349,0.000013054183,0.0000320113,0.99219805,0.00004679834,0.0006625575,0.00015592933,0.0000052981773],"about_ca_topic_score_codex":0.06709773,"about_ca_topic_score_gemma":0.041532688,"teacher_disagreement_score":0.06709773,"about_ca_system_score_codex":0.0012074255,"about_ca_system_score_gemma":0.0014096898,"threshold_uncertainty_score":0.13341433},"labels":[],"label_agreement":null},{"id":"W2019758683","doi":"10.1061/(asce)wr.1943-5452.0000101","title":"Evaluating the Environmental Impacts of Water Distribution Systems by Using EIO-LCA-Based Multiobjective Optimization","year":2010,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":53,"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; Queen's University","funders":"","keywords":"Index (typography); Sorting; Multi-objective optimization; Life-cycle assessment; Environmental science; Genetic algorithm; Environmental engineering; Environmental economics; Economics; Mathematical optimization; Computer science; Production (economics); Mathematics; Microeconomics","score_opus":0.012127201619506462,"score_gpt":0.23476917928487798,"score_spread":0.22264197766537153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019758683","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":[0.5857487,0.0003997486,0.3943378,0.00032052235,0.00003808853,0.00036188195,0.0004714396,0.00034793848,0.017973876],"genre_scores_gemma":[0.92215186,0.00018540068,0.07502784,0.000040269,0.000008248817,0.00019868334,0.0002650985,0.000032012853,0.002090659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994411,0.0002691995,0.000023048493,0.000047630503,0.00016717528,0.000051896022],"domain_scores_gemma":[0.99931836,0.00042905603,0.00008667112,0.000025195593,0.00011992469,0.000020751375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012112182,0.0008776856,0.00059572153,0.0013984705,0.00033794076,0.0010068235,0.00036893363,0.0004487532,0.0010063417],"category_scores_gemma":[0.0024994726,0.00035373375,0.0005446029,0.0010423631,0.00034205648,0.00064809434,0.000453141,0.00036791823,0.00009238799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015105648,0.000019593477,0.00078829634,0.000013793427,0.000023950586,0.00000930269,0.0000041147196,0.9932861,0.00025248862,0.0003955403,0.00004065539,0.00515108],"study_design_scores_gemma":[0.000004387042,0.000026966134,0.00038157165,0.0000027115796,0.000009714968,0.0000028061033,0.000008712095,0.9985159,0.00041953792,0.00049966696,0.00012526831,0.000002783082],"about_ca_topic_score_codex":0.015872555,"about_ca_topic_score_gemma":0.021047065,"teacher_disagreement_score":0.015872555,"about_ca_system_score_codex":0.0018825335,"about_ca_system_score_gemma":0.0013122532,"threshold_uncertainty_score":0.03156036},"labels":[],"label_agreement":null},{"id":"W2019851933","doi":"10.1088/1755-1315/22/3/032027","title":"A steady-state simulation methodology for predicting runaway speed in Francis turbines","year":2014,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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":"Polytechnique Montréal","funders":"","keywords":"Turbine; Transient (computer programming); Computational fluid dynamics; Solver; Torque; Flow (mathematics); Reynolds-averaged Navier–Stokes equations; Range (aeronautics); Francis turbine; Steady state (chemistry); Computer science; Control theory (sociology); Simulation; Engineering; Mechanics; Mechanical engineering; Aerospace engineering; Physics","score_opus":0.02392698325290051,"score_gpt":0.22342429346066262,"score_spread":0.1994973102077621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019851933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06590166,0.00014225623,0.9297721,0.00004203503,0.000034327855,0.00011432387,0.00009437969,0.0006577436,0.0032411958],"genre_scores_gemma":[0.7488657,0.00023184785,0.24864243,0.00002724099,0.00001576732,0.00035568755,0.00022908753,0.00017410069,0.001458058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998386,0.000043129996,0.00001598596,0.000020007526,0.0000637107,0.00001851813],"domain_scores_gemma":[0.99964523,0.00015190654,0.00005449633,0.00003887893,0.00010058677,0.000008914914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045390794,0.00042722808,0.00048677454,0.00044252674,0.00040876417,0.0005266969,0.0008560434,0.0007614154,0.001151376],"category_scores_gemma":[0.0012826528,0.00032901653,0.00050707,0.00032827366,0.00035675036,0.00055657275,0.0003903448,0.0004953383,0.00024620915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004046124,0.00003506728,0.00114851,0.00007677924,0.000017732127,0.00008444162,0.00008551304,0.9560446,0.015199753,0.0063896542,0.00029818335,0.020579314],"study_design_scores_gemma":[0.0000019611134,0.00002557939,0.00017307379,0.000004026074,0.0000030221267,0.000009255048,0.000006503231,0.9965643,0.0025795924,0.000209028,0.00041789096,0.0000057614993],"about_ca_topic_score_codex":0.0052841855,"about_ca_topic_score_gemma":0.004075745,"teacher_disagreement_score":0.0052841855,"about_ca_system_score_codex":0.0003861692,"about_ca_system_score_gemma":0.0006820718,"threshold_uncertainty_score":0.010506868},"labels":[],"label_agreement":null},{"id":"W2020314642","doi":"10.1080/15730620412331290038","title":"Modelling the advection equation under water hammer conditions","year":2004,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and 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":"Canadian Water Network; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Advection; Water hammer; Compressibility; Mechanics; Transient (computer programming); Environmental science; Consistency (knowledge bases); Geotechnical engineering; Finite difference; Water quality; Convergence (economics); Engineering; Computer science; Mathematics; Thermodynamics; Physics","score_opus":0.020334541668196114,"score_gpt":0.1877858425624959,"score_spread":0.16745130089429977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020314642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32391617,0.0009861192,0.64178777,0.001235282,0.0002204184,0.00025394378,0.0007494815,0.00036218954,0.030488636],"genre_scores_gemma":[0.9692113,0.0006183339,0.015161006,0.00009276216,0.0000427396,0.00016061237,0.00017549442,0.00005264968,0.014485079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999841,0.000042962034,0.000010712183,0.000032501215,0.000039850347,0.00003301996],"domain_scores_gemma":[0.9996056,0.00024546345,0.000058332604,0.000017463437,0.00005897829,0.000014176851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043058666,0.0005395784,0.0004932969,0.00043811844,0.00034916488,0.0010840389,0.0008397215,0.0019769527,0.0015665411],"category_scores_gemma":[0.0015662026,0.00032688765,0.0005208632,0.0003285661,0.0007384289,0.0012795994,0.0007863153,0.0008854657,0.00028386415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028623757,0.000025036756,0.0012169309,0.000057586272,0.000008665229,0.00015566096,0.00007846797,0.97818655,0.0047064866,0.012502911,0.00023534654,0.0027977722],"study_design_scores_gemma":[0.0000050300964,0.000007524342,0.00012337885,0.000002298207,0.0000021113096,0.000011170735,0.000010973389,0.9979869,0.00037248374,0.0011206331,0.00035330164,0.000004167039],"about_ca_topic_score_codex":0.019950146,"about_ca_topic_score_gemma":0.007639744,"teacher_disagreement_score":0.019950146,"about_ca_system_score_codex":0.00069888175,"about_ca_system_score_gemma":0.0011050433,"threshold_uncertainty_score":0.039668083},"labels":[],"label_agreement":null},{"id":"W2023279702","doi":"10.5942/jawwa.2014.106.0075","title":"Water distribution system reliability under simultaneous multicomponent failure scenario","year":2014,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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 British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Sensitivity (control systems); Engineering","score_opus":0.0021592655548591327,"score_gpt":0.16193597047073127,"score_spread":0.15977670491587215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023279702","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":[0.9852444,0.00006639746,0.013303602,0.000093531264,0.0000066774937,0.00002252219,0.000090596914,0.000050466922,0.0011218572],"genre_scores_gemma":[0.9994265,0.000015640531,0.00040705214,0.0000019577876,0.0000012882142,0.00000439178,0.000016176784,0.0000024899464,0.00012456282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929523,0.00024876712,0.000029223374,0.00013155618,0.00011955502,0.0001755906],"domain_scores_gemma":[0.9977204,0.0015288225,0.00029075757,0.00012278043,0.0002714134,0.000065773565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016820616,0.0005576975,0.00063789944,0.00064823916,0.00039594906,0.000544651,0.00048213272,0.00066879124,0.0006013881],"category_scores_gemma":[0.0038888294,0.00029289242,0.00056880736,0.000512038,0.0006977979,0.0009731826,0.0005181864,0.00058313337,0.00007989867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012182816,0.000024957484,0.0044884034,0.000022723052,0.000017710592,0.00022157705,0.00005758647,0.9909739,0.0014884387,0.00060722855,0.00010178581,0.0018739539],"study_design_scores_gemma":[0.000007724335,0.00010589119,0.0031144603,0.0000025692264,0.000013092318,0.000038950882,0.000070428236,0.995329,0.00065365987,0.00062076637,0.000036289115,0.0000071561294],"about_ca_topic_score_codex":0.016699143,"about_ca_topic_score_gemma":0.008270025,"teacher_disagreement_score":0.016699143,"about_ca_system_score_codex":0.0012473791,"about_ca_system_score_gemma":0.0007188356,"threshold_uncertainty_score":0.0332039},"labels":[],"label_agreement":null},{"id":"W2024055610","doi":"10.1115/imece2013-65838","title":"Front Wave Prediction in Water Hammer Phenomena Involving Two-Phase Flows","year":2013,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Water hammer; Mechanics; Momentum (technical analysis); Computational fluid dynamics; Two-phase flow; Two-fluid model; Work (physics); Conservation law; Flow (mathematics); Physics; Thermodynamics; Mathematics; Mathematical analysis","score_opus":0.010398997510223997,"score_gpt":0.18567452635977605,"score_spread":0.17527552884955205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024055610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47850367,0.0005667311,0.50644124,0.0004082766,0.00014512363,0.000106131985,0.00010378364,0.00041961207,0.013305417],"genre_scores_gemma":[0.9831103,0.00021261783,0.012255195,0.000029041064,0.000026862897,0.000034984936,0.000054542223,0.00003194846,0.0042445213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992645,0.00001935486,0.0000026100547,0.0000073450087,0.000027291631,0.000016906033],"domain_scores_gemma":[0.99969745,0.00016115373,0.000040162184,0.000018882212,0.000052115283,0.000030246929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002927827,0.0005873549,0.00051907526,0.0004889551,0.00025913725,0.0007695738,0.00050753326,0.0013456547,0.0011390634],"category_scores_gemma":[0.0010395675,0.00019958788,0.0004077196,0.00028379433,0.00068645104,0.00084658584,0.0006147335,0.0007152808,0.00019139827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009765061,0.00008381345,0.0010411885,0.00006532654,0.000012689322,0.00022618046,0.000068893314,0.96519876,0.010710079,0.012896602,0.0004799448,0.009118946],"study_design_scores_gemma":[0.0000020693667,0.000005249013,0.00005809931,8.05297e-7,5.0318897e-7,0.0000035709543,0.0000033766582,0.9992434,0.00023404357,0.00041255887,0.00003514233,0.0000013057256],"about_ca_topic_score_codex":0.00491348,"about_ca_topic_score_gemma":0.0019802933,"teacher_disagreement_score":0.00491348,"about_ca_system_score_codex":0.00046949557,"about_ca_system_score_gemma":0.00042640275,"threshold_uncertainty_score":0.009769738},"labels":[],"label_agreement":null},{"id":"W2024077373","doi":"10.1115/ipc2014-33616","title":"Understanding of the Risks of High Pressures Following Rapid Pressurization in Pipelines Containing Entrapped Air Pockets: A Novel Energy Auditing Approach","year":2014,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Cabin pressurization; Pipeline transport; Transient (computer programming); Mechanics; Flow (mathematics); Petroleum engineering; Environmental science; Materials science; Mechanical engineering; Computer science; Engineering; Physics","score_opus":0.05840067469458764,"score_gpt":0.22134755147303264,"score_spread":0.162946876778445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024077373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1856905,0.00063337624,0.80715364,0.0006672156,0.000041252817,0.00008803438,0.00005568841,0.00020623734,0.005464092],"genre_scores_gemma":[0.9591034,0.0006430805,0.038822513,0.000038872975,0.000028971925,0.000046897898,0.00002294757,0.000014709444,0.0012787487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982965,0.000031864078,0.000011099378,0.000033578675,0.00006893874,0.00002489204],"domain_scores_gemma":[0.9995177,0.00023560104,0.00012818315,0.000039824958,0.000060159822,0.000018477562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038792868,0.00056781335,0.00058395404,0.0005563924,0.00033482592,0.0008036168,0.0007821077,0.00071274315,0.0010180813],"category_scores_gemma":[0.0011599245,0.00040245973,0.00043162864,0.00022546879,0.00084242196,0.0018349664,0.0009849938,0.0010148716,0.00009474941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014422521,0.00019429956,0.0044981646,0.00024219442,0.000034317898,0.00046512525,0.00019149238,0.837183,0.07954929,0.026559908,0.00028984214,0.05064814],"study_design_scores_gemma":[0.000003686498,0.000053708383,0.0006643498,0.0000047859307,0.0000066573048,0.000051940704,0.000041406,0.99141043,0.00471049,0.0028299228,0.00021067407,0.000011898664],"about_ca_topic_score_codex":0.0013967209,"about_ca_topic_score_gemma":0.0010221096,"teacher_disagreement_score":0.0013967209,"about_ca_system_score_codex":0.00046566606,"about_ca_system_score_gemma":0.001049565,"threshold_uncertainty_score":0.0034058094},"labels":[],"label_agreement":null},{"id":"W2024392733","doi":"10.1115/ipc2004-0274","title":"Automatic, Non-Intrusive, Flame Detection in Pipelines","year":2004,"lang":"en","type":"article","venue":"2004 International Pipeline Conference, Volumes 1, 2, and 3","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Flammable liquid; Combustion; Noise (video); Filter (signal processing); Pipeline transport; Acoustics; Environmental science; Computer science; Engineering; Electrical engineering; Waste management; Physics; Artificial intelligence; Environmental engineering","score_opus":0.0061631295931719845,"score_gpt":0.20149755449303935,"score_spread":0.19533442489986738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024392733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20485376,0.00036572627,0.7903705,0.0000638172,0.000020158182,0.00006074283,0.000102462924,0.003099601,0.0010632457],"genre_scores_gemma":[0.794631,0.00016490277,0.2034744,0.000017578594,0.000012630077,0.00004416818,0.00011344242,0.00011784968,0.0014240391],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920017,0.00011540009,0.00002645703,0.00016913544,0.0004321983,0.00005668197],"domain_scores_gemma":[0.9993469,0.0002636003,0.00016155031,0.000038843762,0.00016152073,0.000027532797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007650972,0.00061185146,0.0005595477,0.0011129004,0.00043555332,0.0004555827,0.00074185507,0.0006159378,0.00075359695],"category_scores_gemma":[0.0022382617,0.00055186555,0.0002741291,0.0007881795,0.00066089706,0.00086175825,0.0004047696,0.00034440472,0.00026621405],"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":[0.0011522248,0.0001797298,0.008664322,0.00043070898,0.000043979388,0.0003287988,0.00037519506,0.18259461,0.42365342,0.0028267687,0.0011146024,0.37863562],"study_design_scores_gemma":[0.000058888374,0.0003715267,0.010156569,0.000015205902,0.000020584906,0.00031055737,0.000040928764,0.8517104,0.13315308,0.002177986,0.0019204672,0.0000638582],"about_ca_topic_score_codex":0.0044723013,"about_ca_topic_score_gemma":0.004364029,"teacher_disagreement_score":0.0044723013,"about_ca_system_score_codex":0.00051952666,"about_ca_system_score_gemma":0.000569464,"threshold_uncertainty_score":0.008892596},"labels":[],"label_agreement":null},{"id":"W2024810235","doi":"10.1061/40994(321)76","title":"Clustered Models for the Integration of Sewer Condition Classification Protocols","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Sanitary sewer; Protocol (science); Computer science; Pipeline (software); Systems engineering; Risk analysis (engineering); Engineering; Environmental engineering; Business","score_opus":0.09723807053405663,"score_gpt":0.2778929128247246,"score_spread":0.18065484229066797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024810235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04383783,0.00025673403,0.95057225,0.00035129968,0.000061134975,0.00019769817,0.00043291098,0.0007932755,0.0034968941],"genre_scores_gemma":[0.8068473,0.0004027124,0.17581075,0.00017297533,0.00008178479,0.0009234642,0.0017316086,0.00026133767,0.0137681225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842864,0.00053890125,0.00009786279,0.0004894526,0.00030003098,0.00014510073],"domain_scores_gemma":[0.9959462,0.0020403946,0.00044499958,0.00036633672,0.0011049964,0.000097133416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035814084,0.0009128816,0.0011959181,0.0011909452,0.0005652094,0.0016524988,0.0034751957,0.0020359375,0.0036843996],"category_scores_gemma":[0.011615391,0.00071143574,0.0013056111,0.0012679821,0.00082026847,0.002694751,0.0013285385,0.002261026,0.0010747168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060823633,0.00004838155,0.0007810133,0.000024950068,0.000060021925,0.000024261506,0.000052249405,0.97086316,0.00032602917,0.011119561,0.00069240277,0.015947144],"study_design_scores_gemma":[0.0000025363543,0.000007962926,0.00014725635,0.0000028501013,0.000006696362,0.0000029256191,0.0000043887844,0.9964948,0.00007896947,0.0030928045,0.00015417284,0.0000046487703],"about_ca_topic_score_codex":0.021316025,"about_ca_topic_score_gemma":0.0185918,"teacher_disagreement_score":0.021316025,"about_ca_system_score_codex":0.0030268016,"about_ca_system_score_gemma":0.0016167938,"threshold_uncertainty_score":0.04238391},"labels":[],"label_agreement":null},{"id":"W2025989647","doi":"10.1016/j.jsv.2009.03.037","title":"On the effects of reflections on time delay estimation for leak detection in buried plastic water pipes","year":2009,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Water Systems and Optimization","field":"Engineering","cited_by":59,"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":"Engineering and Physical Sciences Research Council; National Research Council Canada","keywords":"Classification of discontinuities; Leak; Noise (video); Water pipe; Cross-correlation; Acoustics; Leak detection; Function (biology); Cross section (physics); Correlation function (quantum field theory); Pipe network analysis; Phase (matter); Engineering; Structural engineering; Mechanics; Computer science; Physics; Mathematics; Mechanical engineering; Mathematical analysis; Telecommunications","score_opus":0.00804645925670832,"score_gpt":0.22047924243087433,"score_spread":0.212432783174166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025989647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34853125,0.0007636317,0.6493483,0.00011271144,0.00008018808,0.000021740569,0.000040413728,0.00024881927,0.00085300405],"genre_scores_gemma":[0.9170373,0.00081340154,0.08043294,0.00004895671,0.000096034026,0.00001647695,0.00009797259,0.00008270262,0.0013742255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992631,0.000281047,0.00004155802,0.000098349214,0.00023580152,0.00008009052],"domain_scores_gemma":[0.9899676,0.00881929,0.00027076455,0.00018276431,0.00065926334,0.00010040066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013716017,0.00066184474,0.00063783984,0.00035159243,0.00023013516,0.0004893976,0.00053095346,0.00078731327,0.0008065988],"category_scores_gemma":[0.009084097,0.00040798905,0.00048455317,0.00031946602,0.00053500663,0.0010411579,0.00053898006,0.00083428656,0.00021959755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00504597,0.0002893579,0.0044231354,0.00054136844,0.00027131988,0.00042962277,0.0004891414,0.4033361,0.30250242,0.0015938221,0.00066561904,0.28041205],"study_design_scores_gemma":[0.000025816305,0.00037555842,0.0021311142,0.000022583943,0.00009947859,0.0001006252,0.00005735962,0.91740876,0.07913006,0.0003511232,0.00027546927,0.000021962776],"about_ca_topic_score_codex":0.0021249854,"about_ca_topic_score_gemma":0.0018065331,"teacher_disagreement_score":0.0021249854,"about_ca_system_score_codex":0.0002223309,"about_ca_system_score_gemma":0.0003805658,"threshold_uncertainty_score":0.0072538257},"labels":[],"label_agreement":null},{"id":"W2026204325","doi":"10.1243/09544062jmes1839","title":"An improved fuzzy programming model with an L—R fuzzy number for filter management strategies in fluid power systems under uncertainty","year":2010,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science","topic":"Water Systems and 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":"Fuzzy logic; Robustness (evolution); Linearization; Control theory (sociology); Mathematical optimization; Filter (signal processing); Computer science; Fuzzy control system; Nonlinear system; Mathematics; Control (management); Artificial intelligence","score_opus":0.009628358638816191,"score_gpt":0.2246398719516208,"score_spread":0.2150115133128046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026204325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051590847,0.00013680673,0.99110854,0.000114641894,0.00001996449,0.000033075357,0.000064954846,0.00006595144,0.003296978],"genre_scores_gemma":[0.71460223,0.000702742,0.27269098,0.00011885023,0.00006468421,0.0006000049,0.00021896875,0.00006065975,0.0109408805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995376,0.00016467269,0.000020270343,0.00009901212,0.00012463075,0.00005383093],"domain_scores_gemma":[0.9995524,0.00026472379,0.000059376234,0.000015279182,0.00009000729,0.00001824209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010406947,0.0009889586,0.0011410933,0.0005147299,0.00058352423,0.001475102,0.0015188233,0.001445355,0.0031345855],"category_scores_gemma":[0.0014360716,0.00050829654,0.0010135542,0.0007482957,0.0007170331,0.0013685804,0.0006804376,0.0016572406,0.00043088867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014100409,0.000010112536,0.000065466986,0.000029667543,0.0000074202103,0.00003941752,0.00003033719,0.98056763,0.00041665672,0.013538182,0.00023240253,0.005048648],"study_design_scores_gemma":[0.0000029316939,0.000010147604,0.000019667066,0.000003916349,0.0000026021567,0.0000055498645,0.000003871822,0.9969216,0.00008088335,0.0026641663,0.0002810521,0.0000035192709],"about_ca_topic_score_codex":0.012275246,"about_ca_topic_score_gemma":0.0080035245,"teacher_disagreement_score":0.012275246,"about_ca_system_score_codex":0.0016380332,"about_ca_system_score_gemma":0.0017809998,"threshold_uncertainty_score":0.024407625},"labels":[],"label_agreement":null},{"id":"W2026993589","doi":"10.1061/40854(211)24","title":"Financial Benefits from Seven Years of Water Loss Control Utilizing the Sahara System at Thames Water in the United Kingdom","year":2006,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Research Canada","funders":"","keywords":"Mains electricity; Environmental science; Water supply; Electricity; Leakage (economics); Engineering; Hydrology (agriculture); Environmental engineering","score_opus":0.010375214093178759,"score_gpt":0.1722196186769286,"score_spread":0.16184440458374982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026993589","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":[0.7594351,0.00883827,0.001985133,0.024586273,0.00140043,0.00025636825,0.0025570055,0.0003575784,0.20058383],"genre_scores_gemma":[0.9302445,0.0031334339,0.0009801806,0.0011168837,0.0002137601,0.000042890988,0.001000684,0.000030970565,0.06323665],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990644,0.00015662945,0.0000512982,0.000044948112,0.00040332924,0.000279317],"domain_scores_gemma":[0.99733907,0.00027119197,0.00031640637,0.00012794023,0.00092054094,0.0010248058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073524064,0.00033948623,0.00011506628,0.00066807936,0.00088256394,0.0016166815,0.00038357906,0.00060964824,0.013891708],"category_scores_gemma":[0.0034066555,0.00012384936,0.00021893776,0.000618635,0.00037541302,0.0006400556,0.0018561125,0.00052242895,0.0015302991],"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":[0.0020186875,0.00047542268,0.09681272,0.0005541508,0.00014358088,0.007835925,0.0024676663,0.0049257567,0.011771651,0.005542779,0.20160402,0.6658476],"study_design_scores_gemma":[0.00017555422,0.001650986,0.40997276,0.00029060274,0.00009504895,0.0030010398,0.0074387775,0.0021886292,0.0032033229,0.00053121976,0.57133114,0.00012103732],"about_ca_topic_score_codex":0.049398962,"about_ca_topic_score_gemma":0.067812204,"teacher_disagreement_score":0.049398962,"about_ca_system_score_codex":0.0021945229,"about_ca_system_score_gemma":0.0030531597,"threshold_uncertainty_score":0.09822285},"labels":[],"label_agreement":null},{"id":"W2028049166","doi":"10.1007/s11269-013-0372-6","title":"Multi-Objective Design Optimization of Branched Pipeline Systems with Analytical Assessment of Fire Flow Failure Probability","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and 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; Queen's University","funders":"","keywords":"Sizing; Sorting; Multi-objective optimization; Probabilistic logic; Mathematical optimization; Flow (mathematics); Pareto principle; Optimization problem; Pipeline (software); Selection (genetic algorithm); Conditional probability; Pipeline transport; Reduction (mathematics); Fire protection; Computer science; Engineering; Mathematics; Algorithm; Environmental engineering; Civil engineering","score_opus":0.011764683683968815,"score_gpt":0.20059614183905514,"score_spread":0.1888314581550863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028049166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20290773,0.0007368888,0.7868706,0.00037764307,0.00005605454,0.00014679646,0.00017902214,0.00018974114,0.00853553],"genre_scores_gemma":[0.9578314,0.0002290312,0.03876406,0.00003163533,0.000018756311,0.00015043843,0.000089090005,0.00003775454,0.002847848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940443,0.00023793244,0.000021958058,0.000084344036,0.00013322906,0.00011819334],"domain_scores_gemma":[0.998514,0.0009519711,0.00021447455,0.00003998257,0.00019773233,0.000081778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017795787,0.0014053863,0.0015486084,0.0013448584,0.00067884685,0.0015665278,0.0010690561,0.0019118076,0.001848857],"category_scores_gemma":[0.0036001275,0.0011309971,0.0011320414,0.0010202228,0.00093205285,0.0009822466,0.0011800634,0.0009577405,0.0001896731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000099073395,0.0000061725154,0.00006019524,0.000008772437,0.000006513845,0.0000094199095,0.000003990273,0.9986212,0.00015666019,0.00023920013,0.000015293857,0.00086263905],"study_design_scores_gemma":[0.0000025466734,0.000014403182,0.000039602994,0.0000013887521,0.0000027123654,0.0000016669221,0.0000023235184,0.99960285,0.00008673632,0.00022124869,0.000023291126,0.0000012682896],"about_ca_topic_score_codex":0.0073701567,"about_ca_topic_score_gemma":0.005649211,"teacher_disagreement_score":0.0073701567,"about_ca_system_score_codex":0.0014799386,"about_ca_system_score_gemma":0.001641432,"threshold_uncertainty_score":0.014654517},"labels":[],"label_agreement":null},{"id":"W2028524042","doi":"10.1016/j.ijpharm.2006.05.039","title":"Detection of the breakage of pharmaceutical tablets in pneumatic transport","year":2006,"lang":"en","type":"article","venue":"International Journal of Pharmaceutics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":28,"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","funders":"","keywords":"Breakage; Materials science; Volumetric flow rate; Composite material; Biomedical engineering; Mechanics; Engineering","score_opus":0.01167006711318362,"score_gpt":0.25828418489958943,"score_spread":0.24661411778640582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028524042","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":[0.9898755,0.0006672629,0.007952036,0.000094063704,0.00005139276,0.000046077355,0.00023580909,0.00015948035,0.00091834884],"genre_scores_gemma":[0.9956605,0.00021604462,0.0028281407,0.000034134617,0.000017330054,0.000016439404,0.00013691571,0.000017564878,0.0010730098],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999323,0.00007585081,0.000021296866,0.00013630131,0.0003335768,0.00010997804],"domain_scores_gemma":[0.99886525,0.0004126959,0.00033742463,0.000061878614,0.00021940887,0.00010326266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041300082,0.00046178148,0.000713057,0.00085730705,0.00039543814,0.0005847831,0.000650716,0.0015090759,0.0019744677],"category_scores_gemma":[0.0012842997,0.0003570198,0.00043109304,0.0005477617,0.0004928833,0.0006010721,0.00047972737,0.00097465096,0.0005286028],"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":[0.0025687644,0.00013782445,0.015236453,0.00019686187,0.0000546831,0.0005220479,0.00013871308,0.0007515887,0.9640694,0.000109511224,0.00027254262,0.015941618],"study_design_scores_gemma":[0.000067406734,0.003031499,0.10151213,0.000023617444,0.000093837996,0.0011571313,0.00029135824,0.04154881,0.8506081,0.00011511413,0.0014977598,0.000053283118],"about_ca_topic_score_codex":0.0008334884,"about_ca_topic_score_gemma":0.0006849513,"teacher_disagreement_score":0.0019744677,"about_ca_system_score_codex":0.00033538658,"about_ca_system_score_gemma":0.0002235617,"threshold_uncertainty_score":0.0066052675},"labels":[],"label_agreement":null},{"id":"W2031157352","doi":"10.1016/j.chemosphere.2004.11.087","title":"Interpreting drinking water quality in the distribution system using Dempster–Shafer theory of evidence","year":2005,"lang":"en","type":"article","venue":"Chemosphere","topic":"Water Systems and 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":"Université Laval","funders":"","keywords":"Dempster–Shafer theory; Water quality; Quality (philosophy); Computer science; Sensor fusion; Process (computing); Task (project management); Data mining; Data quality; Interpretation (philosophy); Risk analysis (engineering); Artificial intelligence; Engineering; Systems engineering; Operations management","score_opus":0.05100281386725061,"score_gpt":0.26892853512565756,"score_spread":0.21792572125840695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031157352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16289409,0.011257486,0.80315846,0.012089787,0.00019697536,0.00022268566,0.0012927406,0.00024654745,0.0086411955],"genre_scores_gemma":[0.91328514,0.0026418322,0.08269126,0.00037257353,0.0001181563,0.000094599614,0.00031468135,0.000019768924,0.00046206228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9850948,0.010258912,0.0014716953,0.0014088044,0.0014940415,0.00027177812],"domain_scores_gemma":[0.8354643,0.15179151,0.0053036655,0.0031881575,0.0037582263,0.0004942166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027111067,0.0010736729,0.0026640631,0.009835,0.0010654904,0.006309953,0.0017602342,0.0032579033,0.0021405271],"category_scores_gemma":[0.16639765,0.0009149558,0.0019807091,0.005404644,0.0037238894,0.006096097,0.0024127075,0.0026564936,0.00012371174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005184397,0.00020170906,0.06729743,0.002535572,0.0030821352,0.001767682,0.0011143996,0.46838495,0.0012022462,0.2975441,0.003860531,0.15249068],"study_design_scores_gemma":[0.00011799739,0.000112401,0.008492621,0.00028854908,0.0005765398,0.00031714907,0.00040178053,0.3646432,0.0010694517,0.62178606,0.0021157968,0.00007849564],"about_ca_topic_score_codex":0.013153803,"about_ca_topic_score_gemma":0.0101483585,"teacher_disagreement_score":0.027111067,"about_ca_system_score_codex":0.003967494,"about_ca_system_score_gemma":0.0035247013,"threshold_uncertainty_score":0.14337873},"labels":[],"label_agreement":null},{"id":"W2032496072","doi":"10.1061/(asce)em.1943-7889.0000825","title":"Analysis of Water Hammer Attenuation in Applications with Varying Valve Closure Times","year":2014,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":33,"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":"Water hammer; Attenuation; Mechanics; Closure (psychology); Flow (mathematics); Geology; Mathematics; Physics; Optics","score_opus":0.004379407136167034,"score_gpt":0.17198269701126023,"score_spread":0.1676032898750932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032496072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31897175,0.00044474026,0.67072725,0.00013770435,0.000027204023,0.0000485269,0.000030693896,0.00054261973,0.009069487],"genre_scores_gemma":[0.9865763,0.00016611908,0.011717049,0.000016741293,0.000010396016,0.000020701818,0.000019142712,0.00004165086,0.001431937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999696,0.000044177494,0.0000100403495,0.0000329309,0.00017022013,0.000046671772],"domain_scores_gemma":[0.998408,0.001085452,0.00016698975,0.00008145131,0.00022270788,0.00003539128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068884995,0.00034574594,0.0003801954,0.00058167183,0.00023048276,0.00046291147,0.0005028982,0.00050173665,0.00076764193],"category_scores_gemma":[0.0045394506,0.00016878193,0.00029233476,0.00036488788,0.0006391969,0.0006575499,0.00035220786,0.000645401,0.00014974813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025219502,0.0001317256,0.0032535694,0.00025993335,0.000022484352,0.00087662804,0.0003274541,0.7593415,0.14355321,0.019637248,0.0005933466,0.0717507],"study_design_scores_gemma":[0.0000017472494,0.000043322954,0.0006951215,0.000004761171,0.0000040067052,0.00006808336,0.000022063457,0.9916442,0.0064380663,0.00082080916,0.00025258865,0.0000051600173],"about_ca_topic_score_codex":0.0015739985,"about_ca_topic_score_gemma":0.0007913705,"teacher_disagreement_score":0.0015739985,"about_ca_system_score_codex":0.00051085843,"about_ca_system_score_gemma":0.00048619867,"threshold_uncertainty_score":0.0037065148},"labels":[],"label_agreement":null},{"id":"W2032947468","doi":"10.1016/j.proeng.2014.11.161","title":"Battle of Background Leakage Assessment for Water Networks (BBLAWN): An Incremental Savings Approach","year":2014,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Battle; Margin (machine learning); Leakage (economics); Mathematical optimization; Engineering; Baseline (sea); Process (computing); Operations research; Computer science; Reliability engineering; Mathematics; Economics","score_opus":0.00861531370230728,"score_gpt":0.1987886448658803,"score_spread":0.19017333116357302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032947468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012290485,0.00011831727,0.9735087,0.00032489208,0.000025207617,0.00021360797,0.00006429039,0.0002396568,0.013214727],"genre_scores_gemma":[0.6113343,0.00027203374,0.37997863,0.00016970545,0.000047872327,0.00038712533,0.00019032699,0.0001476326,0.0074723363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899393,0.00037887145,0.00002195997,0.000090305286,0.00039454413,0.000120390454],"domain_scores_gemma":[0.99955875,0.00021098107,0.000044940858,0.000035838584,0.00012226566,0.000027215461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016420808,0.0011394059,0.00071682443,0.00095172593,0.00051897875,0.0013867656,0.0015429836,0.0009041973,0.0045068436],"category_scores_gemma":[0.0021157486,0.00057273946,0.00067497225,0.0005433956,0.000615271,0.0013625219,0.0016096666,0.0010275372,0.00047916727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078372315,0.00007025489,0.00027979992,0.000085292246,0.000018194594,0.00006101093,0.00004226563,0.9089321,0.003042686,0.01258314,0.0012045872,0.073602326],"study_design_scores_gemma":[0.000012695999,0.00011165285,0.00011599379,0.000016077405,0.000013371605,0.000027440914,0.000024752575,0.9910734,0.0013962217,0.0051702056,0.00202989,0.000008283187],"about_ca_topic_score_codex":0.0032528054,"about_ca_topic_score_gemma":0.005541607,"teacher_disagreement_score":0.0045068436,"about_ca_system_score_codex":0.0014680282,"about_ca_system_score_gemma":0.0020164838,"threshold_uncertainty_score":0.015076935},"labels":[],"label_agreement":null},{"id":"W2034735153","doi":"10.1680/muen.2006.159.3.155","title":"Cost forecast model for sewer infrastructure","year":2006,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Municipal Engineer","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Logistic regression; Logit; Regression analysis; Predictive modelling; Computer science; Operations research; Engineering; Machine learning","score_opus":0.012500538542627181,"score_gpt":0.1981559635773824,"score_spread":0.18565542503475524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034735153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12990649,0.0006267872,0.8442888,0.0018128155,0.00016657349,0.0001079633,0.0036065935,0.0010406224,0.018443348],"genre_scores_gemma":[0.9513018,0.00057512795,0.022866769,0.00009206079,0.00007009376,0.00023616904,0.0016440193,0.00009250612,0.023121411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963105,0.00009639989,0.000017714437,0.00007978193,0.00010331177,0.00007171063],"domain_scores_gemma":[0.9990307,0.0005104255,0.00012820271,0.000030002688,0.00025457717,0.00004615399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092133705,0.00065411505,0.0007126871,0.0007418628,0.00031549827,0.0010355051,0.0015191531,0.0015129844,0.007219447],"category_scores_gemma":[0.0030014464,0.0003968641,0.000547437,0.0007920193,0.0004139596,0.0014262947,0.00041506818,0.0011470218,0.000931426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012216548,0.0000041259586,0.00025100043,0.000007672472,0.0000035192788,0.000015442547,0.000006060575,0.993616,0.00008963106,0.0037254484,0.00040279914,0.001866122],"study_design_scores_gemma":[0.0000016536346,0.0000027684348,0.000077442375,0.0000012586669,0.0000017360146,0.0000027881504,0.0000023373957,0.99869955,0.000025595451,0.0010222989,0.0001602582,0.0000021379592],"about_ca_topic_score_codex":0.040196586,"about_ca_topic_score_gemma":0.019597784,"teacher_disagreement_score":0.040196586,"about_ca_system_score_codex":0.0018506936,"about_ca_system_score_gemma":0.0011521823,"threshold_uncertainty_score":0.07992524},"labels":[],"label_agreement":null},{"id":"W2035233840","doi":"10.1016/j.jenvman.2008.12.014","title":"Uncertainty characterization approaches for risk assessment of DBPs in drinking water: A review","year":2009,"lang":"en","type":"review","venue":"Journal of Environmental Management","topic":"Water Systems and 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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Risk analysis (engineering); Probabilistic logic; Risk assessment; Probabilistic risk assessment; Risk management; Reliability (semiconductor); Computer science; Sampling (signal processing); Reliability engineering; Engineering; Business; Artificial intelligence","score_opus":0.02505012682763628,"score_gpt":0.24539583067886409,"score_spread":0.22034570385122781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035233840","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":[0.0010086248,0.97401017,0.022936927,0.00035920233,0.00014002435,0.000033445594,0.0001177605,0.000034537596,0.0013593355],"genre_scores_gemma":[0.008499279,0.9772849,0.0132589275,0.00012884077,0.00020397015,0.000039081417,0.00013683151,0.0000119702145,0.0004362742],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998899,0.0002071344,0.00015172383,0.0001908814,0.00050275755,0.000048535672],"domain_scores_gemma":[0.996689,0.0021158832,0.00035184133,0.00009385581,0.00071521377,0.000034228946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032160622,0.002112606,0.0038450742,0.0041872277,0.00039576096,0.0017436633,0.0027717035,0.001563718,0.0013500487],"category_scores_gemma":[0.0035600318,0.00075083144,0.0020109096,0.0048293984,0.0008123662,0.002334765,0.0012885432,0.0014069142,0.0005883418],"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":[0.00005362635,0.000111417205,0.0007553608,0.02084624,0.00058893743,0.0001864265,0.000073757554,0.016537381,0.0031954828,0.0097472565,0.0056760143,0.94222814],"study_design_scores_gemma":[0.0001007829,0.00082521274,0.007278148,0.020587351,0.003679478,0.0037502944,0.000614343,0.06183567,0.03148791,0.095921874,0.77321374,0.000705247],"about_ca_topic_score_codex":0.0038626774,"about_ca_topic_score_gemma":0.003966469,"teacher_disagreement_score":0.0041872277,"about_ca_system_score_codex":0.0010189931,"about_ca_system_score_gemma":0.0017547848,"threshold_uncertainty_score":0.017008364},"labels":[],"label_agreement":null},{"id":"W2035840781","doi":"10.1061/40854(211)14","title":"Pinpointing Exfiltration in Large Diameter Pressurized Wastewater Pipelines with the Sahara Tethered System","year":2006,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Research Canada","funders":"","keywords":"Piping; Pipeline transport; Mains electricity; Wastewater; Leak; Leak detection; Environmental science; Hazard; Engineering; Waste management; Civil engineering; Environmental engineering","score_opus":0.003133987803804017,"score_gpt":0.14999611593675152,"score_spread":0.1468621281329475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035840781","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":[0.9638778,0.00062537886,0.03290652,0.00013014737,0.00005410273,0.00014122513,0.0002060249,0.00049946,0.0015593017],"genre_scores_gemma":[0.9558347,0.0007503143,0.040105395,0.000101965445,0.000010302968,0.000055342567,0.00017121347,0.000045437846,0.0029253918],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993538,0.00012275379,0.000043094125,0.00010321705,0.0002900686,0.000086956024],"domain_scores_gemma":[0.99944955,0.00011479437,0.00017000071,0.000059132908,0.00016862579,0.000037902417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046174406,0.0003310352,0.00037514252,0.0006820143,0.0003069399,0.0004866716,0.0004916145,0.0005450394,0.0013038024],"category_scores_gemma":[0.0010559582,0.00024196185,0.00025459708,0.0006508618,0.00035789018,0.000679587,0.00079248403,0.00031307575,0.0003975112],"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":[0.0005368199,0.00008133158,0.022967432,0.00037589448,0.00004020511,0.0010153813,0.0010559433,0.0016729395,0.89229244,0.00019356975,0.00034382514,0.07942419],"study_design_scores_gemma":[0.000037811686,0.0037481922,0.12462579,0.00012838666,0.00012988443,0.0021065976,0.0026937935,0.013684324,0.83916515,0.00019600632,0.013355152,0.00012890637],"about_ca_topic_score_codex":0.0036425171,"about_ca_topic_score_gemma":0.009997908,"teacher_disagreement_score":0.0036425171,"about_ca_system_score_codex":0.00034340474,"about_ca_system_score_gemma":0.00028666598,"threshold_uncertainty_score":0.0072426796},"labels":[],"label_agreement":null},{"id":"W2036431852","doi":"10.2166/wst.2013.397","title":"Modelling and monitoring of integrated urban wastewater systems: review on status and perspectives","year":2013,"lang":"en","type":"review","venue":"Water Science & Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":77,"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; Stantec (Canada)","funders":"Ministerio de Ciencia y Tecnología; Canada Research Chairs","keywords":"Interfacing; Systems engineering; Computer science; Field (mathematics); Software; Risk analysis (engineering); Engineering; Business","score_opus":0.03226480959025829,"score_gpt":0.24901553056324513,"score_spread":0.21675072097298684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036431852","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":[0.00025131236,0.9976174,0.0013432719,0.00013764374,0.00008379735,0.0000068895542,0.000023033966,0.000010731114,0.0005257868],"genre_scores_gemma":[0.0016989566,0.9966934,0.0011016954,0.0000616491,0.00012333908,0.000009982803,0.000042916323,0.0000032416433,0.00026483095],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994593,0.00010077688,0.00007039803,0.00012421956,0.00020945615,0.000035806945],"domain_scores_gemma":[0.9990841,0.00042291833,0.00015765264,0.000029543822,0.00027139636,0.000034351964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013699731,0.0017024988,0.0033551718,0.0031252927,0.0002720205,0.0014457791,0.0020787953,0.0016095426,0.0019029776],"category_scores_gemma":[0.0010674893,0.00065268995,0.0014056634,0.0045828503,0.00064203964,0.0020902655,0.0010119269,0.0010679679,0.0011357913],"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":[0.00005775902,0.00016435883,0.0005421414,0.041476786,0.00026191006,0.00016863595,0.00008269614,0.00895863,0.003649372,0.007086613,0.012209408,0.92534167],"study_design_scores_gemma":[0.000031195326,0.000423082,0.0021041017,0.010716054,0.0007551163,0.0014136081,0.0002612002,0.0063669817,0.0051031196,0.010368412,0.96230155,0.0001555316],"about_ca_topic_score_codex":0.002245016,"about_ca_topic_score_gemma":0.0023722223,"teacher_disagreement_score":0.0033551718,"about_ca_system_score_codex":0.00052114256,"about_ca_system_score_gemma":0.0012613583,"threshold_uncertainty_score":0.007245183},"labels":[],"label_agreement":null},{"id":"W2036803902","doi":"10.1139/l10-035","title":"Technical hazard identification in water treatment using fault tree analysis","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval; University of British Columbia","funders":"","keywords":"Fault tree analysis; Hazard analysis; Identification (biology); Hazard; Fault (geology); Reliability engineering; Computer science; Tree (set theory); Risk analysis (engineering); Engineering; Mathematics; Business","score_opus":0.007828175687240937,"score_gpt":0.18929125878671374,"score_spread":0.1814630830994728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036803902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06421837,0.00014406019,0.9339252,0.000088663495,0.000010538639,0.000093273586,0.00017259581,0.00043677885,0.00091054104],"genre_scores_gemma":[0.6475709,0.00016081164,0.35099396,0.00003075646,0.000013073674,0.00010221176,0.0003470791,0.0000533911,0.0007278536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991254,0.00027827552,0.00005573457,0.000108199674,0.00033407367,0.000098226476],"domain_scores_gemma":[0.99698514,0.0016835438,0.00046309776,0.00018342344,0.0006168018,0.00006805528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093611644,0.0005526767,0.00050602056,0.0027898094,0.0005867763,0.0007988563,0.0005003185,0.000594276,0.0015291863],"category_scores_gemma":[0.0039313217,0.00022253684,0.00068010436,0.0014381908,0.00048966525,0.0011868057,0.0005677081,0.00041142743,0.00022465445],"study_design_candidate":"simulation_or_modeling","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":[0.00033394026,0.00021078165,0.04360521,0.00061146845,0.000116280746,0.0007638044,0.0013916115,0.43063614,0.036311153,0.013762963,0.0014980186,0.47075868],"study_design_scores_gemma":[0.000015639209,0.00018830653,0.010032064,0.000046956553,0.000048109447,0.00025354716,0.00039780507,0.95365894,0.013057415,0.019456577,0.002797327,0.00004739376],"about_ca_topic_score_codex":0.004674025,"about_ca_topic_score_gemma":0.00370038,"teacher_disagreement_score":0.995326,"about_ca_system_score_codex":0.0008248928,"about_ca_system_score_gemma":0.0009084619,"threshold_uncertainty_score":0.009293616},"labels":[],"label_agreement":null},{"id":"W2036956267","doi":"10.1139/l03-077","title":"Analysis of effects of air pocket on hydraulic failure of urban drainage infrastructure","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":59,"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":"Sanitary sewer; Drainage; Environmental science; Pipeline transport; Storm; Flow (mathematics); Transient (computer programming); Atmospheric pressure; Drainage system (geomorphology); Geotechnical engineering; Engineering; Environmental engineering; Meteorology; Mechanics; Geography","score_opus":0.0016751419080252438,"score_gpt":0.14385025459280157,"score_spread":0.1421751126847763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036956267","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":[0.9971494,0.000014997296,0.002315475,0.000013257401,0.0000019090319,0.000006201743,0.00004286935,0.000040120874,0.00041578722],"genre_scores_gemma":[0.9997731,0.000006485473,0.00011174256,7.6572536e-7,4.0404296e-7,0.0000014835268,0.000010381285,0.000002345628,0.00009332381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999168,0.000019819403,0.000003806984,0.000010680108,0.000016899246,0.000031938715],"domain_scores_gemma":[0.99926347,0.0005260535,0.000085722204,0.000030665953,0.00005568308,0.000038401417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023397984,0.00025872275,0.0003451744,0.00033279203,0.00023979774,0.00026490304,0.00025836556,0.0004048713,0.0015252364],"category_scores_gemma":[0.00092238624,0.00014693648,0.00029889765,0.00019649377,0.0005645087,0.00025272998,0.00028689537,0.00024927148,0.000058215384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004068382,0.00008098203,0.011696665,0.000102762046,0.00003764894,0.0007940018,0.00012508627,0.95442563,0.025366487,0.0004825779,0.00019180306,0.006289538],"study_design_scores_gemma":[0.000017818158,0.00032480518,0.016400363,0.0000037618063,0.000031425272,0.00007106264,0.00014867574,0.97432566,0.008348652,0.0002003188,0.00011382537,0.0000136843455],"about_ca_topic_score_codex":0.007131567,"about_ca_topic_score_gemma":0.004521786,"teacher_disagreement_score":0.007131567,"about_ca_system_score_codex":0.00038602718,"about_ca_system_score_gemma":0.0003341298,"threshold_uncertainty_score":0.014180124},"labels":[],"label_agreement":null},{"id":"W2036985034","doi":"10.1139/s06-005","title":"Residence time distribution analysis of an intermittently operating truck fill facility to determine effective contact time","year":2006,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Water Systems and 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":"U.S. Environmental Protection Agency","keywords":"Truck; Residence time distribution; Residence time (fluid dynamics); Environmental science; Residence; Schedule; Transport engineering; Environmental engineering; Flow (mathematics); Waste management; Engineering; Computer science; Automotive engineering; Mathematics","score_opus":0.002434595817092998,"score_gpt":0.16807370506137745,"score_spread":0.16563910924428446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036985034","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":[0.9889557,0.00003278264,0.010289445,0.000009828735,0.0000016029644,0.00002039039,0.00016570436,0.00008457613,0.000439905],"genre_scores_gemma":[0.99550796,0.000029729854,0.0035214387,0.0000061901055,7.6634126e-7,0.00001612312,0.00014738744,0.000017151648,0.0007533645],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998184,0.000016774226,0.0000065476165,0.000038187118,0.000083787745,0.00003635829],"domain_scores_gemma":[0.99936193,0.00018298173,0.00012446512,0.00003947969,0.00024126907,0.00004985606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004013213,0.00019169707,0.00024129974,0.0005626351,0.00024566255,0.00030899164,0.00046974313,0.00029832515,0.0015717646],"category_scores_gemma":[0.0006995965,0.00012710532,0.00020126652,0.00049792207,0.00025763916,0.0002475834,0.00019941876,0.0002927132,0.00022672451],"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":[0.00039964044,0.0001245186,0.015478942,0.00005918699,0.000008104863,0.00021689833,0.00034036968,0.0027485243,0.9717129,0.00022489682,0.00010702467,0.008579024],"study_design_scores_gemma":[0.000017303171,0.003037272,0.03859595,0.0000115627745,0.000026551741,0.00037471304,0.00033601362,0.02977096,0.92617625,0.00006725985,0.0015489532,0.00003722385],"about_ca_topic_score_codex":0.007896892,"about_ca_topic_score_gemma":0.005391176,"teacher_disagreement_score":0.007896892,"about_ca_system_score_codex":0.0006162085,"about_ca_system_score_gemma":0.00046660472,"threshold_uncertainty_score":0.01570189},"labels":[],"label_agreement":null},{"id":"W2037157817","doi":"10.1139/l99-075","title":"Développement d'une méthodologie de repérage des conduites d'aqueduc présentant des fuites","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"Leak; Leak detection; Context (archaeology); Computer science; Identification (biology); Key (lock); Engineering; Computer security; Geology; Environmental engineering","score_opus":0.028880213194492296,"score_gpt":0.21911249845439176,"score_spread":0.19023228525989946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037157817","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":[0.002218223,0.000075636926,0.9968816,0.00003550526,0.000026155558,0.000038480288,0.000025083773,0.00018883562,0.00051054056],"genre_scores_gemma":[0.049131397,0.00030254255,0.9473312,0.00003973444,0.00004228915,0.00022845759,0.00013007467,0.00020179914,0.0025924684],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982685,0.00047990485,0.00010037766,0.00041607168,0.0006219427,0.000113328955],"domain_scores_gemma":[0.9951449,0.0034280522,0.00016281329,0.00035867805,0.00083324267,0.000072319854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022102052,0.0014342557,0.0014907974,0.0021756503,0.0008147202,0.0030531106,0.001645299,0.0026458788,0.007550581],"category_scores_gemma":[0.008088254,0.00094352977,0.0024809558,0.0012802406,0.0018866747,0.0020612834,0.0015650629,0.0029975092,0.0021546255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024806577,0.00022209385,0.0050379313,0.0011893413,0.00028372585,0.0006941539,0.0014601821,0.22916767,0.06991917,0.06634602,0.0026641402,0.62276757],"study_design_scores_gemma":[0.00007365986,0.00012957382,0.0016376635,0.00008587124,0.00004908274,0.0004102998,0.0003416657,0.9497488,0.017128328,0.015016813,0.015279332,0.000098870754],"about_ca_topic_score_codex":0.010449986,"about_ca_topic_score_gemma":0.0056195385,"teacher_disagreement_score":0.010449986,"about_ca_system_score_codex":0.0009648858,"about_ca_system_score_gemma":0.0017081439,"threshold_uncertainty_score":0.025259137},"labels":[],"label_agreement":null},{"id":"W2038088158","doi":"10.1061/(asce)0733-9429(2006)132:8(778)","title":"Modeling Ventilation Phenomenon in Sanitary Sewer Systems: A System Theoretic Approach","year":2006,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":51,"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":"Sanitary sewer; Piping; Cabin pressurization; Environmental engineering; Environmental science; Drag; Sewage; Civil engineering; Wastewater; Combined sewer; Engineering; Waste management; Stormwater; Surface runoff; Mechanical engineering","score_opus":0.004386570061172717,"score_gpt":0.15354460863012995,"score_spread":0.14915803856895724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038088158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13467216,0.0006472842,0.85404104,0.00050249795,0.00004080714,0.00008501289,0.00013421656,0.00024534,0.009631712],"genre_scores_gemma":[0.9835512,0.00042552562,0.012741067,0.000034079843,0.00003263111,0.00012052719,0.00004942163,0.00003785741,0.0030076418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970007,0.0001377686,0.000011037617,0.000051812083,0.00004978373,0.000049499493],"domain_scores_gemma":[0.9995388,0.00030242893,0.000067057736,0.000015559863,0.000052893265,0.000023235967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061549916,0.0008184092,0.00085687294,0.00055412855,0.00054369896,0.0013635526,0.0011613142,0.0018281717,0.0014436528],"category_scores_gemma":[0.0013193168,0.000500462,0.0008127077,0.00040563487,0.0012887378,0.0013457782,0.00089449337,0.00075189373,0.0001608937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076606475,0.000010452625,0.00020322889,0.000017199922,0.000008574757,0.000029797213,0.000024796554,0.9916931,0.0006694936,0.0063934117,0.00006197705,0.0008801498],"study_design_scores_gemma":[0.000002119965,0.000007664562,0.000044534252,0.0000015215272,0.000002732824,0.000002985325,0.000008013715,0.9982772,0.000079712256,0.0014692394,0.000102198166,0.0000020428863],"about_ca_topic_score_codex":0.008063952,"about_ca_topic_score_gemma":0.0036897755,"teacher_disagreement_score":0.008063952,"about_ca_system_score_codex":0.0012958341,"about_ca_system_score_gemma":0.0010339156,"threshold_uncertainty_score":0.016034007},"labels":[],"label_agreement":null},{"id":"W2038961450","doi":"10.5942/jawwa.2012.104.0135","title":"Survey of water main failures in the United States and Canada","year":2012,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and Optimization","field":"Engineering","cited_by":27,"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":"Water Research Foundation","keywords":"Environmental science; Water resource management; Environmental planning; Business; Environmental protection","score_opus":0.004765428575005085,"score_gpt":0.17511426225784876,"score_spread":0.17034883368284368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038961450","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":[0.84300494,0.00656535,0.0006199088,0.0026806535,0.000049691815,0.00026433077,0.11525537,0.00013878771,0.03142102],"genre_scores_gemma":[0.94912124,0.01199992,0.00066438684,0.0011233184,0.000018979017,0.000104828134,0.027655607,0.00003176127,0.009279963],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99792933,0.00007825317,0.0001448457,0.00015065218,0.0011901686,0.0005066632],"domain_scores_gemma":[0.9919475,0.0003321361,0.0010444266,0.00011465722,0.005543362,0.0010179543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007196475,0.00041084347,0.00045472817,0.0047814446,0.0022405728,0.0012101757,0.0009907023,0.00043171627,0.003024],"category_scores_gemma":[0.0028819195,0.0003896152,0.0005092009,0.015860204,0.0005505484,0.0004960558,0.00090133556,0.0005972907,0.0004952047],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068136345,0.00004448133,0.94897234,0.00021754012,0.00010630113,0.00022214041,0.0010574905,0.00070087257,0.00029657237,0.00039629033,0.028362868,0.019554898],"study_design_scores_gemma":[0.000004017308,0.000016132512,0.98203754,0.00011383785,0.000026761465,0.00009220128,0.002302714,0.00034134634,0.00012134178,0.00003653849,0.014883101,0.000024457366],"about_ca_topic_score_codex":0.99636614,"about_ca_topic_score_gemma":0.9978516,"teacher_disagreement_score":0.020945879,"about_ca_system_score_codex":0.020945879,"about_ca_system_score_gemma":0.04180436,"threshold_uncertainty_score":0.15197366},"labels":[],"label_agreement":null},{"id":"W2039267090","doi":"10.1111/j.1467-8667.2005.00417.x","title":"Fuzzy Expert System to Assess Corrosion of Cast/Ductile Iron Pipes from Backfill Properties","year":2005,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Knowledge base; Fuzzy logic; Expert system; Computer science; Context (archaeology); Mains electricity; Data mining; Inference; Inference engine; Process (computing); Artificial intelligence; Engineering; Geology; Programming language","score_opus":0.008181363677682265,"score_gpt":0.17087365205501875,"score_spread":0.16269228837733649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039267090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48144385,0.00022072069,0.50976247,0.00009160518,0.000065392014,0.00019077124,0.00040172198,0.002007886,0.005815507],"genre_scores_gemma":[0.9421586,0.000051379844,0.05515483,0.000031225896,0.0000079486845,0.00008281171,0.00016513803,0.000013506141,0.0023345833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983823,0.000031459378,0.000015368381,0.000042735286,0.000055786193,0.000016423053],"domain_scores_gemma":[0.9994518,0.00019310607,0.000033956727,0.000036438734,0.0002578174,0.000026808599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004852816,0.00045010998,0.00055455044,0.0004546762,0.0003332327,0.00037153257,0.00040409915,0.0008505945,0.0021407625],"category_scores_gemma":[0.0013209216,0.00021330449,0.00030751666,0.00018494092,0.000119274904,0.00024715462,0.00018582809,0.00026460493,0.00034827655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002073697,0.00058670697,0.008341462,0.00042470422,0.00018762246,0.00050833833,0.00025636316,0.43841013,0.12642576,0.0010906429,0.0035525272,0.41814208],"study_design_scores_gemma":[0.00007146654,0.00029388786,0.0039622476,0.000011919588,0.000056898967,0.00006448764,0.000035435634,0.97875655,0.015788157,0.00034593564,0.0005960817,0.00001695948],"about_ca_topic_score_codex":0.007594659,"about_ca_topic_score_gemma":0.010587998,"teacher_disagreement_score":0.007594659,"about_ca_system_score_codex":0.00040547075,"about_ca_system_score_gemma":0.00045365986,"threshold_uncertainty_score":0.015100896},"labels":[],"label_agreement":null},{"id":"W2040841078","doi":"10.1080/15732479.2014.896021","title":"Infrastructure performance rating models for wastewater treatment plants","year":2014,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Analytic hierarchy process; Unit (ring theory); Business; Environmental economics; Operations management; Engineering; Operations research; Economics; Mathematics","score_opus":0.003660817218011138,"score_gpt":0.16035241961523017,"score_spread":0.15669160239721902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040841078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15368155,0.0007393807,0.82193315,0.00056425907,0.00007448526,0.00039786642,0.0011512301,0.00044365108,0.02101448],"genre_scores_gemma":[0.9615847,0.0003746839,0.031890515,0.00003290821,0.00002637288,0.0002845062,0.00067282596,0.000038882885,0.0050946637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855,0.0006938697,0.00006374831,0.00022347788,0.00023870364,0.00023012192],"domain_scores_gemma":[0.9967706,0.001984808,0.00039698184,0.000113852235,0.0006170056,0.00011665113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003144381,0.001038727,0.00068424473,0.0010911054,0.00039706015,0.0014320095,0.0016618504,0.0011318022,0.003670613],"category_scores_gemma":[0.007681488,0.00032642146,0.0011768467,0.0012084248,0.0005075935,0.0010396156,0.00083964836,0.0014854536,0.0007213627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002097357,0.000040805902,0.0014351484,0.000032414286,0.000014634946,0.000031131338,0.00005278048,0.98433775,0.00014272421,0.005573302,0.00055905496,0.0077593382],"study_design_scores_gemma":[0.0000017011176,0.000015169548,0.0005438292,0.000004031788,0.0000033596345,0.0000052543983,0.00001792387,0.99766195,0.000031810036,0.0014968388,0.00021452259,0.0000035855126],"about_ca_topic_score_codex":0.03008678,"about_ca_topic_score_gemma":0.014297818,"teacher_disagreement_score":0.03008678,"about_ca_system_score_codex":0.0022914722,"about_ca_system_score_gemma":0.0011415624,"threshold_uncertainty_score":0.059823334},"labels":[],"label_agreement":null},{"id":"W2042095761","doi":"10.1097/00005768-200305001-00183","title":"COLD WATER IMMERSION FOLLOWING INTENSE INTERVAL RUNNING IMPROVES SUBSEQUENT RUNNING PERFORMANCE","year":2003,"lang":"en","type":"article","venue":"Medicine & Science in Sports & Exercise","topic":"Water Systems and 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":"Acadia University","funders":"","keywords":"Treadmill; Medicine; Vertical jump; Cryotherapy; Crossover study; Physical therapy; Interval training; Jump; Surgery; Physics","score_opus":0.009714080011383922,"score_gpt":0.21837881045810736,"score_spread":0.20866473044672346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042095761","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":[0.9991709,0.00021149922,0.00031811045,0.000013198093,0.00001207961,0.000010453185,0.000018199444,0.000015255358,0.00023030653],"genre_scores_gemma":[0.9981668,0.00014281654,0.00049469195,0.000018916193,0.000016017568,0.00001490583,0.000091706694,0.0000070174788,0.0010471535],"study_design_codex":"bench_or_experimental","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.99989295,0.000014543047,0.000007689857,0.000030245777,0.000012805023,0.000041740685],"domain_scores_gemma":[0.9998672,0.000014292844,0.00003191777,0.000012739408,0.000016197751,0.00005754262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015638312,0.00039225767,0.00041790554,0.00010499315,0.00013912478,0.00020488143,0.00016629197,0.00018800328,0.0016360357],"category_scores_gemma":[0.00025911623,0.00009056917,0.00015403637,0.000055153876,0.00012635042,0.00015981682,0.00021579071,0.00028821462,0.00018812233],"study_design_candidate":"nonrandomized_trial","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":[0.013973273,0.0064752707,0.017320184,0.00026380847,0.00013351183,0.00008057051,0.0002136551,0.00051076093,0.87665665,0.000032334032,0.00026541718,0.08407457],"study_design_scores_gemma":[0.00043670821,0.10605165,0.80118567,0.00007890389,0.00025168055,0.0002915058,0.00034593622,0.0024856308,0.0870752,0.00009321289,0.0016747508,0.000029151759],"about_ca_topic_score_codex":0.00091865787,"about_ca_topic_score_gemma":0.001980413,"teacher_disagreement_score":0.0016360357,"about_ca_system_score_codex":0.000088815104,"about_ca_system_score_gemma":0.00012150051,"threshold_uncertainty_score":0.0054730773},"labels":[],"label_agreement":null},{"id":"W2044018076","doi":"10.1002/cjce.5450790318","title":"Scale‐up of single phase axial dispersion coefficients in batch and continuous oscillatory baffled tubes","year":2001,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Baffle; Mechanics; Dispersion (optics); Mixing (physics); Flow (mathematics); Scale (ratio); Phase (matter); Materials science; Physics; Thermodynamics; Optics","score_opus":0.0059035899146250214,"score_gpt":0.17572318675304488,"score_spread":0.16981959683841985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044018076","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":[0.9568194,0.00034354077,0.041432135,0.00003422561,0.000022647413,0.000074677795,0.00013119592,0.00023469151,0.0009074395],"genre_scores_gemma":[0.9818554,0.00014088186,0.017255547,0.000008488099,0.0000066183734,0.000047583584,0.000109917266,0.000021769318,0.00055378664],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917907,0.00011053891,0.00004115305,0.00014653889,0.000469115,0.00005357239],"domain_scores_gemma":[0.99775934,0.0010115564,0.00020152041,0.00028675114,0.00065092975,0.00008983982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011199948,0.0003084886,0.0003728301,0.0006005873,0.0002276753,0.00059107196,0.00051936455,0.00033323746,0.00056660885],"category_scores_gemma":[0.0035142174,0.0002379325,0.00030324506,0.00060747814,0.0004579013,0.0005477309,0.0003542771,0.00073206966,0.00020030356],"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":[0.00035876976,0.00020692154,0.0045535425,0.00006302114,0.000022549879,0.000047806665,0.00011937501,0.0062619387,0.9606445,0.0005969349,0.00014190904,0.026982775],"study_design_scores_gemma":[0.000028896693,0.00058952905,0.009715767,0.0000074573895,0.000022558852,0.000037612594,0.000038459923,0.037296616,0.9513098,0.00014979081,0.0007764581,0.000027076198],"about_ca_topic_score_codex":0.0023802265,"about_ca_topic_score_gemma":0.0021631317,"teacher_disagreement_score":0.0023802265,"about_ca_system_score_codex":0.0005912027,"about_ca_system_score_gemma":0.00038564572,"threshold_uncertainty_score":0.0059232116},"labels":[],"label_agreement":null},{"id":"W2044780844","doi":"10.2166/aqua.2013.305","title":"A decision support tool for water mains renewal for small to medium sized utilities: a risk index approach","year":2013,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Kelowna General Hospital; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Columbia university; Index (typography); Library science; Engineering; History; Management; Media studies; Sociology; Computer science; World Wide Web","score_opus":0.022148767066821565,"score_gpt":0.258340634073638,"score_spread":0.23619186700681644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044780844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10317968,0.0020957016,0.77989995,0.008352012,0.00041965887,0.004475486,0.009015744,0.007664252,0.08489753],"genre_scores_gemma":[0.29056433,0.0010927558,0.6940388,0.00052654406,0.00013179825,0.001075606,0.003592452,0.00032559744,0.0086520845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991,0.003020022,0.0010529684,0.00069438975,0.0038411715,0.00039143852],"domain_scores_gemma":[0.9722012,0.018847676,0.0015309615,0.0010642238,0.005530382,0.00082548475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011819535,0.0018475411,0.0022879415,0.011215207,0.001578035,0.010362018,0.0025438406,0.0019444627,0.015162303],"category_scores_gemma":[0.031575564,0.0008350373,0.0036236455,0.0061932043,0.0006529239,0.007548073,0.0034103114,0.0023471084,0.0031356523],"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":[0.0014475668,0.0018562406,0.018194584,0.0028907633,0.0011317477,0.0012784268,0.0022074198,0.1360678,0.004417893,0.061278626,0.04252158,0.72670746],"study_design_scores_gemma":[0.00045150725,0.0009714063,0.00812488,0.0015797237,0.0009447852,0.00068596605,0.004863593,0.81469166,0.0049254633,0.091247566,0.07114744,0.00036600858],"about_ca_topic_score_codex":0.0073503237,"about_ca_topic_score_gemma":0.010514645,"teacher_disagreement_score":0.015162303,"about_ca_system_score_codex":0.0039177346,"about_ca_system_score_gemma":0.004469082,"threshold_uncertainty_score":0.062508464},"labels":[],"label_agreement":null},{"id":"W2045092222","doi":"10.1061/40994(321)79","title":"Assessing Performance of a Water Transmission System Using an Inverse Transient Method","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Regional Municipality of Ottawa; Research Canada; Brock University; University of Toronto","funders":"","keywords":"Transient (computer programming); Computer science; General partnership; Transient response; Engineering; Environmental science; Electrical engineering; Business","score_opus":0.03852197268507599,"score_gpt":0.24507901554085743,"score_spread":0.20655704285578144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045092222","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":[0.5559974,0.00011962377,0.43774232,0.00010647618,0.00001584988,0.0000809425,0.000116919466,0.00087128504,0.004949157],"genre_scores_gemma":[0.98524576,0.0000382896,0.014159779,0.000005648373,0.0000027743692,0.000016611404,0.000033302767,0.000015766498,0.00048198533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995222,0.00014118914,0.000027540646,0.00006891419,0.00020055602,0.000039669234],"domain_scores_gemma":[0.99924076,0.0004669151,0.00007719309,0.00006897394,0.00012044064,0.000025776997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064043415,0.00036679563,0.00036168814,0.00076553226,0.00029420725,0.0006509471,0.0003448619,0.0006393462,0.0011960941],"category_scores_gemma":[0.002142558,0.0001495729,0.0003019546,0.0003648771,0.000384308,0.00071969145,0.00033824518,0.00031412576,0.00021504045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007787689,0.00019467452,0.023112778,0.00024914072,0.00009522033,0.00035520972,0.00055886945,0.66168517,0.16352403,0.0021203635,0.0005515265,0.14677434],"study_design_scores_gemma":[0.0000107155365,0.0003693146,0.0077365725,0.000008685952,0.00002624985,0.00009801806,0.00012729487,0.97172236,0.019104727,0.0004220266,0.00034814028,0.00002584138],"about_ca_topic_score_codex":0.0031087527,"about_ca_topic_score_gemma":0.0016154265,"teacher_disagreement_score":0.0031087527,"about_ca_system_score_codex":0.00030051835,"about_ca_system_score_gemma":0.00029859858,"threshold_uncertainty_score":0.0061812997},"labels":[],"label_agreement":null},{"id":"W2047197795","doi":"10.1016/j.proeng.2014.11.509","title":"The Effect of Future Water Demand Reduction on WDS Rehabilitation Planning","year":2014,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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":"Queen's University; HydraTek (Canada)","funders":"","keywords":"Reduction (mathematics); Rehabilitation; On demand; Business; Environmental science; Water resource management; Physical therapy; Medicine; Mathematics","score_opus":0.0012362883701608346,"score_gpt":0.15830224480973812,"score_spread":0.15706595643957727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047197795","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":[0.95274216,0.00018947212,0.02008728,0.0008794838,0.00005247655,0.00009757412,0.0005736013,0.00012121116,0.025256708],"genre_scores_gemma":[0.99569464,0.0000501086,0.002460718,0.00002131511,0.0000036272454,0.00001480634,0.000100272024,0.000013710457,0.0016406797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994254,0.0002247534,0.000015722977,0.00004474585,0.000120652374,0.00016872081],"domain_scores_gemma":[0.99902487,0.0005590851,0.000092472605,0.00004282466,0.00019349354,0.00008722561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011097129,0.00037979023,0.00035112732,0.00047378926,0.00043383244,0.0009902295,0.0007048895,0.0007800683,0.005259699],"category_scores_gemma":[0.0026250042,0.0003187043,0.00046028156,0.00037301515,0.00041209688,0.0010306392,0.00049800746,0.0007409581,0.0002867518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023275027,0.00011065789,0.0047740065,0.000046765326,0.000023698269,0.00015366243,0.000024293853,0.9781074,0.0023822424,0.0022648952,0.00052539894,0.01135428],"study_design_scores_gemma":[0.000041287163,0.00045162032,0.009499033,0.000018736842,0.00006103582,0.000058712918,0.00042403332,0.98051286,0.004182884,0.002134961,0.0025882279,0.00002659144],"about_ca_topic_score_codex":0.023417866,"about_ca_topic_score_gemma":0.02689104,"teacher_disagreement_score":0.023417866,"about_ca_system_score_codex":0.0021070933,"about_ca_system_score_gemma":0.0018645223,"threshold_uncertainty_score":0.04656309},"labels":[],"label_agreement":null},{"id":"W2047887886","doi":"10.1115/power2011-55202","title":"CFD Analysis of Ventilation Flow for a Scale Model Hydro-Generator","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":24,"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; Hydro-Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computational fluid dynamics; Rotor (electric); Stator; Particle image velocimetry; Simulation; Parametric statistics; Computer science; Mechanics; Interface (matter); Transient (computer programming); Mechanical engineering; Engineering; Turbulence; Physics; Mathematics","score_opus":0.022705729680130328,"score_gpt":0.19028022604016248,"score_spread":0.16757449636003216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047887886","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":[0.95872414,0.00014607004,0.029783303,0.00016658004,0.000033664135,0.00007076944,0.00077142013,0.0004351179,0.009868797],"genre_scores_gemma":[0.9933443,0.00005546759,0.0045101442,0.00001332533,0.0000032376474,0.000029359275,0.0002616629,0.00002855254,0.0017540054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994266,0.000007993045,0.0000029090259,0.000012196823,0.000023332774,0.000010915055],"domain_scores_gemma":[0.99991477,0.000042958443,0.000009211008,0.000008251428,0.000016699829,0.00000820996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009497665,0.00026002227,0.00034310741,0.00021294417,0.00024946718,0.0004471507,0.0003045044,0.0006163369,0.0019662655],"category_scores_gemma":[0.00029302452,0.00012759726,0.00033620434,0.00019590768,0.00035457476,0.00019572153,0.00020026592,0.00023696353,0.00023074728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013580594,0.00006724908,0.004691234,0.0000717989,0.00001174923,0.0003125037,0.000099285106,0.9425773,0.040545024,0.002054833,0.00072754343,0.008705609],"study_design_scores_gemma":[0.000016315336,0.000045646088,0.0020969056,0.000004845431,0.000003397473,0.00002748432,0.000025842655,0.99185854,0.0050797896,0.00022027217,0.00061250554,0.0000085205],"about_ca_topic_score_codex":0.0055979034,"about_ca_topic_score_gemma":0.0026845094,"teacher_disagreement_score":0.0055979034,"about_ca_system_score_codex":0.00052011013,"about_ca_system_score_gemma":0.0005540866,"threshold_uncertainty_score":0.011130631},"labels":[],"label_agreement":null},{"id":"W2047995589","doi":"10.1139/l01-024","title":"Scale model testing and calibration of City of Ottawa sewer weirs","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":true,"ca_institutions":"","funders":"","keywords":"Weir; Calibration; Hydraulics; Discharge coefficient; Hydrology (agriculture); Flow (mathematics); Geotechnical engineering; Sanitary sewer; Flow measurement; Engineering; Geology; Mechanics; Environmental engineering; Geography; Mathematics","score_opus":0.01039429395564108,"score_gpt":0.16108303544956684,"score_spread":0.15068874149392575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047995589","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":[0.9963595,0.000012550168,0.002770011,0.000016078415,0.0000032715102,0.000032759854,0.00024852215,0.00012681412,0.0004302764],"genre_scores_gemma":[0.9974183,0.000014922177,0.0019325287,0.0000038106182,5.1084453e-7,0.000022281421,0.00030668982,0.000010765152,0.00029023408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993555,0.00009527824,0.000038194124,0.00019730291,0.00021364377,0.00010002401],"domain_scores_gemma":[0.99873763,0.00034868837,0.00013156443,0.0002709413,0.00045567186,0.00005559394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007662873,0.00057308056,0.00037624422,0.00037403748,0.0006449426,0.0005250601,0.0011077282,0.0005531779,0.0008429298],"category_scores_gemma":[0.002436503,0.00032962312,0.0004864429,0.0007395384,0.0007145416,0.00044487842,0.00052116514,0.0005436976,0.0001763778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062236696,0.00056532916,0.07991128,0.00013441234,0.00009970595,0.00017076956,0.0005425956,0.82098377,0.06924121,0.0006893683,0.0007566365,0.026282566],"study_design_scores_gemma":[0.00018575703,0.0017075341,0.16341884,0.00001585335,0.00012170549,0.00010852454,0.00063716865,0.6873759,0.14341086,0.00026835155,0.0026236179,0.00012589776],"about_ca_topic_score_codex":0.24465582,"about_ca_topic_score_gemma":0.3125758,"teacher_disagreement_score":0.24465582,"about_ca_system_score_codex":0.0037421926,"about_ca_system_score_gemma":0.0017108,"threshold_uncertainty_score":0.48646355},"labels":[],"label_agreement":null},{"id":"W2048288397","doi":"10.1016/j.watres.2011.12.006","title":"An integrated performance assessment framework for water treatment plants","year":2011,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; Brock University; University of Calgary","funders":"","keywords":"Robustness (evolution); Environmental science; Reliability engineering; Turbidity; Flocculation; Water treatment; Environmental engineering; Process engineering; Computer science; Engineering; Ecology; Biology","score_opus":0.11222473136793129,"score_gpt":0.3464663634152916,"score_spread":0.2342416320473603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048288397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065665077,0.0002243566,0.9862758,0.0002580561,0.000026170415,0.000119820485,0.00013294513,0.00052535563,0.005870927],"genre_scores_gemma":[0.5561958,0.00065440283,0.4359706,0.00013791652,0.00009610008,0.0005901644,0.00058448536,0.00022257812,0.00554794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968094,0.0009804332,0.00016126865,0.00030489013,0.0014092867,0.0003347225],"domain_scores_gemma":[0.9987367,0.0003287993,0.00012625814,0.00010799184,0.00062380626,0.00007655226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004971839,0.0021318204,0.0018508619,0.0017262816,0.0007552051,0.0034785662,0.002728786,0.0015812824,0.0031807094],"category_scores_gemma":[0.0037272256,0.0006646269,0.0017318199,0.0013034808,0.00084688474,0.0029314377,0.002608223,0.0015150785,0.000589753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028253187,0.000082096296,0.0003632474,0.00007331486,0.00009128812,0.00004785608,0.0000479202,0.92481977,0.0010390998,0.040704466,0.00094947586,0.031753253],"study_design_scores_gemma":[0.000009553651,0.00004978846,0.00016762454,0.000019820334,0.00003349187,0.000016253649,0.000024955665,0.97439176,0.0005216007,0.023216797,0.0015351269,0.00001315327],"about_ca_topic_score_codex":0.013433859,"about_ca_topic_score_gemma":0.0116006965,"teacher_disagreement_score":0.013433859,"about_ca_system_score_codex":0.0023528119,"about_ca_system_score_gemma":0.004521627,"threshold_uncertainty_score":0.026711345},"labels":[],"label_agreement":null},{"id":"W2048616273","doi":"10.1139/l09-100","title":"Multi-stage response to contaminant ingress into water distribution systems and probability quantification","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"ALARM; Euclidean distance; Metric (unit); False alarm; Node (physics); Stage (stratigraphy); Probability distribution; Identification (biology); Computer science; Point (geometry); Data mining; Real-time computing; Statistics; Engineering; Mathematics; Artificial intelligence; Structural engineering","score_opus":0.014104546645756963,"score_gpt":0.20408084621144487,"score_spread":0.1899762995656879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048616273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056791414,0.00006359135,0.9395094,0.00009247353,0.000011952495,0.00029302752,0.00011907765,0.002440062,0.00067906466],"genre_scores_gemma":[0.57040584,0.00006489565,0.42612085,0.000085985805,0.000014994882,0.00053052255,0.00029512233,0.00012631877,0.002355354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792886,0.00059824524,0.000110028945,0.00038293606,0.00078880717,0.00019111183],"domain_scores_gemma":[0.99533445,0.0027961559,0.0005273117,0.0004066934,0.0008113759,0.00012409221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019873797,0.00088745187,0.001094558,0.0011541063,0.00051770156,0.00066156,0.0014194113,0.00074511283,0.0027721557],"category_scores_gemma":[0.0064377515,0.00063235534,0.0008726826,0.00077757274,0.0005698313,0.0009889007,0.001278235,0.00089275633,0.000540492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009153564,0.0005088947,0.009874302,0.00046142528,0.000112851434,0.00032575408,0.00038380997,0.5480014,0.10583688,0.0031139127,0.0018436051,0.32862177],"study_design_scores_gemma":[0.000026902288,0.0005726354,0.004514576,0.0000072052126,0.000022827342,0.00013541848,0.000118523254,0.9423509,0.049016047,0.001967026,0.0012152346,0.000052658062],"about_ca_topic_score_codex":0.0042764805,"about_ca_topic_score_gemma":0.004630591,"teacher_disagreement_score":0.0042764805,"about_ca_system_score_codex":0.00072601344,"about_ca_system_score_gemma":0.0011588229,"threshold_uncertainty_score":0.010510385},"labels":[],"label_agreement":null},{"id":"W2050732669","doi":"10.2495/ws130161","title":"Optimal operation of water pumping stations","year":2013,"lang":"en","type":"article","venue":"WIT transactions on ecology and the environment","topic":"Water Systems and 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":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Electricity; Reliability (semiconductor); Energy consumption; Scheduling (production processes); Differential evolution; Total cost; Water supply; Mathematical optimization; Reliability engineering; Environmental science; Engineering; Environmental engineering; Power (physics); Business","score_opus":0.0033869976263348625,"score_gpt":0.14441013082070786,"score_spread":0.14102313319437298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050732669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24833032,0.0004948152,0.7332352,0.00029696216,0.00006676531,0.00020490849,0.00022171195,0.0004240815,0.016725264],"genre_scores_gemma":[0.90923715,0.00019275163,0.08837633,0.00003363173,0.000009304558,0.00009162983,0.00008594275,0.000024659646,0.0019486794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997733,0.000051118666,0.000011714639,0.000061546554,0.00004492162,0.00005736715],"domain_scores_gemma":[0.99976367,0.00009813692,0.00005116607,0.000015582758,0.00005066579,0.000020852493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037868283,0.0005463892,0.0005654427,0.00054102007,0.00046206504,0.0010589149,0.0005780942,0.000708961,0.0020385056],"category_scores_gemma":[0.0013506486,0.00044024625,0.0003585002,0.00047892865,0.00037139663,0.0007915421,0.00051953556,0.00043183027,0.00022449042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105243154,0.000046099252,0.0012546479,0.0000725037,0.00001957302,0.00007476035,0.00005224831,0.94895214,0.006514374,0.006561577,0.0005850925,0.035761803],"study_design_scores_gemma":[0.00002496434,0.000064884174,0.00040345927,0.000008082867,0.000012524268,0.000016148777,0.000039177572,0.99317366,0.002769914,0.0027524726,0.00072590716,0.0000086819455],"about_ca_topic_score_codex":0.004538619,"about_ca_topic_score_gemma":0.005655075,"teacher_disagreement_score":0.004538619,"about_ca_system_score_codex":0.0008705254,"about_ca_system_score_gemma":0.0016167322,"threshold_uncertainty_score":0.009024441},"labels":[],"label_agreement":null},{"id":"W2052430884","doi":"10.1115/detc2013-12667","title":"Indexing Methods for Discrete Mode Pursuing Sampling","year":2013,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Search engine indexing; Sampling (signal processing); Computer science; Algorithm; Grid; Discrete space; Mode (computer interface); Data structure; Data point; Data mining; Mathematics; Artificial intelligence; Geometry; Mathematical analysis","score_opus":0.026942234836132513,"score_gpt":0.3227952094438368,"score_spread":0.29585297460770427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052430884","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":[0.003091568,0.00013736894,0.9941988,0.00003381358,0.000036542344,0.000048527865,0.00007123047,0.00050864747,0.0018734907],"genre_scores_gemma":[0.080609754,0.00027136286,0.9159657,0.0000796147,0.000059960592,0.00027341064,0.00046686624,0.00024564154,0.0020276178],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981142,0.0003724403,0.00016015174,0.00020218779,0.0010669979,0.00008400955],"domain_scores_gemma":[0.99732935,0.0012665562,0.00016203125,0.00059083145,0.0005739441,0.00007735465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017725072,0.0005173546,0.0008896955,0.0014164959,0.00049267896,0.0012928027,0.001348982,0.00051468326,0.0049049654],"category_scores_gemma":[0.007923876,0.00032262778,0.0008254498,0.0023396993,0.0007859868,0.0015701939,0.0015874011,0.0011500228,0.001121106],"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":[0.00030754053,0.00013233026,0.0012851381,0.0003067672,0.000045470315,0.00006563023,0.00020752008,0.11877916,0.011183749,0.14707007,0.0054446566,0.71517193],"study_design_scores_gemma":[0.0000634638,0.000117462805,0.00037274888,0.000030273637,0.0000126067935,0.00010563031,0.00004363355,0.9309939,0.008670668,0.045546018,0.014014049,0.00002954444],"about_ca_topic_score_codex":0.0023741121,"about_ca_topic_score_gemma":0.0016949477,"teacher_disagreement_score":0.0049049654,"about_ca_system_score_codex":0.00077070796,"about_ca_system_score_gemma":0.0012782144,"threshold_uncertainty_score":0.016408682},"labels":[],"label_agreement":null},{"id":"W2052603996","doi":"10.1520/gtj101058","title":"Interpreting Slug Tests with Large Data Sets","year":2009,"lang":"en","type":"article","venue":"Geotechnical Testing Journal","topic":"Water Systems and 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":"Polytechnique Montréal","funders":"","keywords":"Graph; Mathematics; Hydraulic conductivity; Algorithm; Data point; Statistics; Linearity; Data mining; Computer science; Soil science; Combinatorics; Geology; Engineering; Soil water","score_opus":0.0320011581602609,"score_gpt":0.2556356899522327,"score_spread":0.2236345317919718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052603996","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":[0.70391536,0.00042363108,0.28518286,0.00037033478,0.00022269272,0.0003502638,0.0026243331,0.0044822907,0.002428195],"genre_scores_gemma":[0.8873446,0.0001757448,0.10926096,0.00014570227,0.00005994415,0.00016511917,0.0019639416,0.0003966906,0.00048721215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99541616,0.0016761497,0.0005696577,0.0005575075,0.0016104562,0.00016999015],"domain_scores_gemma":[0.9625457,0.025834724,0.002794518,0.0039778384,0.0044947793,0.00035246008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00670818,0.0010615365,0.0010703824,0.0034719526,0.00057353027,0.0018136754,0.0011887347,0.0010952662,0.0013433171],"category_scores_gemma":[0.038050946,0.00044294455,0.00062699267,0.0027634704,0.00082724745,0.0011855542,0.0012021781,0.0008444654,0.00071712607],"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":[0.0021624486,0.00089914555,0.27809268,0.0014836045,0.00079402124,0.0054711387,0.002964785,0.23611663,0.1183186,0.0031237681,0.010575261,0.33999795],"study_design_scores_gemma":[0.00012376688,0.0013018288,0.18406431,0.00025619374,0.0002375618,0.0017548621,0.0029999928,0.6556582,0.12312504,0.01712202,0.012989666,0.00036655847],"about_ca_topic_score_codex":0.002873603,"about_ca_topic_score_gemma":0.0032060198,"teacher_disagreement_score":0.00670818,"about_ca_system_score_codex":0.0006065319,"about_ca_system_score_gemma":0.0004514722,"threshold_uncertainty_score":0.035476625},"labels":[],"label_agreement":null},{"id":"W2053508782","doi":"10.1061/41203(425)37","title":"Real-Time Water Quality Assessment with Bayesian Belief Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Turbidity; Water quality; Computer science; Environmental science; Process (computing); Data mining; Bayesian probability; Process engineering; Artificial intelligence; Machine learning; Engineering","score_opus":0.015120336363517499,"score_gpt":0.2110819889021258,"score_spread":0.19596165253860828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053508782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012550204,0.00028459958,0.98536867,0.00021904887,0.000014041171,0.00005100454,0.00006224868,0.00019880934,0.00125143],"genre_scores_gemma":[0.82682335,0.00059667055,0.16998059,0.00011647263,0.00007776446,0.00030313872,0.00024142087,0.000041344214,0.0018193328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975623,0.0013519474,0.000112924055,0.0003318516,0.00047174824,0.00016918928],"domain_scores_gemma":[0.99486154,0.003780298,0.00055585516,0.00012726436,0.0005692627,0.000105693376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041955668,0.0013161803,0.001248814,0.0017496105,0.00050639187,0.002475783,0.001629678,0.0016163429,0.0015268516],"category_scores_gemma":[0.011966047,0.0010393274,0.00095822423,0.0013186705,0.0013014085,0.002946055,0.0014605181,0.0015247043,0.00027198473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054539556,0.000018327515,0.00037075987,0.000027453047,0.00003285646,0.000019520889,0.000030062352,0.9813462,0.00014881014,0.0058065085,0.00013355535,0.012011417],"study_design_scores_gemma":[0.000005740836,0.000007157032,0.00005057241,0.0000051061224,0.0000048158354,0.00000279442,0.0000046040354,0.9932365,0.00006708662,0.0065267244,0.000083978564,0.0000049635487],"about_ca_topic_score_codex":0.017358664,"about_ca_topic_score_gemma":0.009989118,"teacher_disagreement_score":0.017358664,"about_ca_system_score_codex":0.0025498886,"about_ca_system_score_gemma":0.0014805025,"threshold_uncertainty_score":0.03451526},"labels":[],"label_agreement":null},{"id":"W2053694819","doi":"10.1016/j.advwatres.2005.10.013","title":"Improving the efficiency of ant algorithms using adaptive refinement: Application to storm water network design","year":2005,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Water Systems and Optimization","field":"Engineering","cited_by":43,"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":"Atlantic Canada Opportunities Agency","keywords":"Discretization; Continuous optimization; Mathematical optimization; Benchmark (surveying); Convergence (economics); Discrete space; Computer science; Optimization problem; Algorithm; Local search (optimization); Mathematics; Multi-swarm optimization","score_opus":0.009832698057992485,"score_gpt":0.21136097233145001,"score_spread":0.20152827427345754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053694819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110161886,0.00026612126,0.88552636,0.00023267447,0.000059036032,0.00007706421,0.000023746423,0.00048679265,0.0031662881],"genre_scores_gemma":[0.6726722,0.00018435478,0.32565486,0.000058884176,0.000028829083,0.00009078419,0.000036204387,0.00011304235,0.0011607191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953973,0.00022793487,0.000020380252,0.00004129015,0.00012398823,0.00004666483],"domain_scores_gemma":[0.99318284,0.005356659,0.00026352546,0.00034975333,0.00075757393,0.000089572844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021048419,0.0007441874,0.0009640859,0.00070686114,0.0004981765,0.000441074,0.0010544695,0.000814976,0.0010552815],"category_scores_gemma":[0.009450626,0.00040376707,0.00048996473,0.0006866706,0.0008097069,0.000745879,0.0007833439,0.0008588003,0.00017485933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009668669,0.000055847653,0.00064638985,0.000034275447,0.000018160741,0.000020060816,0.000053661373,0.9597226,0.0014367383,0.0022036596,0.00031787262,0.035394087],"study_design_scores_gemma":[0.000012898072,0.000015012852,0.000031099666,0.0000010070373,0.0000026404582,0.000002868665,0.0000027779515,0.99912184,0.00027560836,0.00047880766,0.0000542301,0.0000013046781],"about_ca_topic_score_codex":0.0108904205,"about_ca_topic_score_gemma":0.01009285,"teacher_disagreement_score":0.0108904205,"about_ca_system_score_codex":0.00052959484,"about_ca_system_score_gemma":0.00094206375,"threshold_uncertainty_score":0.02165407},"labels":[],"label_agreement":null},{"id":"W2054083568","doi":"10.1061/(asce)1076-0342(2004)10:2(43)","title":"Quantifying Effectiveness of Cathodic Protection in Water Mains: Theory","year":2004,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Cathodic protection; Mains electricity; Electricity; Computer science; Environmental science; Reliability engineering; Risk analysis (engineering); Engineering; Business; Electrical engineering; Voltage; Electrochemistry","score_opus":0.00843540695914511,"score_gpt":0.20724560581192894,"score_spread":0.19881019885278384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054083568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025440037,0.0017745824,0.9537867,0.00090831675,0.000062618645,0.00014355329,0.00018575264,0.00013443414,0.017564045],"genre_scores_gemma":[0.87881047,0.004528001,0.108933225,0.00028833278,0.00014282686,0.000556887,0.00015128584,0.00009631577,0.0064926744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786204,0.0007355993,0.000090959,0.00032717007,0.00070743955,0.0002767533],"domain_scores_gemma":[0.9930582,0.0052688206,0.0007273437,0.00028314258,0.00055798155,0.000104602135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037369288,0.0015089579,0.0010934689,0.0019731994,0.0005471712,0.0027374327,0.0025018705,0.0029313369,0.0035432477],"category_scores_gemma":[0.01150406,0.00089673465,0.001322572,0.001522373,0.003709714,0.005398575,0.0019266346,0.0016905755,0.0004426917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017607377,0.00006069453,0.0005669516,0.00016520826,0.000029315266,0.000029502284,0.00006396354,0.8912599,0.0010848895,0.098141864,0.0004761595,0.008103852],"study_design_scores_gemma":[0.000009827566,0.00009592814,0.00054032163,0.00006844164,0.000025501513,0.000040543295,0.00006499718,0.9278755,0.0012107617,0.06855051,0.001489253,0.000028307235],"about_ca_topic_score_codex":0.0074493526,"about_ca_topic_score_gemma":0.0032244837,"teacher_disagreement_score":0.0074493526,"about_ca_system_score_codex":0.0036230695,"about_ca_system_score_gemma":0.0017547572,"threshold_uncertainty_score":0.026287317},"labels":[],"label_agreement":null},{"id":"W2054944549","doi":"10.1016/j.advwatres.2008.01.010","title":"Comparison of derivative-free optimization methods for groundwater supply and hydraulic capture community problems","year":2008,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Water Systems and 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":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Sandia National Laboratories; Army Research Office; National Institute of Environmental Health Sciences; North Carolina State University; National Nuclear Security Administration; National Institutes of Health; National Science Foundation","keywords":"Maxima and minima; Mathematical optimization; Groundwater; Computer science; Water supply; Optimization problem; Mathematics; Environmental science; Engineering; Environmental engineering","score_opus":0.02573503127780425,"score_gpt":0.29605094781387176,"score_spread":0.2703159165360675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054944549","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":[0.12067636,0.004417058,0.85003364,0.0018659995,0.00060469366,0.00033236956,0.00044129428,0.0009118455,0.020716745],"genre_scores_gemma":[0.7064805,0.0022892356,0.27912027,0.00054889015,0.0003157065,0.0005001264,0.00087493466,0.00084344915,0.00902685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986338,0.0007320736,0.00009569188,0.00009762569,0.0003311576,0.00010953482],"domain_scores_gemma":[0.9904717,0.0071812673,0.00026676632,0.0004325688,0.0013230819,0.0003246327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005235006,0.0016305527,0.0024825765,0.0019962739,0.0010082105,0.0016989367,0.0024609226,0.0031827062,0.0031716423],"category_scores_gemma":[0.011641622,0.00074385985,0.0017874782,0.0018705291,0.001119814,0.002164123,0.0019043384,0.002530163,0.00044509556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024270007,0.00017541033,0.0004858202,0.00018609544,0.00009148086,0.000026377586,0.000043643562,0.95655614,0.00030021864,0.010355386,0.0013149241,0.030221824],"study_design_scores_gemma":[0.00003291706,0.00003831841,0.00017142325,0.000009805458,0.000012127932,0.0000060409443,0.000011089328,0.99741745,0.00016745321,0.001802427,0.00032194136,0.000008999728],"about_ca_topic_score_codex":0.015833395,"about_ca_topic_score_gemma":0.011591192,"teacher_disagreement_score":0.015833395,"about_ca_system_score_codex":0.0016866721,"about_ca_system_score_gemma":0.00319761,"threshold_uncertainty_score":0.031482518},"labels":[],"label_agreement":null},{"id":"W2055261027","doi":"10.1109/cec.2013.6557636","title":"Pipeline optimization using a novel hybrid algorithm combining front projection and the non-dominated sorting genetic algorithm-II (FP-NSGA-II)","year":2013,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sorting; Pipeline (software); Multi-objective optimization; Algorithm; Convergence (economics); Mathematical optimization; Genetic algorithm; Computer science; Projection (relational algebra); Optimization problem; Pipeline transport; Mathematics; Engineering","score_opus":0.0074867004844172465,"score_gpt":0.18992388269465663,"score_spread":0.18243718221023938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055261027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010711708,0.00010474239,0.98681784,0.000058952493,0.000026909587,0.0000772467,0.000020857773,0.00026267822,0.0019191228],"genre_scores_gemma":[0.21823674,0.00017155227,0.77775675,0.00014899233,0.000032684075,0.00046791782,0.00015784058,0.000074786985,0.002952828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994647,0.00018216457,0.000024054494,0.00006437581,0.00020886984,0.000055825316],"domain_scores_gemma":[0.99965453,0.0001821752,0.000036085534,0.000022425857,0.00008200315,0.000022803182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010044972,0.0011788252,0.0010400163,0.0011567512,0.00044811243,0.0007233281,0.0009640722,0.0011793599,0.0011285943],"category_scores_gemma":[0.0010932435,0.00050327583,0.0010264711,0.0008375994,0.0004917401,0.0005411666,0.0007947229,0.0007467353,0.0002481783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004571674,0.00006896211,0.0003967489,0.00008845272,0.00006749231,0.000052536107,0.000047096924,0.9137531,0.004061711,0.0043390845,0.0007242598,0.07635481],"study_design_scores_gemma":[0.000013927721,0.000047753674,0.00008911519,0.0000073442593,0.000008986361,0.00001854855,0.0000054262255,0.9974692,0.0007852652,0.0008865625,0.0006629028,0.0000050150315],"about_ca_topic_score_codex":0.003972491,"about_ca_topic_score_gemma":0.0041373754,"teacher_disagreement_score":0.003972491,"about_ca_system_score_codex":0.00065354764,"about_ca_system_score_gemma":0.0016077698,"threshold_uncertainty_score":0.007898748},"labels":[],"label_agreement":null},{"id":"W2057914983","doi":"10.2166/wst.2009.225","title":"Wastewater treatment modelling: dealing with uncertainties","year":2009,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":92,"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":"Relevance (law); Computer science; Context (archaeology); Reliability (semiconductor); Uncertainty analysis; Risk analysis (engineering); Management science; Operations research; Engineering; Simulation","score_opus":0.010745523027764596,"score_gpt":0.19196094745331338,"score_spread":0.18121542442554878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057914983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02186759,0.0026294002,0.96225214,0.0042897384,0.00014582556,0.00004524575,0.00010157534,0.00011058385,0.008557936],"genre_scores_gemma":[0.8225398,0.0047218627,0.16855471,0.00058456126,0.00055523304,0.0002108609,0.0001351987,0.00010428322,0.0025934863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99362576,0.0037486576,0.00027371847,0.00049270847,0.0016027053,0.00025633944],"domain_scores_gemma":[0.9874013,0.010138138,0.0009310734,0.0006186406,0.0007870953,0.00012378822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006690769,0.0009482386,0.0016351597,0.0010877412,0.0010839496,0.003507043,0.0017324629,0.0031847898,0.0008326351],"category_scores_gemma":[0.023940777,0.00067230343,0.0011296329,0.0017231758,0.002177242,0.0046134507,0.0033148085,0.002640771,0.00013203443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021223701,0.000012689493,0.00088487734,0.00015414934,0.000056951227,0.0001625453,0.0002574565,0.86695796,0.0003054115,0.10809564,0.00058107515,0.022510102],"study_design_scores_gemma":[0.000006862248,0.00002243462,0.00021789645,0.00008741052,0.000021171629,0.00009502901,0.00012199802,0.7149496,0.00054431503,0.28065607,0.0032446918,0.00003248495],"about_ca_topic_score_codex":0.004353128,"about_ca_topic_score_gemma":0.0027381633,"teacher_disagreement_score":0.006690769,"about_ca_system_score_codex":0.0017304693,"about_ca_system_score_gemma":0.0017344623,"threshold_uncertainty_score":0.035384595},"labels":[],"label_agreement":null},{"id":"W2059819780","doi":"10.1214/11-aap760","title":"Analysis of error propagation in particle filters with approximation","year":2011,"lang":"en","type":"article","venue":"The Annals of Applied Probability","topic":"Water Systems and 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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Particle filter; Approximation error; Exponential function; Parametric statistics; Particle (ecology); Approximation theory; Error analysis; Approximation algorithm; Applied mathematics; Computer science; Node (physics); Particle system; Mathematics; Filter (signal processing); Algorithm; Mathematical optimization; Mathematical analysis; Physics; Statistics; Quantum mechanics","score_opus":0.08832290302781962,"score_gpt":0.24836812057365007,"score_spread":0.16004521754583045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059819780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011004686,0.00028002262,0.9866067,0.00033191522,0.000045174616,0.000028594577,0.000029096913,0.00010968354,0.0015640619],"genre_scores_gemma":[0.81337005,0.0010635968,0.17518811,0.00031686536,0.00018954136,0.00032997873,0.00025023564,0.00027150442,0.009020089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973133,0.000787855,0.00015217916,0.0003529445,0.0011074437,0.00028622933],"domain_scores_gemma":[0.96934175,0.025197688,0.001591592,0.0008713041,0.0026937714,0.0003038015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008665625,0.0013962289,0.0013590195,0.0012880701,0.0008479285,0.0020190072,0.0016276505,0.0023731613,0.0021508764],"category_scores_gemma":[0.04554082,0.00082876754,0.001036372,0.0009912612,0.0025246355,0.003113605,0.0024089313,0.0021124089,0.0003238373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068165515,0.000014325639,0.000613351,0.0000650033,0.000029732286,0.000059078357,0.00006251116,0.96180797,0.00072077615,0.030045971,0.0002812545,0.006231953],"study_design_scores_gemma":[0.0000042754577,0.00001190294,0.00008472607,0.0000065840745,0.0000046880514,0.000009274068,0.0000045762786,0.9960349,0.00034415966,0.003349957,0.0001399928,0.000004892379],"about_ca_topic_score_codex":0.012587296,"about_ca_topic_score_gemma":0.0035307743,"teacher_disagreement_score":0.012587296,"about_ca_system_score_codex":0.0026253422,"about_ca_system_score_gemma":0.0021892325,"threshold_uncertainty_score":0.04582876},"labels":[],"label_agreement":null},{"id":"W2061518910","doi":"10.1061/(asce)wr.1943-5452.0000136","title":"Performance Estimation of a Remote Field Eddy Current Method for the Inspection of Water Distribution Pipes","year":2010,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique","funders":"Université Laval","keywords":"Eddy current; Nondestructive testing; Corrosion; Eddy-current testing; Attenuation; Structural integrity; Field (mathematics); Materials science; Structural engineering; Engineering; Composite material; Optics; Mathematics; Physics","score_opus":0.009319830693094071,"score_gpt":0.24231488646105503,"score_spread":0.23299505576796095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061518910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.189474,0.00031770088,0.80828756,0.00006171439,0.00001864389,0.000043798464,0.000022820544,0.0006473694,0.0011263861],"genre_scores_gemma":[0.8715985,0.00012545966,0.12718065,0.000019474714,0.000013243453,0.000036682555,0.000046166486,0.00002845708,0.0009514622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997049,0.00009781749,0.000010439758,0.000058382368,0.00011330666,0.000015096392],"domain_scores_gemma":[0.99892896,0.0005860969,0.000104565304,0.000058208127,0.00029314822,0.000028987515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008089349,0.0004724989,0.00047893645,0.0004401667,0.00012636982,0.0003091708,0.00041927883,0.0007299202,0.0008124828],"category_scores_gemma":[0.0024818433,0.00024169924,0.00021248664,0.00015889145,0.00023471784,0.0003956227,0.0002612608,0.0002518878,0.00027510754],"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":[0.0010496107,0.00018879618,0.0072242506,0.00032217632,0.000113548325,0.00016113311,0.00021488288,0.15078358,0.29343975,0.0007923338,0.0006500827,0.54505986],"study_design_scores_gemma":[0.00004498889,0.00034358195,0.005413015,0.00001296873,0.0000253098,0.00014233118,0.00003148276,0.9624607,0.030809056,0.00017756056,0.0005114844,0.000027498587],"about_ca_topic_score_codex":0.0014958127,"about_ca_topic_score_gemma":0.0010560262,"teacher_disagreement_score":0.0014958127,"about_ca_system_score_codex":0.0002060518,"about_ca_system_score_gemma":0.00021701676,"threshold_uncertainty_score":0.0042781234},"labels":[],"label_agreement":null},{"id":"W2063741556","doi":"10.4043/25700-ms","title":"Water Hammer in Upstream Production","year":2015,"lang":"en","type":"article","venue":"Offshore Technology Conference","topic":"Water Systems and 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":"ConocoPhillips (Canada)","funders":"","keywords":"Water hammer; Hammer; Pipeline transport; Transient (computer programming); Explosive material; Mechanics; Computer science; Engineering; Mechanical engineering; Physics","score_opus":0.02104069567918199,"score_gpt":0.20423342721446053,"score_spread":0.18319273153527854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063741556","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":[0.58728033,0.000443027,0.20737533,0.0013163652,0.0001803858,0.00019639304,0.0009774688,0.0008656492,0.201365],"genre_scores_gemma":[0.9842205,0.000096823715,0.003095001,0.000022102073,0.0000037927541,0.000032630312,0.000090500864,0.000034952503,0.012403764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998437,0.000034636676,0.0000061658375,0.000027736016,0.000043670516,0.00004405258],"domain_scores_gemma":[0.9997807,0.00010796152,0.00002240126,0.000021707863,0.000041008414,0.00002627436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003241471,0.0002930254,0.00037930842,0.00035318075,0.0005622079,0.0011016311,0.000533583,0.0009083897,0.01987182],"category_scores_gemma":[0.0012269118,0.00026686434,0.0002856353,0.00039381784,0.0008311341,0.001175059,0.00070662814,0.0005257101,0.0009030754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056603178,0.00003450375,0.00064506056,0.000036006557,0.000003933943,0.0001433556,0.00004035213,0.9783382,0.0014657257,0.01282305,0.00093631446,0.005476895],"study_design_scores_gemma":[0.000025241423,0.000088210356,0.000536033,0.000011884175,0.000004408596,0.000019699384,0.000094412964,0.98676807,0.0012925365,0.009124436,0.00202481,0.0000102337945],"about_ca_topic_score_codex":0.010778058,"about_ca_topic_score_gemma":0.007083733,"teacher_disagreement_score":0.01987182,"about_ca_system_score_codex":0.001267026,"about_ca_system_score_gemma":0.00091055955,"threshold_uncertainty_score":0.066477895},"labels":[],"label_agreement":null},{"id":"W2063763647","doi":"10.1007/s00477-013-0780-4","title":"Comparative study of fuzzy evidential reasoning and fuzzy rule-based approaches: an illustration for water quality assessment in distribution networks","year":2013,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Water Systems and Optimization","field":"Engineering","cited_by":28,"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; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Multiple-criteria decision analysis; Data mining; Quality (philosophy); Inference; Fuzzy logic; Water quality; Sampling (signal processing); Computational intelligence; Machine learning; Artificial intelligence; Risk analysis (engineering); Operations research; Mathematics","score_opus":0.07408012324311208,"score_gpt":0.3399884895750173,"score_spread":0.26590836633190523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063763647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18730947,0.0033404906,0.7791746,0.0011576659,0.000063916916,0.000114172435,0.00009566878,0.00009617136,0.028647814],"genre_scores_gemma":[0.87507015,0.0010872101,0.122221924,0.00006462892,0.000039904422,0.000037718215,0.000033219927,0.000014605851,0.0014306209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965945,0.0020231667,0.000121643854,0.00018669863,0.0009797461,0.00009418169],"domain_scores_gemma":[0.9798651,0.017627075,0.00048799632,0.0007216005,0.0011913702,0.00010691677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00847768,0.0006114973,0.0007648687,0.0014755419,0.00060774595,0.0026142485,0.0012769647,0.0014018922,0.0019012113],"category_scores_gemma":[0.020847276,0.0002577979,0.0009886642,0.001244449,0.0014399478,0.0030730958,0.0009431838,0.0010419858,0.0001496993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003158829,0.00035596,0.003332889,0.00048430043,0.00023476024,0.00032822907,0.0011605129,0.45977163,0.0027731147,0.38767233,0.00062686525,0.14294364],"study_design_scores_gemma":[0.000027752852,0.00014421936,0.0014063627,0.000077347606,0.00007457354,0.00007842588,0.00026927053,0.8817316,0.0009545572,0.11392973,0.0012741366,0.000031926797],"about_ca_topic_score_codex":0.0032848278,"about_ca_topic_score_gemma":0.0040442054,"teacher_disagreement_score":0.00847768,"about_ca_system_score_codex":0.0012841895,"about_ca_system_score_gemma":0.00096716423,"threshold_uncertainty_score":0.044834733},"labels":[],"label_agreement":null},{"id":"W2065495772","doi":"10.1080/00221686.2008.9521858","title":"Optimal design and operation of irrigation pumping stations using mathematical programming and Genetic Algorithm (GA)","year":2008,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":35,"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; Operating cost; Computer science; Genetic algorithm; Scheduling (production processes); Computation; Selection (genetic algorithm); Nonlinear programming; Lagrange multiplier; Capital cost; Nonlinear system; Mathematics; Algorithm; Engineering","score_opus":0.09022741923866252,"score_gpt":0.3235501843682656,"score_spread":0.2333227651296031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065495772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043113496,0.00035441265,0.9509561,0.0002388588,0.000027039609,0.00014834871,0.000066928595,0.00028607534,0.0048087616],"genre_scores_gemma":[0.54571354,0.0006560623,0.45030266,0.000092964525,0.000021413856,0.00052980176,0.00012449535,0.000055403234,0.0025036354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955136,0.00020523428,0.000021263819,0.00008227315,0.00008504952,0.000054877488],"domain_scores_gemma":[0.9996234,0.00022571479,0.00007376948,0.000018030089,0.00004539045,0.000013593973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080018956,0.00076029665,0.0008766183,0.0007214824,0.0003218404,0.0010953601,0.0007182994,0.0010695935,0.0013157229],"category_scores_gemma":[0.0019124208,0.0007448785,0.00071104977,0.00091429957,0.0008093501,0.00069739646,0.0005233455,0.00057661725,0.00021961704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001667193,0.00001306045,0.00020330159,0.000027554526,0.000011818785,0.000015226361,0.000013855603,0.98537964,0.000677994,0.002954081,0.00014729823,0.010539493],"study_design_scores_gemma":[0.000016705744,0.000027403365,0.00008886145,0.000007205658,0.000009188523,0.000007919024,0.000008508457,0.99656314,0.0004878023,0.0022303746,0.00054790475,0.000004926637],"about_ca_topic_score_codex":0.0068333945,"about_ca_topic_score_gemma":0.0060933414,"teacher_disagreement_score":0.0068333945,"about_ca_system_score_codex":0.0011079636,"about_ca_system_score_gemma":0.0024030246,"threshold_uncertainty_score":0.013587236},"labels":[],"label_agreement":null},{"id":"W2065651243","doi":"10.1115/ipc2010-31018","title":"Field Validation of a Dynamic Model for an MFL ILI Tool in Gas Pipelines","year":2010,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":true,"ca_institutions":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Pigging; Pipeline transport; Pipeline (software); Compressibility; Marine engineering; Line (geometry); Engineering; Pressure drop; Simulation; Mechanics; Petroleum engineering; Geology; Mechanical engineering; Aerospace engineering; Physics; Mathematics; Geometry","score_opus":0.011091425896201464,"score_gpt":0.23096870780426257,"score_spread":0.2198772819080611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065651243","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":[0.92095155,0.00013232414,0.066846766,0.00028577764,0.00007201739,0.00027848512,0.0012514364,0.0013884199,0.0087931575],"genre_scores_gemma":[0.99166447,0.00003124281,0.0060120528,0.000018363653,0.0000024051535,0.000099208984,0.00040794245,0.000026352025,0.0017378591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997125,0.00005264749,0.000020140149,0.00006497849,0.00007811501,0.000071603994],"domain_scores_gemma":[0.99917114,0.0003482122,0.00007469754,0.00006377823,0.00029896406,0.000043246717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009576022,0.00068425905,0.0006891756,0.00058721966,0.00082976476,0.0009024703,0.0012503059,0.0018909122,0.0023012387],"category_scores_gemma":[0.0017293065,0.0003442637,0.00059458613,0.00040988755,0.0007383962,0.00051287713,0.00055017485,0.00083841383,0.0005386324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066831766,0.00006541529,0.0019657097,0.00004229918,0.0000047379863,0.00005470549,0.00003723297,0.9918365,0.002321775,0.00016190905,0.00020465093,0.0032382705],"study_design_scores_gemma":[0.0000127302565,0.000053413045,0.0005797757,0.000005452086,0.000001802704,0.000003514348,0.000023792076,0.9982128,0.0008578464,0.000025324529,0.00021859701,0.0000049611326],"about_ca_topic_score_codex":0.10286621,"about_ca_topic_score_gemma":0.044404503,"teacher_disagreement_score":0.10286621,"about_ca_system_score_codex":0.0013847694,"about_ca_system_score_gemma":0.0018720058,"threshold_uncertainty_score":0.20453489},"labels":[],"label_agreement":null},{"id":"W2066307528","doi":"10.1016/j.watres.2010.03.010","title":"Early determination of toxicant concentration in water supply using MHE","year":2010,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extended Kalman filter; Toxicant; Estimator; Kalman filter; Moving horizon estimation; Water supply; Estimation; Estimation theory; Computer science; Environmental science; Engineering; Environmental engineering; Chemistry; Algorithm; Mathematics; Artificial intelligence; Statistics","score_opus":0.036486173546456245,"score_gpt":0.29833983493171906,"score_spread":0.26185366138526284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066307528","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":[0.90836835,0.0010480745,0.08562288,0.0001824902,0.000066449014,0.00011146342,0.00033324485,0.0002989523,0.003968092],"genre_scores_gemma":[0.9767363,0.000274541,0.019947134,0.000049350758,0.000012394167,0.00003285542,0.00006683611,0.000010889521,0.0028698386],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968517,0.000059630987,0.000012581951,0.00006452322,0.0001385172,0.000039583407],"domain_scores_gemma":[0.99971086,0.00014074672,0.000031881595,0.00002302325,0.0000785985,0.000014749136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033092164,0.00021178398,0.0003944566,0.00022884985,0.00016956154,0.00031390242,0.00022023015,0.00037616983,0.00093646883],"category_scores_gemma":[0.00053273176,0.00015448619,0.00024212038,0.00019121204,0.00025852892,0.00046154298,0.0003416284,0.0004222035,0.00031134873],"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":[0.00044304552,0.000034213597,0.0042583244,0.00011048866,0.000009862643,0.000048203736,0.000050323833,0.0010103156,0.977246,0.00024604113,0.000091336806,0.016451925],"study_design_scores_gemma":[0.0000064631213,0.00020559992,0.004003525,0.000004612364,0.000008409617,0.000028432718,0.00003687951,0.0072397236,0.98732835,0.00009126043,0.0010395705,0.000007197093],"about_ca_topic_score_codex":0.0009942605,"about_ca_topic_score_gemma":0.0017687465,"teacher_disagreement_score":0.0009942605,"about_ca_system_score_codex":0.00026223282,"about_ca_system_score_gemma":0.00017780956,"threshold_uncertainty_score":0.0031328201},"labels":[],"label_agreement":null},{"id":"W2066477610","doi":"10.1016/j.ijmultiphaseflow.2014.01.009","title":"Identification of flow regime in vertical upward air–water pipe flow using differential pressure signals and elastic maps","year":2014,"lang":"en","type":"article","venue":"International Journal of Multiphase Flow","topic":"Water Systems and 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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Mechanics; Flow (mathematics); Range (aeronautics); Differential pressure; Materials science; Static pressure; Physics; Composite material","score_opus":0.008966690733875194,"score_gpt":0.2273795057407721,"score_spread":0.2184128150068969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066477610","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":[0.8792961,0.000051031268,0.119344056,0.00004285616,0.000009873832,0.000018986571,0.00004043413,0.00009142734,0.0011051769],"genre_scores_gemma":[0.9944811,0.000018352695,0.005234061,0.0000030028143,0.0000034137179,0.0000049269192,0.00001746871,0.0000027667918,0.0002349651],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999484,0.000009573925,0.0000027518838,0.000015927664,0.000011336372,0.0000120823015],"domain_scores_gemma":[0.99989915,0.00004463618,0.000019186382,0.000006181652,0.000018900833,0.000011831202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013290056,0.0002807431,0.00016371459,0.00048391017,0.0001806212,0.00025611286,0.00016392594,0.00026805032,0.00032720383],"category_scores_gemma":[0.00037636442,0.00016496662,0.00016805175,0.00022264764,0.00024299226,0.00038569135,0.00032549293,0.00021098623,0.000056015324],"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":[0.0012056605,0.00041779314,0.047486164,0.00018286397,0.000054382126,0.0006165594,0.00033472735,0.31192693,0.39688593,0.004524421,0.0004255735,0.23593894],"study_design_scores_gemma":[0.0000083413215,0.00006159776,0.016970327,0.0000035167623,0.0000066125954,0.00004359995,0.000036809706,0.9716917,0.010576672,0.00046592578,0.00012499894,0.000009833879],"about_ca_topic_score_codex":0.0011777226,"about_ca_topic_score_gemma":0.0011243005,"teacher_disagreement_score":0.0011777226,"about_ca_system_score_codex":0.00009275469,"about_ca_system_score_gemma":0.00018243409,"threshold_uncertainty_score":0.0023417473},"labels":[],"label_agreement":null},{"id":"W2066592454","doi":"10.1016/j.ymssp.2012.03.006","title":"The use of laser range finder on a robotic platform for pipe inspection","year":2012,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Robot; Laser; Range (aeronautics); Computer vision; Artificial intelligence; Engineering; Computer science; Acoustics; Optics; Aerospace engineering; Physics","score_opus":0.05027809725130679,"score_gpt":0.2240057640245708,"score_spread":0.173727666773264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066592454","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":[0.547027,0.0011506256,0.43694192,0.00042757762,0.00021545631,0.00016220474,0.000102595324,0.0017922035,0.012180419],"genre_scores_gemma":[0.8381281,0.00028042932,0.15519756,0.00007622229,0.00003332625,0.000052789434,0.000049137965,0.000044174452,0.006138232],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969494,0.00003724502,0.000007097518,0.00007764676,0.00014878002,0.000034290293],"domain_scores_gemma":[0.9997733,0.000061001647,0.000046040004,0.000050593993,0.000047435333,0.000021593873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027666677,0.00039806069,0.0003866127,0.0003248961,0.0004235269,0.00036700442,0.00074969436,0.00074314105,0.0013158645],"category_scores_gemma":[0.0004084372,0.00033556746,0.00037793056,0.0002494522,0.0003233662,0.000704729,0.0007173133,0.00034638864,0.00040187046],"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":[0.00033681386,0.00010660354,0.0017980363,0.00015872541,0.000042793803,0.00040566913,0.00016794846,0.016195381,0.87290215,0.0025524145,0.0007347575,0.104598776],"study_design_scores_gemma":[0.00008523655,0.002856937,0.012596257,0.000056229608,0.00014923459,0.0017020038,0.00015803345,0.2974538,0.663713,0.0021565137,0.018912952,0.00015988121],"about_ca_topic_score_codex":0.00072526315,"about_ca_topic_score_gemma":0.0012392051,"teacher_disagreement_score":0.0013158645,"about_ca_system_score_codex":0.00025623548,"about_ca_system_score_gemma":0.00067799015,"threshold_uncertainty_score":0.004401982},"labels":[],"label_agreement":null},{"id":"W2066701718","doi":"10.1061/(asce)0733-9496(2002)128:5(370)","title":"Contribution of Neural Networks for Modeling Trihalomethanes Occurrence in Drinking Water","year":2002,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada","keywords":"Artificial neural network; Water quality; Environmental science; Logistic regression; Water treatment; Trihalomethane; Water resources; Environmental engineering; Computer science; Machine learning","score_opus":0.019149310742214074,"score_gpt":0.21409487468046362,"score_spread":0.19494556393824955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066701718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47502217,0.0028239524,0.51071453,0.0008695532,0.00021527342,0.00006615448,0.00022813841,0.0005140144,0.009546112],"genre_scores_gemma":[0.9799861,0.0006096055,0.017454492,0.00004253884,0.00004760545,0.000044085995,0.00007940392,0.000017057835,0.0017191982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997423,0.00012660857,0.000018782037,0.00004063921,0.000048075133,0.000023581599],"domain_scores_gemma":[0.9988098,0.00083661213,0.0000954022,0.00004028603,0.00019473948,0.000023174623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079276867,0.0009011762,0.00048066085,0.0004722713,0.00024992283,0.00078974856,0.00040511144,0.0009145566,0.0006905079],"category_scores_gemma":[0.0037419952,0.00032366443,0.00043399647,0.0003631093,0.00022126085,0.00096459076,0.0003037798,0.00058327656,0.00015916272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035980436,0.00002338966,0.0010296179,0.000013057673,0.000026846394,0.000015229279,0.0000051896754,0.9868994,0.00034272336,0.00026143627,0.000064542204,0.011282674],"study_design_scores_gemma":[0.0000011889156,0.000007988844,0.00014754683,0.0000016806114,0.000005139513,0.0000027101366,0.0000013196228,0.99938273,0.00015347228,0.00024924602,0.00004479697,0.0000021792328],"about_ca_topic_score_codex":0.013067777,"about_ca_topic_score_gemma":0.0063901464,"teacher_disagreement_score":0.013067777,"about_ca_system_score_codex":0.0005283555,"about_ca_system_score_gemma":0.00044699083,"threshold_uncertainty_score":0.025983453},"labels":[],"label_agreement":null},{"id":"W2066736896","doi":"10.1061/(asce)he.1943-5584.0000422","title":"Discriminating between the Lognormal and the Log-Logistic Distributions for Hydrological Frequency Analysis","year":2011,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Water Systems and 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":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Log-normal distribution; Statistics; Frequency distribution; Frequency analysis; Mathematics; Environmental science; Hydrology (agriculture); Econometrics; Geology","score_opus":0.02928405530892069,"score_gpt":0.21140381275299955,"score_spread":0.18211975744407885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066736896","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":[0.100809366,0.000413649,0.89568436,0.0005505619,0.000041605937,0.00009623999,0.0001347909,0.00023533321,0.0020341398],"genre_scores_gemma":[0.7165883,0.00035171237,0.2813805,0.00013811426,0.00012357881,0.00022026754,0.00033260186,0.00008175433,0.00078300433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9938583,0.0042116507,0.0002173775,0.00037867264,0.0011624094,0.00017158348],"domain_scores_gemma":[0.9376449,0.0558276,0.0022722187,0.0019448405,0.001735913,0.00057464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013013916,0.0005678239,0.00074903766,0.0023560333,0.00084540574,0.0016043665,0.0010564332,0.0012062548,0.0022200847],"category_scores_gemma":[0.08511943,0.00022146304,0.0008447994,0.002242197,0.0015229638,0.0024607026,0.001403852,0.0017446638,0.00048177733],"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":[0.0006062089,0.00032039912,0.057137918,0.00030367982,0.0001512669,0.0007850277,0.0005844969,0.46788013,0.006644433,0.16932295,0.004035779,0.2922277],"study_design_scores_gemma":[0.000026877402,0.00010534597,0.004577454,0.000026587639,0.000012098177,0.00029994763,0.00015222382,0.90674156,0.00088218047,0.08593759,0.001190149,0.00004788035],"about_ca_topic_score_codex":0.0018402912,"about_ca_topic_score_gemma":0.0022358606,"teacher_disagreement_score":0.013013916,"about_ca_system_score_codex":0.00087145244,"about_ca_system_score_gemma":0.0011744262,"threshold_uncertainty_score":0.06882501},"labels":[],"label_agreement":null},{"id":"W2068217738","doi":"10.1115/ipc2006-10173","title":"Prediction of the Location and Duration of Water Condensation in Nominally Dry Gas Transmission Pipelines","year":2006,"lang":"en","type":"article","venue":"Volume 2: Integrity Management; Poster Session; Student Paper Competition","topic":"Water Systems and 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":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Corrosion; Dew point; Relative humidity; Water vapor; Condensation; Humidity; Dry gas; Materials science; Inlet; Environmental science; Evaporation; Pipeline transport; Saturation (graph theory); Upset; Mass transfer; Mechanics; Environmental engineering; Metallurgy; Chemistry; Meteorology; Geology","score_opus":0.007157205157630063,"score_gpt":0.1909629897069559,"score_spread":0.18380578454932583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068217738","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":[0.9941577,0.00003520061,0.004528721,0.000023838807,0.000003877305,0.0000142593135,0.0004093583,0.000293923,0.0005330392],"genre_scores_gemma":[0.9982668,0.000022216804,0.0011428097,0.0000028406173,0.0000010475471,0.0000060109724,0.00027207195,0.000014539016,0.00027171645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999201,0.000007042951,0.0000032573219,0.000028387938,0.000018453913,0.000022679855],"domain_scores_gemma":[0.99961424,0.00018608899,0.00006923781,0.000017439355,0.000066551314,0.000046505927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000182817,0.0005684044,0.0003293285,0.00045513775,0.00030012266,0.00036747113,0.000450242,0.0006580248,0.0006191585],"category_scores_gemma":[0.0009418399,0.00037066566,0.00038726768,0.0003062314,0.00039949614,0.00043444577,0.00025008444,0.00036714703,0.00015195504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017654877,0.00007694,0.019309698,0.000043043972,0.000013370066,0.00013849452,0.00006786455,0.9527573,0.023170985,0.00018034966,0.00021775159,0.0038475327],"study_design_scores_gemma":[0.000010091727,0.00004708167,0.004955408,0.0000018580893,0.0000037882032,0.00001169449,0.000012531013,0.98926544,0.0055641984,0.00005665982,0.00006520591,0.00000606863],"about_ca_topic_score_codex":0.027899712,"about_ca_topic_score_gemma":0.01396934,"teacher_disagreement_score":0.027899712,"about_ca_system_score_codex":0.001063485,"about_ca_system_score_gemma":0.0006211826,"threshold_uncertainty_score":0.05547464},"labels":[],"label_agreement":null},{"id":"W2069342886","doi":"10.1061/(asce)0733-9429(2002)128:6(625)","title":"Transient Flow in a Rapidly Filling Horizontal Pipe Containing Trapped Air","year":2002,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":262,"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":"Body orifice; Water hammer; Mechanics; Cushioning; Materials science; Oscillation (cell signaling); Orifice plate; Pressure drop; Airflow; Leakage (economics); Thermodynamics; Chemistry; Physics; Composite material; Engineering; Mechanical engineering","score_opus":0.00803481250076387,"score_gpt":0.17076844357270018,"score_spread":0.1627336310719363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069342886","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":[0.9958675,0.000047402704,0.0037640133,0.000020051843,0.0000031065504,0.0000069905523,0.000023036979,0.0000869312,0.0001810016],"genre_scores_gemma":[0.9988702,0.000022838829,0.00093024154,0.000002945037,0.0000019471856,0.000005202289,0.000016339387,0.0000037731197,0.00014659394],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987555,0.000020120355,0.0000040690793,0.00002563933,0.000035176367,0.000039445848],"domain_scores_gemma":[0.99964845,0.00019315106,0.00006919608,0.000018436343,0.00003999609,0.000030689764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026626152,0.00022824713,0.0003765406,0.00019010456,0.00028174167,0.00033920605,0.00030433433,0.00043596563,0.00045944666],"category_scores_gemma":[0.0005671116,0.00021140873,0.00021738747,0.00022674222,0.0008873056,0.0003909155,0.00031418714,0.00031988783,0.000060316637],"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":[0.00055486674,0.00011789878,0.007421784,0.0001428886,0.000018940325,0.0011194169,0.0004784464,0.04378937,0.9347685,0.00096907525,0.00016499712,0.010453814],"study_design_scores_gemma":[0.0001415435,0.0034478637,0.036242176,0.000030389283,0.000044322114,0.0004097201,0.0004859995,0.6245166,0.33298162,0.00078157044,0.0008421009,0.000075977165],"about_ca_topic_score_codex":0.00159541,"about_ca_topic_score_gemma":0.0006058054,"teacher_disagreement_score":0.00159541,"about_ca_system_score_codex":0.00044163648,"about_ca_system_score_gemma":0.00040682356,"threshold_uncertainty_score":0.0032043457},"labels":[],"label_agreement":null},{"id":"W2069482161","doi":"10.1139/l10-082","title":"A global modeling approach to the hydraulic performance evaluation of a sewer network","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Hôpital Notre-Dame; Espace pour la vie","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrograph; Flow routing; Routing (electronic design automation); Surface runoff; Nonlinear system; Node (physics); Flow (mathematics); Inflow; Computer science; Hydrology (agriculture); Civil engineering; Environmental science; Engineering; Mathematics; Geotechnical engineering; Geology; Structural engineering","score_opus":0.011766599827903975,"score_gpt":0.1791661164657137,"score_spread":0.16739951663780972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069482161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012492888,0.00006487885,0.9830791,0.000056778077,0.000008917366,0.00002721292,0.00010279044,0.00031196064,0.0038554722],"genre_scores_gemma":[0.69943595,0.000736864,0.29212394,0.000060875056,0.00006201724,0.00032176563,0.00052834465,0.00033335455,0.0063968897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996829,0.00011170628,0.0000121356725,0.00005555159,0.00011441619,0.000023353738],"domain_scores_gemma":[0.9996387,0.00014972172,0.00004685256,0.00007377581,0.00007673973,0.000014228193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006639105,0.0010801207,0.0007342401,0.00082237576,0.00030282728,0.0010234724,0.00069156167,0.0005799201,0.002114046],"category_scores_gemma":[0.001186337,0.0002775267,0.0006719774,0.00089527346,0.0005266984,0.0015185096,0.0007162955,0.0006241061,0.00030603615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009217152,0.000012459656,0.00067384954,0.000027513135,0.000021983886,0.000017099594,0.000030773343,0.96928126,0.0019329021,0.009304646,0.00026712017,0.01842113],"study_design_scores_gemma":[0.000002198554,0.000033045733,0.00033691063,0.000004986572,0.000011914369,0.0000109439425,0.000018693183,0.99341416,0.00096137996,0.0037354361,0.001464139,0.000006112829],"about_ca_topic_score_codex":0.0027082337,"about_ca_topic_score_gemma":0.0034361866,"teacher_disagreement_score":0.0027082337,"about_ca_system_score_codex":0.0007559907,"about_ca_system_score_gemma":0.00073498214,"threshold_uncertainty_score":0.0070722103},"labels":[],"label_agreement":null},{"id":"W2070919380","doi":"10.1108/14777830610700928","title":"A new system for locating leaks in urban water distribution pipes","year":2006,"lang":"en","type":"article","venue":"Management of Environmental Quality An International Journal","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Leak detection; Leak; Leakage (economics); Computer science; Engineering; Risk analysis (engineering); Environmental engineering","score_opus":0.009695423336738321,"score_gpt":0.2209990491387703,"score_spread":0.211303625802032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070919380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040104065,0.00036895872,0.94381636,0.0003252085,0.00029618462,0.00036832888,0.0002663574,0.009901694,0.004552857],"genre_scores_gemma":[0.47768337,0.0005896176,0.50300175,0.0005961469,0.00031840292,0.00066678884,0.0005643033,0.00034681108,0.016232861],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999005,0.0001618248,0.00005914337,0.00027043498,0.00043879944,0.00006472687],"domain_scores_gemma":[0.9986029,0.0002383014,0.00021220832,0.000196024,0.000669347,0.00008115858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006966886,0.00068705645,0.0006857929,0.0012934462,0.0006411282,0.0008352144,0.0009466462,0.00074278103,0.005358737],"category_scores_gemma":[0.0017828193,0.00040554267,0.0002792498,0.0008473223,0.00048650507,0.001757316,0.0013620108,0.0006302425,0.0016081762],"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":[0.00076669065,0.00026069616,0.007418077,0.0009483712,0.0000788718,0.00044915781,0.0006235835,0.011924443,0.25024936,0.004398174,0.011561937,0.71132064],"study_design_scores_gemma":[0.0007130026,0.0035592413,0.0230732,0.0003479732,0.0005456941,0.0029658333,0.00042176014,0.3701407,0.43583837,0.004026446,0.15786871,0.0004990584],"about_ca_topic_score_codex":0.0008285275,"about_ca_topic_score_gemma":0.00093182904,"teacher_disagreement_score":0.005358737,"about_ca_system_score_codex":0.0005677352,"about_ca_system_score_gemma":0.00083087716,"threshold_uncertainty_score":0.017926753},"labels":[],"label_agreement":null},{"id":"W2071572476","doi":"10.1061/40690(2003)92","title":"Interactive Simulation-Based Educational Software for Management of Municipal Sewer Networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Western University","funders":"","keywords":"Computer science; Upgrade; Key (lock); Software; Interface (matter); User interface; Engineering management; Software engineering; Engineering; Operating system","score_opus":0.011162360929948626,"score_gpt":0.24164536127307715,"score_spread":0.23048300034312852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071572476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027566418,0.00010260704,0.8649021,0.00023346649,0.00004460761,0.0005293091,0.0038389636,0.08048389,0.022298718],"genre_scores_gemma":[0.33288428,0.0006716981,0.6160385,0.0001910001,0.00004817799,0.0037993586,0.008731423,0.0084156105,0.029219972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997857,0.00006736618,0.000020383166,0.00002409529,0.00008234394,0.000020078642],"domain_scores_gemma":[0.99881953,0.00082054845,0.000043311265,0.000094373514,0.00015218227,0.0000699731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075630244,0.000625274,0.00053610624,0.0006169022,0.00033619395,0.0006407199,0.0017141332,0.00066116353,0.037838478],"category_scores_gemma":[0.0022646615,0.0005936496,0.00051604054,0.0006508935,0.00028010973,0.000811279,0.00080471183,0.00090047496,0.003827466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007161112,0.0009302843,0.00529754,0.0005603843,0.00013665977,0.0003710703,0.00087293086,0.6570567,0.016284255,0.031588346,0.078265816,0.20791993],"study_design_scores_gemma":[0.0003047706,0.0000953889,0.0012134812,0.00006215207,0.0000404356,0.000112973314,0.000040263363,0.92906296,0.008291619,0.007721154,0.053015776,0.000039021685],"about_ca_topic_score_codex":0.0037116974,"about_ca_topic_score_gemma":0.00452866,"teacher_disagreement_score":0.037838478,"about_ca_system_score_codex":0.00057912717,"about_ca_system_score_gemma":0.00075368927,"threshold_uncertainty_score":0.12658238},"labels":[],"label_agreement":null},{"id":"W2071783769","doi":"10.1115/ipc2008-64071","title":"Parametric and Non-Parametric Survival Models for “Time to Failure” of Water Pipelines: Case Study","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Pipeline transport; Parametric statistics; Pipeline (software); Mains electricity; Parametric model; Computer science; Asset (computer security); Asset management; Reliability engineering; Environmental science; Engineering; Civil engineering; Petroleum engineering; Environmental engineering; Statistics; Mechanical engineering; Mathematics; Computer security","score_opus":0.021688009069602804,"score_gpt":0.21440169144579713,"score_spread":0.19271368237619432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071783769","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":[0.7894934,0.0007859726,0.20054838,0.0011025236,0.00006827361,0.0004956528,0.003474753,0.00041231042,0.0036187766],"genre_scores_gemma":[0.9782453,0.00028155503,0.016465984,0.000042084233,0.000036164878,0.0003176268,0.0017003698,0.00003307693,0.0028778599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711573,0.0017709846,0.00012474516,0.00038843366,0.00024190715,0.00035826032],"domain_scores_gemma":[0.95756614,0.036165778,0.0025165125,0.0017785698,0.0015714933,0.00040136493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014492003,0.0010325138,0.0010872014,0.0019277381,0.0005943779,0.0011075737,0.0025958316,0.0020662742,0.0031167308],"category_scores_gemma":[0.027628321,0.00044727046,0.0021230804,0.0022164972,0.0014762572,0.0013428426,0.00092368515,0.0020076318,0.00046827114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003852883,0.00021690952,0.023721637,0.00012263209,0.00011818702,0.00055920234,0.0004772533,0.9375472,0.0004871736,0.015748933,0.0022118147,0.018403713],"study_design_scores_gemma":[0.000020260979,0.00016564569,0.0076747052,0.000015989357,0.000032544118,0.00016683059,0.00021975023,0.98520327,0.00030596246,0.0054757237,0.0006827831,0.000036622594],"about_ca_topic_score_codex":0.017796868,"about_ca_topic_score_gemma":0.014147445,"teacher_disagreement_score":0.017796868,"about_ca_system_score_codex":0.0024285621,"about_ca_system_score_gemma":0.0013202135,"threshold_uncertainty_score":0.07664198},"labels":[],"label_agreement":null},{"id":"W2071959028","doi":"10.1080/00221686.2008.9521839","title":"Numerical investigation of flow recirculation in a draft tube","year":2008,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and 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; Manitoba Hydro","keywords":"Mechanics; Turbulence; Backflow; Flow (mathematics); Computational fluid dynamics; Reynolds number; Tube (container); Head (geology); Materials science; Physics; Geology; Inlet","score_opus":0.06699681702006464,"score_gpt":0.2914386624837353,"score_spread":0.22444184546367063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071959028","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":[0.9833218,0.00011112525,0.013331915,0.0000895731,0.000029187931,0.000044800116,0.00013775975,0.00010703457,0.0028269065],"genre_scores_gemma":[0.991379,0.00006124903,0.007306725,0.0000082160805,0.0000034171055,0.000027020667,0.00008893792,0.00001101864,0.001114364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983,0.000036833986,0.000012283505,0.00002376068,0.00004865834,0.000048486254],"domain_scores_gemma":[0.99922466,0.00039196282,0.0001006902,0.0000552962,0.00014638495,0.00008102005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041456343,0.00039139818,0.00088110694,0.0004683294,0.0006620646,0.00071267993,0.000499822,0.0011637554,0.0012083206],"category_scores_gemma":[0.0016017671,0.00024039613,0.0004998413,0.00052138977,0.0008819792,0.00040530093,0.00050795457,0.0004153532,0.00012241249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004960062,0.00016578966,0.0059503694,0.00012857302,0.000023408546,0.001022005,0.00020727444,0.9395972,0.044100787,0.0025897783,0.00031202167,0.0054067983],"study_design_scores_gemma":[0.00004497386,0.00027270394,0.0016271401,0.0000090592175,0.000010511341,0.00009759586,0.00006586571,0.9905133,0.006790693,0.00021015247,0.000340772,0.000017185721],"about_ca_topic_score_codex":0.005426022,"about_ca_topic_score_gemma":0.0020511916,"teacher_disagreement_score":0.005426022,"about_ca_system_score_codex":0.0005921275,"about_ca_system_score_gemma":0.0007087829,"threshold_uncertainty_score":0.010788858},"labels":[],"label_agreement":null},{"id":"W207211564","doi":"10.21236/ada446913","title":"Results and Lessons Learned Interim Report: Altus AFB Site","year":2005,"lang":"en","type":"report","venue":"","topic":"Water Systems and 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":"","keywords":"Interim; Geography; Archaeology","score_opus":0.06349111841137772,"score_gpt":0.30407632703470006,"score_spread":0.24058520862332233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W207211564","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":[0.21449667,0.0012781742,0.028528865,0.012722547,0.001777802,0.0050593694,0.15635262,0.004684965,0.575099],"genre_scores_gemma":[0.45240292,0.0010739466,0.04508464,0.0018589325,0.000300786,0.0019468163,0.08979153,0.0010944098,0.40644604],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99605054,0.00066379644,0.00012422424,0.00031701964,0.0023850237,0.00045947617],"domain_scores_gemma":[0.99449325,0.0003862476,0.0001488115,0.00046965035,0.004158204,0.00034383958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043777972,0.00093394134,0.00046847007,0.0007768132,0.0013001814,0.002333256,0.0013119776,0.0010425752,0.030502787],"category_scores_gemma":[0.004124168,0.00020845911,0.00059855264,0.0008838919,0.0002624014,0.0010496515,0.0006578643,0.0008287351,0.01220278],"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":[0.0015438049,0.0018861737,0.06460986,0.0005244353,0.0001333209,0.0009140236,0.0008606367,0.015277287,0.013119785,0.002774665,0.58016527,0.31819072],"study_design_scores_gemma":[0.00033784617,0.0040889643,0.107415974,0.00026993768,0.0002595485,0.00040845465,0.00453818,0.004991305,0.041585825,0.0015374317,0.8343792,0.00018734718],"about_ca_topic_score_codex":0.08626392,"about_ca_topic_score_gemma":0.11063587,"teacher_disagreement_score":0.08626392,"about_ca_system_score_codex":0.0025233787,"about_ca_system_score_gemma":0.003807092,"threshold_uncertainty_score":0.17152363},"labels":[],"label_agreement":null},{"id":"W2072953516","doi":"10.1080/1573062x.2011.630093","title":"Exploration of the relationship between water main breaks and temperature covariates","year":2012,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Environmental science; Air temperature; Homogeneous; Atmospheric sciences; Climatology; Meteorology; Hydrology (agriculture); Geography; Engineering; Mathematics; Geology; Geotechnical engineering","score_opus":0.022378947841864727,"score_gpt":0.19933925350792975,"score_spread":0.17696030566606502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072953516","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":[0.993853,0.00013539335,0.00501836,0.000087606386,0.000008000966,0.000016341066,0.0006665266,0.000046249883,0.00016845058],"genre_scores_gemma":[0.9969501,0.000047498426,0.0015693919,0.000011327987,0.0000066035936,0.000014060848,0.001098931,0.000011623395,0.00029041478],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969927,0.0017371242,0.00013554507,0.00060749456,0.00025441815,0.000272739],"domain_scores_gemma":[0.9285677,0.06430885,0.0033433165,0.0020527225,0.0008055468,0.0009219729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062053753,0.0007907177,0.00089021144,0.00097028527,0.00048290243,0.0012186511,0.0010137289,0.0011307375,0.0023274557],"category_scores_gemma":[0.02232994,0.00050109974,0.0019037294,0.001504099,0.000674989,0.001166834,0.0008204659,0.0021741902,0.0003005344],"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":[0.0005709036,0.00019533189,0.96065956,0.00004361899,0.0009106597,0.0001980172,0.00016819627,0.030290648,0.0010924187,0.00030679506,0.00023704323,0.005326771],"study_design_scores_gemma":[0.000028681321,0.00066001585,0.8147378,0.000019087885,0.00026962438,0.00013441518,0.0005692219,0.18112254,0.0009927368,0.00056809495,0.00085569016,0.000042042655],"about_ca_topic_score_codex":0.028843585,"about_ca_topic_score_gemma":0.027121684,"teacher_disagreement_score":0.028843585,"about_ca_system_score_codex":0.0005684995,"about_ca_system_score_gemma":0.001125393,"threshold_uncertainty_score":0.05735141},"labels":[],"label_agreement":null},{"id":"W2074447032","doi":"10.1061/41109(373)58","title":"Community-Driven and Reliability-Based Budget Allocation for Water Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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; Aecom (Canada)","funders":"","keywords":"Computer science; Robustness (evolution); Reliability (semiconductor); Criticality; Budget constraint; Analytic hierarchy process; Mains electricity; Reliability engineering; Hierarchy; Service (business); Operations research; Engineering; Business","score_opus":0.006104811126671634,"score_gpt":0.1926765844984627,"score_spread":0.18657177337179107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074447032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062477782,0.00012816493,0.9299307,0.0003698671,0.000017964812,0.00041051826,0.00012288307,0.00013718706,0.0064049833],"genre_scores_gemma":[0.7734222,0.00018231067,0.22250967,0.000032528933,0.000012269565,0.0007022609,0.00012186147,0.000056974197,0.0029599492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977679,0.0016136899,0.00005069584,0.00012185354,0.00028039748,0.00016553015],"domain_scores_gemma":[0.9976477,0.0014332029,0.00029136424,0.00009134921,0.00042354854,0.00011281521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004118608,0.00053840654,0.00047591672,0.0013277448,0.0005270967,0.0011024483,0.0014415722,0.000777977,0.0025978892],"category_scores_gemma":[0.008343957,0.00051922095,0.00042314111,0.0011874299,0.00062351127,0.0013878524,0.0008324121,0.00061992137,0.00019460464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024344095,0.00002927628,0.0006153391,0.000033745902,0.000009530531,0.000025206462,0.00008791377,0.9621615,0.00024494476,0.022240628,0.00027963307,0.014247977],"study_design_scores_gemma":[0.0000077805535,0.000016262728,0.0001801737,0.0000082000915,0.0000034524282,0.0000075863254,0.000058070244,0.9922686,0.00012893845,0.0067544654,0.00056123326,0.0000051914003],"about_ca_topic_score_codex":0.016719012,"about_ca_topic_score_gemma":0.021289852,"teacher_disagreement_score":0.016719012,"about_ca_system_score_codex":0.0035693948,"about_ca_system_score_gemma":0.0033806202,"threshold_uncertainty_score":0.033243418},"labels":[],"label_agreement":null},{"id":"W2074944457","doi":"10.1139/l05-121","title":"Hydrograph-based storm sewer design optimization by genetic algorithm","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":63,"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":"Genetic algorithm; Benchmark (surveying); Hydrograph; Mathematical optimization; Storm Water Management Model; Storm; Optimal design; Computer science; Network planning and design; Scheme (mathematics); Engineering; Stormwater; Mathematics; Drainage basin; Surface runoff","score_opus":0.0034189620426690317,"score_gpt":0.13561696603036094,"score_spread":0.1321980039876919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074944457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06972069,0.00027093303,0.91976357,0.0002609519,0.000028841125,0.00011952696,0.00017720045,0.00042908962,0.009229143],"genre_scores_gemma":[0.8419352,0.00038756317,0.14962582,0.0000753749,0.000018597442,0.0005116024,0.00026459247,0.000067332134,0.0071139084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998702,0.000043207936,0.0000038923727,0.00001991294,0.000039522565,0.000023177354],"domain_scores_gemma":[0.99982303,0.00009752091,0.00002573165,0.000007993476,0.000037101738,0.000008664863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003439498,0.00046967555,0.00080536347,0.0005845432,0.00032981517,0.0006223479,0.00072204944,0.0011140503,0.0018928762],"category_scores_gemma":[0.0008556805,0.00041434634,0.0005688212,0.00067887525,0.00053584564,0.00046154932,0.00040240752,0.00044885866,0.00021499103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037243335,0.0000034099314,0.00005672671,0.000003645965,0.0000028306229,0.00000490252,0.0000031264565,0.99785966,0.00008263548,0.0006140564,0.00006488986,0.0013003988],"study_design_scores_gemma":[0.0000041705366,0.000005023197,0.000025859728,0.0000010418747,0.0000013088442,0.0000015102651,0.0000013487286,0.99942005,0.000045237193,0.0003717315,0.000121865254,8.1753353e-7],"about_ca_topic_score_codex":0.01790218,"about_ca_topic_score_gemma":0.012487511,"teacher_disagreement_score":0.01790218,"about_ca_system_score_codex":0.0011524936,"about_ca_system_score_gemma":0.001795826,"threshold_uncertainty_score":0.035596013},"labels":[],"label_agreement":null},{"id":"W2076408219","doi":"10.1139/l06-042","title":"Possibilistic approach for consideration of uncertainties to estimate structural capacity of ageing cast iron water mains","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":63,"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":"Mains electricity; Service life; Safety factor; Fuzzy logic; Monte Carlo method; Pipeline transport; Factor of safety; Pipeline (software); Structural engineering; Engineering; Reliability engineering; Computer science; Mathematics","score_opus":0.009683813522707304,"score_gpt":0.18802696057274798,"score_spread":0.17834314705004067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076408219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019712126,0.0001571491,0.978512,0.000087899294,0.0000096483955,0.000040926505,0.00004790017,0.0000413391,0.0013909547],"genre_scores_gemma":[0.87048507,0.00026844803,0.12688461,0.000047274752,0.00003860323,0.00031099442,0.0001362085,0.000026561635,0.0018023041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836355,0.0007292096,0.000076120006,0.00019245424,0.00054173754,0.00009690728],"domain_scores_gemma":[0.99638414,0.0029314344,0.00022747501,0.00008916498,0.00029734676,0.00007052333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028791556,0.0011952171,0.0009635781,0.0021834373,0.00062535383,0.0018537202,0.001863214,0.001607319,0.0017104066],"category_scores_gemma":[0.008092907,0.0009965625,0.0017180305,0.0010663063,0.0013952883,0.0014628433,0.0012625211,0.0012895904,0.00018086677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003314187,0.00001561058,0.00036599708,0.00004628775,0.00004675269,0.00011734248,0.0000522618,0.9792597,0.00064132456,0.014431213,0.00008825384,0.004902038],"study_design_scores_gemma":[0.0000026458615,0.0000128571955,0.00008632889,0.0000048086736,0.0000092479995,0.000017131315,0.000008149882,0.994361,0.0001611732,0.0052153,0.00011504454,0.0000063658963],"about_ca_topic_score_codex":0.0075907465,"about_ca_topic_score_gemma":0.004983842,"teacher_disagreement_score":0.0075907465,"about_ca_system_score_codex":0.0019055923,"about_ca_system_score_gemma":0.0014009378,"threshold_uncertainty_score":0.015226603},"labels":[],"label_agreement":null},{"id":"W2077109823","doi":"10.1002/cjce.5450790105","title":"Transient analysis of gas flow in a straight pipe","year":2001,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and 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":"Mechanics; Pressure drop; Volumetric flow rate; Pipe flow; Flow coefficient; Flow (mathematics); Transient (computer programming); Mass flow rate; Work (physics); Steady state (chemistry); Transient flow; Pipeline transport; Materials science; Thermodynamics; Surge; Chemistry; Physics; Engineering; Mechanical engineering; Meteorology; Turbulence; Computer science","score_opus":0.005115395268088949,"score_gpt":0.15534308858426757,"score_spread":0.15022769331617863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077109823","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":[0.9641892,0.000087704124,0.033515967,0.000034322184,0.000008756293,0.000019642563,0.00007113648,0.00029131264,0.001782092],"genre_scores_gemma":[0.99793315,0.0000269478,0.0016954063,0.0000032601195,9.743659e-7,0.000008790327,0.000031470725,0.0000080131385,0.00029205467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989736,0.000019077475,0.000004977701,0.000014467388,0.000035706504,0.000028341174],"domain_scores_gemma":[0.999713,0.00016826314,0.000039122817,0.000020532805,0.00004341136,0.000015650208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002020593,0.00019994538,0.00033398427,0.00036303676,0.0002637229,0.00025933696,0.00032725156,0.00045434493,0.0011278527],"category_scores_gemma":[0.00076144264,0.0001642094,0.00039149224,0.00025435057,0.000513517,0.00031442058,0.00019348602,0.00021213198,0.00012176101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024475684,0.000069473135,0.0042714267,0.000063677275,0.000026151125,0.0004513671,0.00013987433,0.93218905,0.054274876,0.0013680691,0.000108374385,0.0067929067],"study_design_scores_gemma":[0.000008767677,0.00011336046,0.0012143513,0.0000032743676,0.0000059872073,0.00003520129,0.000021375683,0.9879418,0.010342354,0.0001801482,0.00012658664,0.000006766877],"about_ca_topic_score_codex":0.003182913,"about_ca_topic_score_gemma":0.0008063376,"teacher_disagreement_score":0.003182913,"about_ca_system_score_codex":0.00039875493,"about_ca_system_score_gemma":0.00037111028,"threshold_uncertainty_score":0.0063287616},"labels":[],"label_agreement":null},{"id":"W2079487548","doi":"10.1007/s12667-010-0016-3","title":"An MILP-based formulation for minimizing pumping energy costs of oil pipelines: beneficial to both the environment and pipeline companies","year":2010,"lang":"en","type":"article","venue":"Energy Systems","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Pipeline transport; Mathematical optimization; Integer programming; Electricity; Schedule; Linear programming; Pipeline (software); Time horizon; Scheduling (production processes); Computer science; Engineering; Environmental engineering; Mathematics","score_opus":0.008727705600298395,"score_gpt":0.19780763309837318,"score_spread":0.18907992749807478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079487548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02017426,0.0005128525,0.95736617,0.0014282455,0.000165941,0.00016601349,0.00038816017,0.00018373776,0.01961457],"genre_scores_gemma":[0.6639344,0.0008397804,0.31811512,0.0004084348,0.00020906219,0.00060836883,0.00051830406,0.00023222687,0.015134369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953866,0.00021653746,0.000014747637,0.00005411692,0.000116135074,0.00005973152],"domain_scores_gemma":[0.99923456,0.00049245404,0.00005594057,0.000024944742,0.00014404955,0.000048063866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013910102,0.0012051293,0.0011872002,0.0007127095,0.00062940037,0.002011702,0.0010496946,0.0021479311,0.0052636075],"category_scores_gemma":[0.00328618,0.001085859,0.0008751683,0.0010580564,0.0008024926,0.0014820258,0.0011179205,0.0017010059,0.0004554105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020889147,0.000016703973,0.000063295556,0.000041167412,0.000007461016,0.000028354754,0.000008771471,0.98973876,0.00019878756,0.0052977144,0.0007660765,0.0038120286],"study_design_scores_gemma":[0.0000073570186,0.000010827357,0.000027600758,0.00000677697,0.0000045771735,0.0000040095356,0.000009277112,0.9967961,0.00012112918,0.0026273963,0.00038250556,0.0000024567578],"about_ca_topic_score_codex":0.008487502,"about_ca_topic_score_gemma":0.010008891,"teacher_disagreement_score":0.008487502,"about_ca_system_score_codex":0.0014256502,"about_ca_system_score_gemma":0.0025256611,"threshold_uncertainty_score":0.017608464},"labels":[],"label_agreement":null},{"id":"W2079995886","doi":"10.2478/v10047-008-0024-4","title":"KPIM of Gas Transportation: Robust Modification of Gas Pipeline Equations","year":2008,"lang":"en","type":"article","venue":"Latvian Journal of Physics and Technical Sciences","topic":"Water Systems and 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 Saskatchewan","funders":"","keywords":"Flow (mathematics); Volumetric flow rate; Pipeline (software); Pipeline transport; Real gas; Turbulence; Mechanics; Darcy–Weisbach equation; Mathematics; Physics; Engineering; Mechanical engineering; Geotechnical engineering; Porosity; Porous medium","score_opus":0.03925059158691733,"score_gpt":0.22970933200050078,"score_spread":0.19045874041358346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079995886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050459947,0.00014301088,0.9860815,0.00012363242,0.00010934814,0.00008957145,0.00017687162,0.00081429793,0.007415678],"genre_scores_gemma":[0.51638585,0.0005698814,0.45575723,0.00029347572,0.00018065191,0.00051048916,0.00090124656,0.0014657339,0.023935357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998835,0.00020787747,0.00006370852,0.00019269265,0.0005905075,0.00011020554],"domain_scores_gemma":[0.99868757,0.0002881979,0.00018157302,0.00028076192,0.0005304441,0.00003147632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012213273,0.001114177,0.0009955899,0.00047016717,0.00037445384,0.0012147514,0.002398404,0.0011253945,0.005336671],"category_scores_gemma":[0.005015057,0.0006343077,0.001191541,0.00059022306,0.00064297806,0.0017728219,0.0016078088,0.0022746853,0.0023181194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010038762,0.0000419548,0.0009662436,0.00017303227,0.00002981943,0.00018342867,0.00008340557,0.8683287,0.010016662,0.048313357,0.0050331024,0.06673004],"study_design_scores_gemma":[0.000004103191,0.000015562382,0.00016350561,0.000005059811,0.000004115983,0.000023740009,0.0000037624422,0.9924489,0.0012524455,0.0024727976,0.0035958923,0.000010118121],"about_ca_topic_score_codex":0.009883905,"about_ca_topic_score_gemma":0.004016757,"teacher_disagreement_score":0.009883905,"about_ca_system_score_codex":0.0014624796,"about_ca_system_score_gemma":0.0018757408,"threshold_uncertainty_score":0.019652784},"labels":[],"label_agreement":null},{"id":"W2080189693","doi":"10.2166/aqua.2013.125","title":"Probabilistic modeling of cast iron water distribution pipe corrosion","year":2013,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Icon; Computer science; Art history; World Wide Web; Art","score_opus":0.017037725613366677,"score_gpt":0.23321827397235487,"score_spread":0.2161805483589882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080189693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21906385,0.0031301263,0.7441084,0.0018558328,0.00021293019,0.0001498601,0.0014559313,0.0009509709,0.029072117],"genre_scores_gemma":[0.97122824,0.0011860856,0.01169473,0.00005200032,0.00008457915,0.00006711902,0.0004609696,0.00007530326,0.0151509065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996171,0.00009380283,0.000015370111,0.00007096082,0.00013577568,0.000067068744],"domain_scores_gemma":[0.99900717,0.0004049244,0.00021861892,0.00005192536,0.00025336185,0.00006408332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074396975,0.00073345547,0.0007674909,0.0009996202,0.0003295194,0.001080867,0.0011872725,0.0011772542,0.0023139678],"category_scores_gemma":[0.002809015,0.000764095,0.0010088321,0.0005872751,0.0007913954,0.00091812626,0.0005383598,0.0007624152,0.00052515545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004395763,0.000018284883,0.0025360822,0.000047660793,0.000022840679,0.00009842253,0.000039199764,0.9734494,0.0012779145,0.014087249,0.0012268407,0.0071522123],"study_design_scores_gemma":[0.000003497551,0.000012289525,0.00083266076,0.00000519833,0.0000057067014,0.000031722422,0.0000077049845,0.99602014,0.00015859201,0.0025095928,0.0004064191,0.000006384465],"about_ca_topic_score_codex":0.029233325,"about_ca_topic_score_gemma":0.018563587,"teacher_disagreement_score":0.029233325,"about_ca_system_score_codex":0.0014121063,"about_ca_system_score_gemma":0.0007232933,"threshold_uncertainty_score":0.05812633},"labels":[],"label_agreement":null},{"id":"W2080355597","doi":"10.1016/j.energy.2005.10.027","title":"Application of probabilistic networks for decision support in power system analysis","year":2005,"lang":"en","type":"article","venue":"Energy","topic":"Water Systems and 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":"Kinectrics (Canada)","funders":"","keywords":"Probabilistic logic; Decision support system; Computer science; Electric power system; Power (physics); Operations research; Risk analysis (engineering); Engineering; Data mining; Artificial intelligence; Business; Physics","score_opus":0.0034077441538377324,"score_gpt":0.18616210578752915,"score_spread":0.1827543616336914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080355597","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":[0.005140283,0.00041199123,0.99239725,0.00026197944,0.000043236527,0.000023132607,0.000067522546,0.00020489459,0.0014497486],"genre_scores_gemma":[0.62011,0.0024284103,0.37387437,0.00019143493,0.00035885183,0.0002832475,0.00031516203,0.000162775,0.0022757982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806017,0.0011157341,0.00011278266,0.0002153235,0.00043208065,0.000063810556],"domain_scores_gemma":[0.9872617,0.011077945,0.0005647137,0.0003627652,0.00063249696,0.00010038057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032441635,0.0010843767,0.001377749,0.0012775601,0.00067690125,0.0015825477,0.0013091249,0.001210431,0.0030788716],"category_scores_gemma":[0.02293974,0.00081107835,0.0008668342,0.0018383302,0.0008009467,0.0025668982,0.0013344632,0.0016661533,0.00048008046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059144484,0.00002975549,0.0004233153,0.00008786916,0.000060653972,0.00004153944,0.000032893164,0.93118906,0.0003570027,0.025736576,0.0004948486,0.0414873],"study_design_scores_gemma":[0.000006067113,0.000009095872,0.000038747567,0.000007840586,0.0000103991115,0.000012347413,0.0000029813423,0.96869177,0.00016296667,0.030552782,0.00050050823,0.0000044863023],"about_ca_topic_score_codex":0.004135415,"about_ca_topic_score_gemma":0.0036025073,"teacher_disagreement_score":0.004135415,"about_ca_system_score_codex":0.0008320212,"about_ca_system_score_gemma":0.0009061257,"threshold_uncertainty_score":0.017156959},"labels":[],"label_agreement":null},{"id":"W2080776187","doi":"10.1061/(asce)1076-0342(2006)12:3(184)","title":"Optimal Scheduling of Replacement and Rehabilitation of Water Distribution Systems","year":2006,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and 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":"Western University","funders":"","keywords":"Minification; Total cost; Scheduling (production processes); Operations research; Pipeline (software); Exponential distribution; Rehabilitation; Poisson process; Computer science; Pipeline transport; Poisson distribution; Mathematical optimization; Engineering; Mathematics; Statistics; Environmental engineering; Economics","score_opus":0.0022261917553837458,"score_gpt":0.17593391557074628,"score_spread":0.17370772381536254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080776187","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":[0.6140356,0.0006931015,0.3774542,0.00029673864,0.00008642953,0.00032893513,0.0002021138,0.000528597,0.006374309],"genre_scores_gemma":[0.98161215,0.00008343021,0.01691786,0.000013628429,0.0000099768395,0.00005592565,0.00007792816,0.00003045814,0.0011985719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991721,0.0002749074,0.00003303373,0.00011725411,0.00013446175,0.00026836354],"domain_scores_gemma":[0.9988863,0.00045909118,0.00023614484,0.00006641323,0.00014515367,0.00020698506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001406389,0.00064808596,0.0010104221,0.0005613982,0.00060301926,0.00081319996,0.0009146633,0.000705176,0.00247385],"category_scores_gemma":[0.003164249,0.0006553862,0.00039120868,0.0004902252,0.0006983931,0.00077871943,0.00044333487,0.00060525973,0.00024317837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015700099,0.000051715546,0.0008540772,0.0000344915,0.000013045346,0.000051466246,0.00003918196,0.98174083,0.0019346282,0.0015276944,0.00038342553,0.013212491],"study_design_scores_gemma":[0.000028384933,0.00012087937,0.0009056266,0.000004305089,0.000011272113,0.000018072216,0.000049535232,0.9957312,0.0010759332,0.0015323553,0.0005135003,0.000008998918],"about_ca_topic_score_codex":0.012212308,"about_ca_topic_score_gemma":0.0097270375,"teacher_disagreement_score":0.012212308,"about_ca_system_score_codex":0.001990959,"about_ca_system_score_gemma":0.0021623436,"threshold_uncertainty_score":0.024282455},"labels":[],"label_agreement":null},{"id":"W2082164090","doi":"10.1139/l11-004","title":"Critical superposition instant of surge waves in surge tank with long headrace tunnel","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"National Natural Science Foundation of China","keywords":"Surge tank; Surge; Superposition principle; Hydropower; Oscillation (cell signaling); Computer simulation; Mechanics; Marine engineering; Engineering; Geotechnical engineering; Environmental science; Physics; Meteorology; Mathematics; Electrical engineering; Mathematical analysis","score_opus":0.010021106017292608,"score_gpt":0.16296395921844203,"score_spread":0.15294285320114942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082164090","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":[0.96213514,0.0000741697,0.034954365,0.000052018746,0.000012363326,0.000012805327,0.000023633644,0.00009912006,0.0026363893],"genre_scores_gemma":[0.9990262,0.0000112888965,0.0008043315,0.0000022005615,0.0000024641124,0.000002495999,0.000008290035,0.000005785589,0.0001370004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998945,0.000016079559,0.000005324077,0.000012622208,0.0000368553,0.00003463708],"domain_scores_gemma":[0.99937207,0.00022823681,0.00016638187,0.000026762424,0.000056323872,0.00015019771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037496662,0.0002819846,0.00029423586,0.00041672564,0.00040611153,0.0003516561,0.00021668222,0.00032626654,0.0019038065],"category_scores_gemma":[0.0015084695,0.00019155492,0.00034929105,0.0002394927,0.00071574486,0.00062921864,0.00068985176,0.00038154828,0.00008761045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003834724,0.000188933,0.06311212,0.00030655987,0.00018404998,0.0139933415,0.0019940196,0.4590068,0.37358558,0.03891481,0.0012968535,0.043582227],"study_design_scores_gemma":[0.00006821397,0.0007840347,0.033924397,0.00003615182,0.00006609677,0.0011377184,0.0005707747,0.91433734,0.03970395,0.008447464,0.0008589243,0.00006486194],"about_ca_topic_score_codex":0.0007380898,"about_ca_topic_score_gemma":0.00063857215,"teacher_disagreement_score":0.0019038065,"about_ca_system_score_codex":0.00037810637,"about_ca_system_score_gemma":0.00024052337,"threshold_uncertainty_score":0.0063688755},"labels":[],"label_agreement":null},{"id":"W2083053874","doi":"10.1080/15732479.2010.502179","title":"Data acquisition and analysis for water main rehabilitation techniques","year":2010,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University; Aecom (Canada)","funders":"","keywords":"Mains electricity; Rehabilitation; Engineering; Downtime; Trenchless technology; Forensic engineering; Civil engineering; Reliability engineering; Environmental engineering; Pipeline transport","score_opus":0.0027007518957861756,"score_gpt":0.18825963581319244,"score_spread":0.18555888391740627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083053874","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17602141,0.0007851978,0.25018498,0.0013127874,0.00040165734,0.011003808,0.48867184,0.01680048,0.05481781],"genre_scores_gemma":[0.32277447,0.0010748317,0.403751,0.00054333033,0.00011730446,0.016546227,0.23510057,0.001258941,0.018833334],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9965719,0.00032001096,0.0005447865,0.0005686434,0.0017558725,0.00023874342],"domain_scores_gemma":[0.98581785,0.0024610776,0.0011921629,0.0016206185,0.0086614285,0.00024682874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025076903,0.001046145,0.0007641627,0.004941372,0.0009442061,0.0013359885,0.0010313748,0.0008699276,0.014535196],"category_scores_gemma":[0.011473755,0.0003789797,0.0009221683,0.00639648,0.00044363507,0.0010235753,0.0009883803,0.0010725739,0.006521943],"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":[0.0015706902,0.001316274,0.10613499,0.0028759858,0.00023725208,0.0010271163,0.0015179666,0.01971544,0.046321128,0.0054487376,0.107909225,0.7059252],"study_design_scores_gemma":[0.00031097155,0.001319928,0.29870448,0.0012597663,0.00026359063,0.00075780536,0.0030312734,0.11507318,0.12737276,0.0061274557,0.44536957,0.0004092586],"about_ca_topic_score_codex":0.026197804,"about_ca_topic_score_gemma":0.030756155,"teacher_disagreement_score":0.026197804,"about_ca_system_score_codex":0.0013625602,"about_ca_system_score_gemma":0.004383723,"threshold_uncertainty_score":0.052090585},"labels":[],"label_agreement":null},{"id":"W2083309713","doi":"10.1115/ipc2014-33740","title":"Pump Station Design","year":2014,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Jacobs (Canada)","funders":"","keywords":"Upgrade; Engineering; Work (physics); Sump (aquarium); Metering mode; Civil engineering; Construction engineering; Systems engineering; Computer science; Mechanical engineering; Waste management","score_opus":0.010562660793182835,"score_gpt":0.16920134650652122,"score_spread":0.1586386857133384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083309713","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":[0.018848266,0.00081793027,0.8579709,0.0005784742,0.00041959176,0.00061263156,0.0015480256,0.0025565808,0.116647676],"genre_scores_gemma":[0.4086147,0.0025796364,0.45187896,0.0004958112,0.000196457,0.0013666822,0.003833748,0.001659846,0.1293742],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989498,0.00016000053,0.00007225585,0.0001542033,0.00056750164,0.00009616961],"domain_scores_gemma":[0.99967325,0.00003588004,0.000031310235,0.000052705906,0.00016899538,0.000037851074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000775138,0.0009995255,0.00058211194,0.0009943609,0.0010388601,0.0026247555,0.0019331328,0.0012560287,0.030635215],"category_scores_gemma":[0.0012803327,0.00069809763,0.0007155184,0.0010452972,0.0005664609,0.0018102481,0.0015372313,0.0008627314,0.010103082],"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":[0.0005276218,0.00016582996,0.0042388365,0.0015264759,0.00009548503,0.00047411973,0.00034658177,0.2580212,0.03425108,0.119980626,0.042262845,0.53810936],"study_design_scores_gemma":[0.0001588462,0.000854784,0.0033977167,0.00016835812,0.00013051239,0.0007532684,0.00030062554,0.21900274,0.030366795,0.020046918,0.72466177,0.00015764807],"about_ca_topic_score_codex":0.001997703,"about_ca_topic_score_gemma":0.0024174317,"teacher_disagreement_score":0.030635215,"about_ca_system_score_codex":0.0010054178,"about_ca_system_score_gemma":0.0017229413,"threshold_uncertainty_score":0.102485},"labels":[],"label_agreement":null},{"id":"W2083565072","doi":"10.1021/ie030268+","title":"Computer-Aided Simulation Model for Natural Gas Pipeline Network System Operations","year":2004,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":15,"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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Natural gas; Pipeline (software); Compressor station; Computer science; Pipeline transport; Gas compressor; Simulation; Petroleum engineering; Engineering; Mechanical engineering; Operating system","score_opus":0.07348620538950676,"score_gpt":0.2977144866896324,"score_spread":0.22422828130012565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083565072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05999334,0.0002693284,0.8809134,0.00049402926,0.00013797756,0.00031872964,0.0020510324,0.0028854366,0.052936696],"genre_scores_gemma":[0.82800555,0.0006325473,0.12679619,0.000121184356,0.00005165283,0.001294712,0.0030182726,0.00035043058,0.03972947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997056,0.0000722387,0.00001598115,0.00005133543,0.00011915623,0.00003566552],"domain_scores_gemma":[0.9995315,0.00022971544,0.000034411198,0.000031586915,0.00015437316,0.000018384239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036700215,0.0006951831,0.0006982503,0.00058271,0.00067307247,0.0009515515,0.0010827833,0.0010851584,0.008782719],"category_scores_gemma":[0.0011414504,0.00039131404,0.0008786371,0.0005946692,0.0004059879,0.0009298624,0.00048448506,0.0008292422,0.0012155605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014076484,0.000009828328,0.00017406596,0.000010932697,0.0000046660853,0.000022506945,0.00001291854,0.9948732,0.00035656733,0.0025609,0.00036008967,0.0016002177],"study_design_scores_gemma":[0.0000057721363,0.0000051658117,0.000046736186,0.0000017777118,0.0000022620852,0.000004965175,0.0000029300595,0.9979387,0.00018851072,0.00046811212,0.001332281,0.0000027500407],"about_ca_topic_score_codex":0.036288712,"about_ca_topic_score_gemma":0.018980248,"teacher_disagreement_score":0.036288712,"about_ca_system_score_codex":0.0015011609,"about_ca_system_score_gemma":0.0022664238,"threshold_uncertainty_score":0.072155},"labels":[],"label_agreement":null},{"id":"W2083687918","doi":"10.1016/j.proeng.2014.11.195","title":"Simultaneous Multi-pipe Failure Impact on Reliability of Water Distribution Systems","year":2014,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"University of British Columbia","keywords":"Reliability (semiconductor); Reliability engineering; Set (abstract data type); Computer science; Rank (graph theory); Engineering; Mathematics; Power (physics)","score_opus":0.0032607054752076922,"score_gpt":0.17645253495378935,"score_spread":0.17319182947858167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083687918","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":[0.975749,0.00016855953,0.02303099,0.000044626533,0.000010506017,0.000011946331,0.00006167442,0.000080127844,0.00084257877],"genre_scores_gemma":[0.9991258,0.000022777713,0.0007260698,0.0000019539311,0.0000018991558,0.0000027727983,0.000014069511,0.000003592534,0.00010116012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880123,0.0005538683,0.000056113055,0.00012511235,0.00030233897,0.0001613605],"domain_scores_gemma":[0.9933954,0.004790636,0.0008300947,0.00026601728,0.00054079003,0.00017700809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020838461,0.00075242127,0.0004737511,0.0014214125,0.00033182514,0.00084976165,0.00030090913,0.00058144814,0.0010772989],"category_scores_gemma":[0.005064343,0.00043435383,0.0007791748,0.00063685095,0.00072217645,0.001080677,0.0006845803,0.0004670515,0.00012958028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010078661,0.00013104205,0.026787868,0.00018888798,0.000177365,0.00093352096,0.00024348809,0.9141313,0.029278954,0.002079087,0.00022033553,0.024820251],"study_design_scores_gemma":[0.000019934841,0.0010226904,0.029876577,0.00001668515,0.000117217314,0.00024851074,0.00017141084,0.95426536,0.012021085,0.0020015615,0.00019424374,0.00004472461],"about_ca_topic_score_codex":0.0015028272,"about_ca_topic_score_gemma":0.0017512051,"teacher_disagreement_score":0.0020838461,"about_ca_system_score_codex":0.0004554231,"about_ca_system_score_gemma":0.00027092386,"threshold_uncertainty_score":0.011020601},"labels":[],"label_agreement":null},{"id":"W2085249544","doi":"10.2166/wqrjc.2014.030","title":"Experimental and numerical investigation of the breakage of wastewater flocs in orifice flow","year":2014,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Water Systems and 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":"Environment and Climate Change Canada; University of Toronto","funders":"Canadian Water Network","keywords":"Breakage; Body orifice; Particle (ecology); Mechanics; Orifice plate; Wastewater; Effluent; Volumetric flow rate; Flow (mathematics); Materials science; Environmental engineering; Environmental science; Composite material; Engineering; Physics; Mechanical engineering; Geology","score_opus":0.05206829347373288,"score_gpt":0.31009188981284874,"score_spread":0.25802359633911587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085249544","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":[0.9975434,0.00016686406,0.0017339538,0.000024023142,0.000013958212,0.000013491038,0.0000734888,0.000029576035,0.00040129517],"genre_scores_gemma":[0.99690956,0.00010990712,0.0026349283,0.0000056317563,0.0000029376815,0.000015410915,0.00006348137,0.0000042719457,0.00025381532],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996375,0.000033480654,0.0000387422,0.00007444446,0.00012721361,0.00008861271],"domain_scores_gemma":[0.99874276,0.00054405216,0.00023013519,0.00015814095,0.00022512805,0.0000997545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078313536,0.00036175744,0.0006124218,0.0006513961,0.0006810627,0.00043295205,0.0005177154,0.0009949697,0.00097422674],"category_scores_gemma":[0.0014063234,0.00021984309,0.00050336507,0.0005299988,0.00072972407,0.00045702548,0.0003783397,0.00081042014,0.00009307517],"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":[0.00091046345,0.00068920845,0.017544217,0.0004016082,0.00005138521,0.0008583172,0.00073351024,0.20277853,0.7561463,0.0014377501,0.00023523948,0.018213393],"study_design_scores_gemma":[0.00006818943,0.0017480577,0.021854175,0.00004195524,0.0000432655,0.00018209819,0.00028024268,0.5345788,0.43977064,0.0003857249,0.000979195,0.0000677422],"about_ca_topic_score_codex":0.0020858413,"about_ca_topic_score_gemma":0.0014394494,"teacher_disagreement_score":0.0020858413,"about_ca_system_score_codex":0.00046452443,"about_ca_system_score_gemma":0.0002855269,"threshold_uncertainty_score":0.00414747},"labels":[],"label_agreement":null},{"id":"W2085551846","doi":"10.1061/(asce)1076-0342(2008)14:3(272)","title":"Discussion of “Prediction of Water Pipe Asset Life Using Neural Networks” by D. Achim, F. Ghotb, and K. J. McManus","year":2008,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Artificial neural network; Asset (computer security); Engineering; Operations research; Computer science; Artificial intelligence; Computer security","score_opus":0.00692616076646919,"score_gpt":0.1820893027280745,"score_spread":0.17516314196160532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085551846","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008584226,0.026044162,0.2609102,0.662524,0.019233054,0.00007602787,0.0004047356,0.00017205469,0.022051567],"genre_scores_gemma":[0.5070332,0.03855823,0.12368425,0.1833411,0.07486298,0.000623285,0.00043294585,0.00030900977,0.071154974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977489,0.0011706762,0.00011582203,0.00036416843,0.0005209154,0.00007938195],"domain_scores_gemma":[0.9963527,0.0025530069,0.00013068283,0.00015209986,0.00073472556,0.000076789394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046185204,0.000719035,0.00080380286,0.00069603825,0.0012629981,0.0025673148,0.0028727234,0.007149654,0.00304091],"category_scores_gemma":[0.016561072,0.0005042002,0.0012397033,0.0014114986,0.0029734871,0.0038437415,0.0014577071,0.006360113,0.00089764805],"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":[0.0000927731,0.000053926957,0.0014874347,0.00032945952,0.00017553654,0.00028898584,0.00017802289,0.10689445,0.00082560343,0.694357,0.15088387,0.044433042],"study_design_scores_gemma":[0.00004326556,0.000057850237,0.0009877745,0.0003058655,0.000046146688,0.000121928686,0.00011650726,0.19306771,0.001775787,0.72414744,0.07925206,0.00007766625],"about_ca_topic_score_codex":0.009501362,"about_ca_topic_score_gemma":0.0073932232,"teacher_disagreement_score":0.009501362,"about_ca_system_score_codex":0.0018737197,"about_ca_system_score_gemma":0.0010222114,"threshold_uncertainty_score":0.024425387},"labels":[],"label_agreement":null},{"id":"W2087093687","doi":"10.1115/icone16-48531","title":"Thermalhydraulic Analysis of the Steam Generator Design for the 700 MWe Indian Nuclear Power Plant","year":2008,"lang":"en","type":"article","venue":"Volume 1: Plant Operations, Maintenance, Installations and Life Cycle; Component Reliability and Materials Issues; Advanced Applications of Nuclear Technology; Codes, Standards, Licensing and Regulatory Issues","topic":"Water Systems and 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":"Atomic Energy (Canada)","funders":"","keywords":"Boiler feedwater; Natural circulation; Boiler (water heating); Nuclear engineering; Header; Bundle; Pressurized water reactor; Nuclear power plant; Engineering; Heat transfer; Electricity generation; Air preheater; Steam drum; Nuclear power; Boiler feedwater pump; Environmental science; Power (physics); Waste management; Materials science; Mechanics; Superheated steam; Computer science; Nuclear physics; Physics","score_opus":0.006864814341342133,"score_gpt":0.20507385373543913,"score_spread":0.19820903939409698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087093687","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":[0.88831675,0.0001257744,0.08858288,0.000093447554,0.000016116252,0.0001529381,0.0005466563,0.00088860595,0.021276798],"genre_scores_gemma":[0.9867379,0.000045770783,0.010434952,0.000007810953,0.0000023266166,0.00006184187,0.00017951989,0.0000622707,0.0024676537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986434,0.000022331782,0.0000044429057,0.000016070811,0.00007328163,0.000019496934],"domain_scores_gemma":[0.9998679,0.000046443834,0.000018574387,0.000013481451,0.00004678239,0.0000068262966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015506308,0.00031955194,0.000236603,0.00032067898,0.00022823646,0.0003089083,0.00036539906,0.00017070635,0.002052499],"category_scores_gemma":[0.0003946614,0.0001576126,0.0003059772,0.00023062367,0.00026598672,0.00018407429,0.00018940688,0.0003171863,0.00023604195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021036719,0.000041365092,0.0071406937,0.00024142326,0.000024407662,0.00017668144,0.00016235976,0.871221,0.08379646,0.0037858437,0.00113969,0.03205968],"study_design_scores_gemma":[0.000047801455,0.00055259845,0.01145495,0.0000124199905,0.000039764443,0.00017505886,0.00010669095,0.9275592,0.05396887,0.00086056656,0.0051896577,0.00003251607],"about_ca_topic_score_codex":0.005038243,"about_ca_topic_score_gemma":0.005033684,"teacher_disagreement_score":0.005038243,"about_ca_system_score_codex":0.0008331624,"about_ca_system_score_gemma":0.000851818,"threshold_uncertainty_score":0.010017812},"labels":[],"label_agreement":null},{"id":"W2087335156","doi":"10.5942/jawwa.2014.106.0019","title":"Predicting the potential of contaminant intrusion in water distribution systems","year":2013,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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 British Columbia, Okanagan Campus; University of British Columbia; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Intrusion; Environmental science; Fuzzy logic; Groundwater; Distribution (mathematics); Environmental engineering; Petroleum engineering; Computer science; Engineering; Geology; Geotechnical engineering; Mathematics; Artificial intelligence","score_opus":0.0018015674697163275,"score_gpt":0.153928411014107,"score_spread":0.15212684354439068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087335156","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":[0.9705945,0.00004677821,0.027583214,0.00007008512,0.0000062613135,0.000025756874,0.00009541643,0.00009960961,0.0014782693],"genre_scores_gemma":[0.9980621,0.000013362697,0.001659188,0.0000027752008,0.0000012200028,0.0000041175217,0.00003028235,0.000002704383,0.00022422838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973816,0.00005476122,0.000013932218,0.0000568083,0.000070942624,0.00006540005],"domain_scores_gemma":[0.99869245,0.0008507281,0.0001676479,0.000045808396,0.00016683307,0.00007666519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004640396,0.00052751764,0.00051799306,0.0006699941,0.00035745013,0.0009162609,0.00047453234,0.0009763978,0.00057758036],"category_scores_gemma":[0.002506069,0.00050769077,0.0004373238,0.00044311222,0.0003873297,0.0008512865,0.00040451947,0.0005004031,0.000064062566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024336752,0.000013683068,0.0031885784,0.000004849197,0.000008223715,0.00004116118,0.000006797948,0.99490696,0.0005639663,0.00013750837,0.000027963892,0.001075979],"study_design_scores_gemma":[0.0000015969114,0.000008530977,0.000496872,6.385711e-7,0.0000019391264,0.0000045034467,0.000006074979,0.9990865,0.00028243737,0.000095747506,0.000013692985,0.0000014877571],"about_ca_topic_score_codex":0.030568575,"about_ca_topic_score_gemma":0.015836358,"teacher_disagreement_score":0.030568575,"about_ca_system_score_codex":0.0013309454,"about_ca_system_score_gemma":0.0008713989,"threshold_uncertainty_score":0.0607813},"labels":[],"label_agreement":null},{"id":"W2087776151","doi":"10.3826/jhr.2008.3015","title":"A transient 2-D water quality model for pipeline systems","year":2008,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and 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":"Turbulence; Mechanics; Advection; Transient (computer programming); Flow (mathematics); Diffusion; Physics; Thermodynamics; Computer science","score_opus":0.16213924566033913,"score_gpt":0.35515570380133527,"score_spread":0.19301645814099613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087776151","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":[0.18930596,0.00018740022,0.7947916,0.00048256235,0.00008032526,0.00015124853,0.0008198046,0.0009993329,0.013181752],"genre_scores_gemma":[0.9710841,0.00014287833,0.021468224,0.000067732355,0.00002195645,0.00020923968,0.0003247394,0.00005859198,0.006622527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998616,0.000028560473,0.000008706697,0.000039962142,0.000036542922,0.000024571033],"domain_scores_gemma":[0.9997973,0.000066398665,0.00003933871,0.000019268567,0.00005395054,0.000023755032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022931203,0.00052884116,0.00060471974,0.0003062025,0.00054186385,0.0007801705,0.0010273453,0.0012670542,0.002147969],"category_scores_gemma":[0.00068586844,0.0004325195,0.00081751554,0.0002963948,0.0007563503,0.0008853357,0.0007875339,0.0006587043,0.00023030199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019778225,0.000013197081,0.0003727026,0.000013819414,0.0000044287563,0.000035779907,0.000019092811,0.9953046,0.001642098,0.0016147037,0.000116244075,0.00084351044],"study_design_scores_gemma":[0.0000039992537,0.000008759198,0.000078569865,7.172853e-7,0.0000016107489,0.000004287444,0.0000036427655,0.99940956,0.00016152592,0.00018573887,0.00013877702,0.0000027385186],"about_ca_topic_score_codex":0.017235078,"about_ca_topic_score_gemma":0.007661679,"teacher_disagreement_score":0.017235078,"about_ca_system_score_codex":0.0010431862,"about_ca_system_score_gemma":0.0011765878,"threshold_uncertainty_score":0.03426951},"labels":[],"label_agreement":null},{"id":"W2088384059","doi":"10.2166/hydro.2011.098","title":"Hybrid Pareto archived dynamically dimensioned search for multi-objective combinatorial optimization: application to water distribution network design","year":2011,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":23,"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":"Benchmark (surveying); Metric (unit); Multi-objective optimization; Pareto principle; Mathematical optimization; Computer science; Search engine; Algorithm; Mathematics; Engineering","score_opus":0.01906586776065223,"score_gpt":0.21536217091353319,"score_spread":0.19629630315288096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088384059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061686296,0.0005195001,0.93156546,0.00015268005,0.00006650033,0.00010864151,0.00010622061,0.00058755017,0.0052070892],"genre_scores_gemma":[0.5908059,0.00027320665,0.40569475,0.00009646529,0.00003063479,0.0003390676,0.00026667886,0.00008232171,0.0024110118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994572,0.00021220543,0.00003250491,0.000045126322,0.00022228781,0.00003064056],"domain_scores_gemma":[0.9991315,0.00044414625,0.00006280935,0.00011525831,0.00020838027,0.00003792126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014044123,0.0005753329,0.00093616726,0.0011484344,0.00040059848,0.0008996431,0.0010694462,0.00077397726,0.0011335426],"category_scores_gemma":[0.0021967795,0.00033263504,0.0006883912,0.001308372,0.0005071157,0.00057725265,0.00097511633,0.0006326812,0.0001894582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043014596,0.0000488613,0.0005139175,0.00004624974,0.000036970978,0.000028079243,0.000029087625,0.92870975,0.0011191698,0.0035087243,0.0004496807,0.06546645],"study_design_scores_gemma":[0.0000140406555,0.000038288843,0.00008530415,0.0000046930613,0.0000050170124,0.00001576563,0.0000042441807,0.99725914,0.00075884577,0.001240794,0.0005690426,0.0000047807475],"about_ca_topic_score_codex":0.0020904934,"about_ca_topic_score_gemma":0.0022891022,"teacher_disagreement_score":0.0020904934,"about_ca_system_score_codex":0.00094931753,"about_ca_system_score_gemma":0.0010484038,"threshold_uncertainty_score":0.0074273944},"labels":[],"label_agreement":null},{"id":"W2089206969","doi":"10.1115/ipc2002-27113","title":"Effective Time Method for the Determination of Interface Diffusion of H2S Slugs in Natural Gas Pipeline Systems","year":2002,"lang":"en","type":"article","venue":"4th International Pipeline Conference, Parts A and B","topic":"Water Systems and 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":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Thermal diffusivity; Mechanics; Diffusion; Mass diffusivity; Pipeline (software); Flow (mathematics); Interface (matter); Slug flow; Fick's laws of diffusion; Work (physics); Materials science; Thermodynamics; Two-phase flow; Engineering; Physics; Mechanical engineering; Bubble","score_opus":0.01319895436364003,"score_gpt":0.2473866066280521,"score_spread":0.23418765226441207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089206969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057273757,0.00031298338,0.9402569,0.000026802067,0.000025041332,0.00004729152,0.00006477326,0.000597257,0.0013951878],"genre_scores_gemma":[0.65899724,0.00054092077,0.3363611,0.000023712266,0.000014972393,0.00015364298,0.00014067281,0.00013001253,0.0036377348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997832,0.000050698196,0.000010170154,0.000031549524,0.0001068,0.00001757976],"domain_scores_gemma":[0.9993612,0.00033628737,0.00008908987,0.000029752993,0.0001590935,0.000024578814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004560587,0.00045723986,0.00035266078,0.0010795138,0.00023111522,0.0004057244,0.0006147836,0.0005250233,0.0011550181],"category_scores_gemma":[0.0016211678,0.00020749892,0.00027498297,0.0005013958,0.00028518031,0.0006945868,0.0002686042,0.0004508931,0.00028909222],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042121,0.00013126887,0.0046580792,0.0003946734,0.000050846185,0.000332265,0.00030120643,0.5679957,0.2650799,0.023806239,0.0007999961,0.13602856],"study_design_scores_gemma":[0.0000060850534,0.000040080584,0.00038744375,0.000003008209,0.000003157113,0.000029905415,0.000017931301,0.9787467,0.019270306,0.0007373611,0.00074625603,0.00001174876],"about_ca_topic_score_codex":0.0032921361,"about_ca_topic_score_gemma":0.0021519216,"teacher_disagreement_score":0.0032921361,"about_ca_system_score_codex":0.00061077974,"about_ca_system_score_gemma":0.00061714713,"threshold_uncertainty_score":0.006545961},"labels":[],"label_agreement":null},{"id":"W2089245452","doi":"10.1007/s11269-013-0272-9","title":"A CFD Modeling Approach for Municipal Sewer System Design Optimization to Minimize Emissions into Receiving Water Body","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Computational fluid dynamics; Volume of fluid method; Turbulence; Environmental science; Hydrogeology; Marine engineering; Combined sewer; Flow (mathematics); Environmental engineering; Engineering; Stormwater; Geotechnical engineering; Meteorology; Mechanics; Surface runoff","score_opus":0.016331034851378424,"score_gpt":0.1965034849992662,"score_spread":0.18017245014788777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089245452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041724347,0.00034712552,0.922139,0.0005358812,0.00012932163,0.00015832734,0.00025592235,0.00047976885,0.03423041],"genre_scores_gemma":[0.7868433,0.0004632722,0.19251066,0.00019969566,0.000084565174,0.00045778698,0.000257635,0.00023523235,0.018947866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997999,0.000057772548,0.0000084371895,0.00002683449,0.0000748043,0.00003219853],"domain_scores_gemma":[0.9997502,0.00012654583,0.000021863563,0.000014024355,0.00007355493,0.000013911978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004171684,0.0006634504,0.0010047379,0.0005767178,0.00085229054,0.001047873,0.0008967034,0.0017068045,0.0038822263],"category_scores_gemma":[0.0009807635,0.0006594188,0.001158803,0.0006741815,0.00040567564,0.0005774722,0.00070242205,0.00079261314,0.0003968422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006289772,0.000012002666,0.000095228075,0.000010739469,0.0000058137107,0.000017037053,0.00001086081,0.9946241,0.0004019621,0.0017037444,0.00018596451,0.0029263368],"study_design_scores_gemma":[0.0000022394208,0.000004284565,0.000023759947,0.0000017038013,0.0000018581092,0.0000022556485,0.0000035084113,0.99925584,0.00010641587,0.0002665823,0.000329853,0.000001601584],"about_ca_topic_score_codex":0.030763663,"about_ca_topic_score_gemma":0.022941595,"teacher_disagreement_score":0.030763663,"about_ca_system_score_codex":0.0011057915,"about_ca_system_score_gemma":0.0022819063,"threshold_uncertainty_score":0.061169207},"labels":[],"label_agreement":null},{"id":"W2090113166","doi":"10.1016/j.proeng.2014.11.443","title":"An Energy Approach to Studying Pipe Network Transients","year":2014,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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":"Transient (computer programming); Pipeline (software); Energy (signal processing); Perspective (graphical); Computer science; Systems engineering; Selection (genetic algorithm); Risk analysis (engineering); Engineering; Mechanical engineering; Artificial intelligence; Physics; Business","score_opus":0.005685472133780592,"score_gpt":0.15669104681514603,"score_spread":0.15100557468136544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090113166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02376543,0.00032553606,0.95516056,0.00037909613,0.00005280514,0.000039796432,0.000112017275,0.000094556955,0.0200702],"genre_scores_gemma":[0.90893704,0.0010858686,0.08126177,0.00016409099,0.000119455515,0.00017097525,0.00011467969,0.00007073827,0.008075364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997669,0.00009852792,0.000013534448,0.00003939108,0.00006204018,0.000019513282],"domain_scores_gemma":[0.99962056,0.0002384444,0.000044923498,0.00004177542,0.000043479435,0.000010736963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035054403,0.0005984249,0.00034637525,0.000791645,0.0003869359,0.0007540862,0.0006406872,0.0007708355,0.0027259765],"category_scores_gemma":[0.0012773472,0.00022698168,0.00042219867,0.0007159564,0.00077346823,0.0014658497,0.00083216984,0.0008578904,0.00027459103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000496546,0.0000714894,0.0021778618,0.00015200264,0.00006649807,0.00036402457,0.00020897793,0.7003144,0.014942495,0.24524249,0.00079529086,0.035614837],"study_design_scores_gemma":[0.000009088798,0.00009606905,0.0012276743,0.000032713448,0.00002297632,0.00014266737,0.00019184177,0.8636266,0.0025166422,0.12588327,0.006227392,0.000023133616],"about_ca_topic_score_codex":0.0011583676,"about_ca_topic_score_gemma":0.0010348753,"teacher_disagreement_score":0.0027259765,"about_ca_system_score_codex":0.00048412423,"about_ca_system_score_gemma":0.0003433928,"threshold_uncertainty_score":0.009119332},"labels":[],"label_agreement":null},{"id":"W2090824016","doi":"10.1139/l07-095","title":"Impacts of cathodic protection on pipe break probabilities: a Toronto case study","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Cathodic protection; Environmental science; Forensic engineering; Water pipe; Engineering; Inlet; Mechanical engineering; Physics","score_opus":0.01232895874080642,"score_gpt":0.18102848166497318,"score_spread":0.16869952292416676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090824016","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":[0.9886537,0.00042575772,0.0017899259,0.00026840376,0.000009491557,0.000091997215,0.0028255738,0.00004354726,0.005891487],"genre_scores_gemma":[0.99550074,0.00033523413,0.0007884082,0.000012425939,0.000004392098,0.000024951845,0.0010211244,0.0000073831975,0.0023052876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99909985,0.0001823671,0.000039177798,0.00013052358,0.00029236352,0.0002556556],"domain_scores_gemma":[0.99620914,0.0018713537,0.00054167456,0.00021182829,0.00088517537,0.00028076262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009384687,0.0007663721,0.00037852023,0.0015255618,0.001023861,0.0009557888,0.0014075582,0.0011617584,0.0036308302],"category_scores_gemma":[0.0035507053,0.00042232685,0.0008267593,0.0021111239,0.0011027551,0.00054462557,0.0006768862,0.0006135579,0.00023613108],"study_design_candidate":"observational","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":[0.0010958626,0.00039616076,0.21146974,0.00042682476,0.00035441856,0.0070427004,0.0011501652,0.7344835,0.006554166,0.005510688,0.007086472,0.02442927],"study_design_scores_gemma":[0.0002764081,0.0013687717,0.47163802,0.00012647659,0.0004882424,0.0016255279,0.005573785,0.4960468,0.00833083,0.0018916305,0.012385644,0.0002479077],"about_ca_topic_score_codex":0.83860284,"about_ca_topic_score_gemma":0.88662744,"teacher_disagreement_score":0.16139716,"about_ca_system_score_codex":0.01836827,"about_ca_system_score_gemma":0.0043680896,"threshold_uncertainty_score":0.3246953},"labels":[],"label_agreement":null},{"id":"W2091516449","doi":"10.1016/j.ijmultiphaseflow.2005.03.005","title":"Air–water counter-current slug flow data in vertical-to-horizontal pipes containing orifice type obstructions","year":2005,"lang":"en","type":"article","venue":"International Journal of Multiphase Flow","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"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":"Slugging; Slug flow; Body orifice; Mechanics; Countercurrent exchange; Geology; Pipe flow; Two-phase flow; Geotechnical engineering; Flow (mathematics); Physics; Engineering; Thermodynamics","score_opus":0.023798541695262555,"score_gpt":0.28155317453474416,"score_spread":0.2577546328394816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091516449","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":[0.9971752,0.000035387413,0.0011992714,0.000025212292,0.000016616827,0.000010245504,0.0006754399,0.0002011101,0.00066136767],"genre_scores_gemma":[0.9988288,0.000014202899,0.00040550166,0.0000044910416,0.000004092503,0.00000412534,0.0004984268,0.000013399171,0.00022693626],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979943,0.000025894426,0.000019335597,0.000046503585,0.00006603109,0.00004282877],"domain_scores_gemma":[0.998708,0.00043480363,0.00014330052,0.0001212204,0.00048269468,0.00010993164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029622755,0.00035211822,0.00046482164,0.001371334,0.00042507867,0.00046311805,0.0003879359,0.0006025657,0.0017600064],"category_scores_gemma":[0.0013185694,0.00017281114,0.0002545012,0.000989757,0.000591052,0.00088827504,0.00030384003,0.00042901747,0.00030693086],"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":[0.014297548,0.001199647,0.3633516,0.00063209055,0.00013491047,0.001891656,0.0011846217,0.14201798,0.36861208,0.0025643627,0.008175328,0.09593832],"study_design_scores_gemma":[0.00016919692,0.0011320276,0.23447543,0.000034185905,0.00007825649,0.0002628846,0.0004513669,0.5617608,0.19829017,0.0006520762,0.0025709765,0.00012262564],"about_ca_topic_score_codex":0.009585756,"about_ca_topic_score_gemma":0.0068626404,"teacher_disagreement_score":0.009585756,"about_ca_system_score_codex":0.0004761685,"about_ca_system_score_gemma":0.0004207673,"threshold_uncertainty_score":0.019059956},"labels":[],"label_agreement":null},{"id":"W2091686119","doi":"10.1139/l10-066","title":"Effects of water–air mixtures on hydraulic transients","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidad Nacional Autónoma de México","keywords":"Pipeline transport; Transient (computer programming); Environmental science; Airflow; Hydraulic jump; Flow (mathematics); Hydraulics; Homogeneous; Atmospheric pressure; Secondary air injection; Mechanics; Two-phase flow; Petroleum engineering; Engineering; Geology; Environmental engineering; Waste management; Mechanical engineering; Thermodynamics","score_opus":0.0020501574592622575,"score_gpt":0.1405221970467031,"score_spread":0.13847203958744084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091686119","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":[0.99414647,0.00041038485,0.004530352,0.000038060214,0.000017322294,0.00001695391,0.00005161352,0.00010296624,0.0006858376],"genre_scores_gemma":[0.9991893,0.00009887686,0.0004116335,0.000008217189,0.0000032287364,0.000005001618,0.000021828178,0.00000883562,0.00025307309],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974495,0.000043071952,0.00001758321,0.000043332173,0.00008795975,0.00006308577],"domain_scores_gemma":[0.9993986,0.0004139358,0.000086466396,0.000026857944,0.000044665394,0.000029483672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023638878,0.00037858862,0.00059365045,0.00025711794,0.00030201682,0.00058583554,0.00028538893,0.0004521376,0.0017915793],"category_scores_gemma":[0.0011671453,0.00022795156,0.00026981183,0.00028683548,0.0005794673,0.0007114659,0.00044646472,0.00048158361,0.0001570776],"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":[0.0019787233,0.00006775042,0.0019249723,0.00021636719,0.000025598449,0.0007407372,0.0001359327,0.026827674,0.95926166,0.00028158497,0.000053877113,0.008485102],"study_design_scores_gemma":[0.000054502714,0.0013254294,0.008796843,0.000013469722,0.00003692068,0.00021569485,0.00017214775,0.051850993,0.9365817,0.00017719713,0.0007456842,0.000029419734],"about_ca_topic_score_codex":0.0010769895,"about_ca_topic_score_gemma":0.0007826109,"teacher_disagreement_score":0.0017915793,"about_ca_system_score_codex":0.000364042,"about_ca_system_score_gemma":0.0002124129,"threshold_uncertainty_score":0.0059933662},"labels":[],"label_agreement":null},{"id":"W2092699332","doi":"10.1061/(asce)wr.1943-5452.0000530","title":"The Challenge of Air Valves: A Selective Critical Literature Review","year":2015,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Sizing; Pipeline transport; Current (fluid); Computer science; Air bearing; Mechanical engineering; Environmental science; Engineering; Marine engineering; Electrical engineering","score_opus":0.01366560652047834,"score_gpt":0.24003369514637973,"score_spread":0.22636808862590138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092699332","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":[0.00024891467,0.99298054,0.00065711,0.0036571275,0.0011299693,0.00001836496,0.00004596925,0.000011615223,0.0012504536],"genre_scores_gemma":[0.0024611633,0.99392176,0.0007180083,0.0013526591,0.0011096485,0.00003059111,0.00005155271,0.0000073915494,0.0003472702],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977233,0.0006948845,0.00046335798,0.0002973973,0.0006772567,0.00014385639],"domain_scores_gemma":[0.977433,0.0151862055,0.0013886624,0.00043489042,0.0051965355,0.00036064742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004824332,0.0012027497,0.0019336795,0.012523721,0.0010975354,0.0037370247,0.0018711542,0.0029344636,0.004690661],"category_scores_gemma":[0.018827558,0.00078838685,0.0014805243,0.011356248,0.0019195452,0.0066501535,0.0016763358,0.002699706,0.0014505287],"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":[0.00011409854,0.000087094966,0.0006341472,0.10824272,0.00027964558,0.0005734864,0.0010299563,0.0012984126,0.0006964767,0.03508757,0.091516055,0.76044023],"study_design_scores_gemma":[0.000009946333,0.00010297214,0.0010837731,0.11734721,0.00043897523,0.00095872045,0.0015035196,0.0003751293,0.000450161,0.012927252,0.86473835,0.000064027045],"about_ca_topic_score_codex":0.0034479515,"about_ca_topic_score_gemma":0.004900806,"teacher_disagreement_score":0.012523721,"about_ca_system_score_codex":0.0024178512,"about_ca_system_score_gemma":0.008860138,"threshold_uncertainty_score":0.025513768},"labels":[],"label_agreement":null},{"id":"W2093598484","doi":"10.1139/l10-078","title":"Supporting a drinking water contaminant warning system using the adenosine triphosphate test","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph; LuminUltra Technologies (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; University of Guelph","keywords":"Contamination; False positive paradox; Environmental science; Ultrafiltration (renal); Adenosine triphosphate; Sample (material); Environmental chemistry; Chlorine; Chemistry; Chromatography; Computer science; Biology; Ecology; Biochemistry; Artificial intelligence","score_opus":0.005032925862916644,"score_gpt":0.17404728916440482,"score_spread":0.16901436330148817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093598484","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":[0.6702718,0.0007402721,0.30999297,0.0014997132,0.0004189414,0.001044086,0.0020873535,0.0066174334,0.007327412],"genre_scores_gemma":[0.86786985,0.00030693362,0.12465869,0.00048728107,0.00006663485,0.00028785196,0.0012204779,0.000078294455,0.005023903],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99581933,0.0008056099,0.00034426225,0.00056810916,0.002273672,0.00018895596],"domain_scores_gemma":[0.99411386,0.0017183733,0.00078148436,0.00044018615,0.002655532,0.0002905795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024445297,0.0007066342,0.0005647436,0.00084279367,0.00044224694,0.0013379201,0.0012717993,0.0018365216,0.0024020167],"category_scores_gemma":[0.006835201,0.00035659276,0.0003720905,0.00066224317,0.000519896,0.0011049849,0.0007848918,0.0006921049,0.0016074651],"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":[0.00090371835,0.00026287918,0.013371877,0.00023946517,0.000024676063,0.00018034302,0.00008673218,0.0019135458,0.9299666,0.0004932213,0.0013489542,0.0512079],"study_design_scores_gemma":[0.00007397746,0.0013686537,0.00803497,0.000025154111,0.000058155576,0.00037954867,0.00006307017,0.026778048,0.9556503,0.00047820184,0.0070251245,0.000064813474],"about_ca_topic_score_codex":0.002201115,"about_ca_topic_score_gemma":0.002413137,"teacher_disagreement_score":0.0024445297,"about_ca_system_score_codex":0.000521827,"about_ca_system_score_gemma":0.0015228499,"threshold_uncertainty_score":0.012928069},"labels":[],"label_agreement":null},{"id":"W2093828402","doi":"10.1016/j.proeng.2014.02.034","title":"Predicting the Leakage Exponents of Elastically Deforming Cracks in Pipes","year":2014,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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":"Golder Associates (Canada)","funders":"","keywords":"Leakage (economics); Materials science; Structural engineering; Composite material; Forensic engineering; Mechanics; Engineering; Physics","score_opus":0.0025489516520226228,"score_gpt":0.14414324070441978,"score_spread":0.14159428905239715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093828402","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":[0.92224646,0.00029535673,0.076299846,0.000043688477,0.000008550718,0.00002838526,0.00008043147,0.00026272677,0.000734565],"genre_scores_gemma":[0.99361414,0.00008959456,0.0060147853,0.000004795151,0.0000025609916,0.000009061459,0.000040198272,0.00001983267,0.00020499212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998653,0.000027591037,0.00001389711,0.000029547613,0.000049440536,0.000014092512],"domain_scores_gemma":[0.9981755,0.0009735827,0.00050880894,0.000096937554,0.0002040961,0.00004112952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003913168,0.0004073321,0.00026952487,0.00073457457,0.00010532446,0.00030842007,0.0002886023,0.00058454764,0.00029509046],"category_scores_gemma":[0.003331773,0.00029376606,0.00021681945,0.00031667048,0.00029142757,0.0011954426,0.00031792224,0.0003344708,0.00010498584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034725745,0.00011182284,0.08393775,0.0003245142,0.000030258101,0.00087326526,0.0003737195,0.68980557,0.16596322,0.0021806248,0.00038724553,0.05566473],"study_design_scores_gemma":[0.000006260106,0.00007744415,0.010672841,0.000013800351,0.0000044463213,0.00010120571,0.000036658126,0.9746663,0.013709525,0.0005419901,0.00015634201,0.000013165974],"about_ca_topic_score_codex":0.00069430284,"about_ca_topic_score_gemma":0.0010116916,"teacher_disagreement_score":0.00073457457,"about_ca_system_score_codex":0.00029693698,"about_ca_system_score_gemma":0.00017926437,"threshold_uncertainty_score":0.00215441},"labels":[],"label_agreement":null},{"id":"W2095059834","doi":"10.1061/9780784413548.057","title":"Sensor Placement Optimization for Water Quality Model Calibration","year":2014,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2014","topic":"Water Systems and 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":"Bentley (Canada)","funders":"","keywords":"Calibration; Intrusion; Water quality; Engineering; Computer science; Field (mathematics); Data quality; Data modeling; Wireless sensor network; Data mining; Remote sensing; Real-time computing; Database","score_opus":0.008249839022229867,"score_gpt":0.18452873911997753,"score_spread":0.17627890009774766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095059834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012483388,0.00007792175,0.98482436,0.00007086938,0.00001447521,0.000042315416,0.000048096612,0.00031743402,0.0021209973],"genre_scores_gemma":[0.6618119,0.00019705847,0.334066,0.00010745892,0.000019838813,0.00025956437,0.00023784007,0.00020926853,0.003091127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993691,0.00023183983,0.000021567364,0.00015510728,0.0001720016,0.00005037118],"domain_scores_gemma":[0.99930096,0.00035031265,0.00010474889,0.000084951214,0.00014029429,0.000018689374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010899568,0.001467437,0.0008504753,0.0006230061,0.0003674244,0.0006909374,0.0007307043,0.0009341398,0.0019356092],"category_scores_gemma":[0.0032162624,0.00069752504,0.00065330515,0.000691677,0.00055361516,0.001196113,0.0008963656,0.00092772744,0.00039461002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017223114,0.000018747845,0.0002073135,0.000027778873,0.00000888071,0.000011635729,0.000014200948,0.9823104,0.0022178849,0.001180203,0.00032092078,0.013664821],"study_design_scores_gemma":[0.000003498752,0.000020109423,0.0000994508,0.000002085396,0.000002358826,0.0000047468443,0.0000061587575,0.9974032,0.001162792,0.0009384261,0.00035327437,0.0000038463086],"about_ca_topic_score_codex":0.003953214,"about_ca_topic_score_gemma":0.0034939393,"teacher_disagreement_score":0.003953214,"about_ca_system_score_codex":0.000735942,"about_ca_system_score_gemma":0.0010037974,"threshold_uncertainty_score":0.007860422},"labels":[],"label_agreement":null},{"id":"W2095388792","doi":"10.1016/j.watres.2013.09.061","title":"Development of a system dynamics model for financially sustainable management of municipal watermain networks","year":2013,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and 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":"University of Waterloo","funders":"","keywords":"Causal loop diagram; System dynamics; Environmental economics; Sustainability; Service (business); Water supply; Business; Economics; Finance; Computer science; Engineering; Marketing; Environmental engineering","score_opus":0.029528223399113943,"score_gpt":0.2593871620828239,"score_spread":0.22985893868371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095388792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08606377,0.00025765065,0.8721695,0.0012119103,0.00014630205,0.00023905243,0.00091870944,0.00065178686,0.038341317],"genre_scores_gemma":[0.9313833,0.00025592686,0.054158162,0.000113523645,0.00003596621,0.0005094021,0.0005250694,0.00013366714,0.012884966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975497,0.00008360245,0.000010481532,0.000045120487,0.00005652284,0.00004937463],"domain_scores_gemma":[0.99954385,0.00023392492,0.00004367016,0.00002331722,0.00011372888,0.000041590003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006167634,0.0005955027,0.001015036,0.00049916666,0.0008720106,0.0013653551,0.0013045054,0.0019449772,0.0064915037],"category_scores_gemma":[0.0017094429,0.00056032103,0.00090193644,0.0005081812,0.00055410503,0.0012906367,0.0011251351,0.0014889452,0.0006222234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000492089,0.000007640952,0.00010229451,0.000007252552,0.000004219353,0.000013438391,0.0000074509926,0.99570435,0.00010251013,0.002997404,0.00014267142,0.00090588594],"study_design_scores_gemma":[0.0000030398962,0.0000031126349,0.000023494225,0.0000016446731,0.0000015542893,0.000001137677,0.000003597288,0.99897206,0.00003868028,0.0007569344,0.00019341413,0.0000013601182],"about_ca_topic_score_codex":0.035175413,"about_ca_topic_score_gemma":0.020033268,"teacher_disagreement_score":0.035175413,"about_ca_system_score_codex":0.0018266541,"about_ca_system_score_gemma":0.003156568,"threshold_uncertainty_score":0.06994134},"labels":[],"label_agreement":null},{"id":"W2096249987","doi":"10.1061/9780784413616.032","title":"Issues in Decision Support Tools for Sustainable Infrastructure Management","year":2014,"lang":"en","type":"article","venue":"Computing in Civil and Building Engineering (2014)","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Interoperability; Critical infrastructure; Public infrastructure; Sustainability; Variety (cybernetics); Decision support system; Computer science; Government (linguistics); Context (archaeology); Risk analysis (engineering); Work (physics); Population; Process management; Business; Engineering; Computer security","score_opus":0.003876234424901279,"score_gpt":0.20410946156917545,"score_spread":0.20023322714427416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096249987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005542739,0.0027637903,0.91751707,0.02986301,0.000677703,0.00027366824,0.00095842563,0.0019315317,0.040472053],"genre_scores_gemma":[0.14641114,0.0039517977,0.83769345,0.0017387653,0.0006258758,0.00043612515,0.0013355582,0.00033971242,0.0074675577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99111676,0.00437056,0.0009823046,0.00075402146,0.0023180754,0.00045829508],"domain_scores_gemma":[0.968012,0.024807496,0.00090969645,0.0015236248,0.004090007,0.0006572813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01480393,0.0017848426,0.0017903475,0.0026971353,0.0019964732,0.016086549,0.0043406156,0.004416344,0.012481339],"category_scores_gemma":[0.041473266,0.0010766392,0.001830251,0.005657944,0.0028402451,0.012675377,0.0035404442,0.0048281522,0.004664877],"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":[0.0000734993,0.00023366362,0.0012818122,0.0007593663,0.00011324424,0.00031410513,0.0006784366,0.16488807,0.0005741145,0.61448884,0.021878336,0.19471647],"study_design_scores_gemma":[0.000050681403,0.00004850205,0.00024714047,0.0005855669,0.000039761246,0.00010700048,0.0009030272,0.30588996,0.00084761204,0.57361144,0.11758979,0.000079510246],"about_ca_topic_score_codex":0.017384743,"about_ca_topic_score_gemma":0.014043185,"teacher_disagreement_score":0.017384743,"about_ca_system_score_codex":0.003489451,"about_ca_system_score_gemma":0.006856631,"threshold_uncertainty_score":0.078291595},"labels":[],"label_agreement":null},{"id":"W2097418253","doi":"10.1115/fedsm2003-45262","title":"Optimum Selection of Hydraulic Devices for Water Hammer Control in the Pipeline Systems Using Genetic Algorithm","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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","funders":"","keywords":"Water hammer; Sizing; Transient (computer programming); Genetic algorithm; Pipeline (software); Selection (genetic algorithm); Pipeline transport; Computer science; Hammer; Transient response; Hydraulic machinery; Steady state (chemistry); Control theory (sociology); Engineering; Control engineering; Mathematical optimization; Control (management); Mechanical engineering; Mathematics; Artificial intelligence","score_opus":0.011418534703223605,"score_gpt":0.20568006327004396,"score_spread":0.19426152856682036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097418253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22271504,0.00024899066,0.77176034,0.00019763976,0.000024085466,0.00013325107,0.000038720034,0.000370168,0.0045117484],"genre_scores_gemma":[0.9180565,0.00009675924,0.08037417,0.00003311448,0.0000069516855,0.00010030739,0.00003053315,0.000021345559,0.0012803447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997794,0.00007374005,0.000009853924,0.000041952153,0.000051760784,0.0000432685],"domain_scores_gemma":[0.9995376,0.00027079057,0.0000780036,0.000018002356,0.000073805924,0.000021885216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007099769,0.00062339014,0.00075816887,0.0006623461,0.00026145417,0.00070602866,0.00051557174,0.00078052515,0.0010707271],"category_scores_gemma":[0.0016147799,0.00041068564,0.00036497298,0.00037121857,0.00067276816,0.00048872846,0.00028574016,0.00039196853,0.00013670186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027475056,0.000021164264,0.000290717,0.00001976568,0.000009706753,0.000020081043,0.00002299106,0.9845205,0.0019755373,0.0010091005,0.00010189236,0.011981183],"study_design_scores_gemma":[0.000022475957,0.00004093174,0.00015929459,0.0000035185042,0.000009242605,0.000005654495,0.000007832287,0.99806935,0.0008638002,0.000665534,0.00014895402,0.0000035017265],"about_ca_topic_score_codex":0.0043485234,"about_ca_topic_score_gemma":0.0040404927,"teacher_disagreement_score":0.0043485234,"about_ca_system_score_codex":0.00078785996,"about_ca_system_score_gemma":0.0009539397,"threshold_uncertainty_score":0.008646429},"labels":[],"label_agreement":null},{"id":"W2098086366","doi":"10.2166/aqua.2013.213","title":"Influence of porous media on intrusion rate into water distribution pipes","year":2013,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Icon; Citation; Architecture; China; Download; Zhàng; Computer science; Library science; Art history; World Wide Web; Geography; History; Archaeology","score_opus":0.00871307597943684,"score_gpt":0.22968005013758122,"score_spread":0.22096697415814437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098086366","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":[0.987071,0.0025954861,0.0032647853,0.00015149923,0.000091631395,0.00006425541,0.00022986773,0.00016354109,0.006367905],"genre_scores_gemma":[0.9984724,0.00055004656,0.0003272132,0.000015895146,0.000007933824,0.000010289471,0.00006577332,0.00001575918,0.00053459784],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996189,0.000055942553,0.00003357829,0.00007382517,0.00012344598,0.00009430458],"domain_scores_gemma":[0.99863917,0.00066801195,0.00024514782,0.000043902353,0.00030088288,0.00010301476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039601207,0.00021247428,0.00032464875,0.0006867422,0.0003684175,0.0012314862,0.00022506929,0.0002971573,0.002883079],"category_scores_gemma":[0.0021397949,0.000246483,0.00036650483,0.0003798811,0.0004935643,0.0011785389,0.000567578,0.000407652,0.0004308451],"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":[0.0014873206,0.00021559175,0.03242989,0.00082192966,0.00010868044,0.00071996177,0.00040364108,0.017841544,0.8955538,0.0011690307,0.0014599017,0.047788616],"study_design_scores_gemma":[0.00006529884,0.0013339454,0.08001545,0.00011539222,0.00018199647,0.0003781365,0.0011074818,0.06323263,0.8463801,0.0006673739,0.006432306,0.000089861474],"about_ca_topic_score_codex":0.0020492082,"about_ca_topic_score_gemma":0.0017375305,"teacher_disagreement_score":0.002883079,"about_ca_system_score_codex":0.0004793861,"about_ca_system_score_gemma":0.00035516833,"threshold_uncertainty_score":0.009644866},"labels":[],"label_agreement":null},{"id":"W2098888815","doi":"10.1061/40934(252)18","title":"Sewer Pipeline Operational Condition Prediction Using Multiple Regression","year":2007,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Pipeline (software); Sanitary sewer; Regression analysis; Vulnerability (computing); Pipeline transport; Engineering; Reliability engineering; Computer science; Civil engineering; Environmental engineering","score_opus":0.013950062585288373,"score_gpt":0.22974754094206898,"score_spread":0.2157974783567806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098888815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3181484,0.00013361388,0.67814404,0.000121868514,0.000013634621,0.000042122854,0.00036151038,0.0015768205,0.0014579312],"genre_scores_gemma":[0.96958804,0.00006421787,0.028953463,0.000006564737,0.000005087365,0.000032171323,0.00020608024,0.000041493262,0.0011028451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995074,0.00013454804,0.000021166743,0.00016776216,0.0001195356,0.000049535854],"domain_scores_gemma":[0.99902356,0.00054081244,0.00015543205,0.00006977107,0.0001821877,0.00002826358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056727056,0.0005936986,0.00065838645,0.0006446734,0.00016881911,0.0005671378,0.0005988428,0.0005200228,0.0013314576],"category_scores_gemma":[0.0034240412,0.0003205152,0.00049709337,0.0006590228,0.0002009712,0.00071650854,0.00029636963,0.0006514706,0.0003941446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040041195,0.000035973775,0.004729564,0.000015711883,0.000024047376,0.000026886004,0.0000126392,0.9712459,0.001599041,0.00027335176,0.00022646494,0.02177031],"study_design_scores_gemma":[0.0000011345928,0.000010605054,0.00066604285,7.0345754e-7,0.0000017828112,0.0000031762231,0.0000016621464,0.99879813,0.00039303102,0.00007877029,0.00004259408,0.000002403702],"about_ca_topic_score_codex":0.011207489,"about_ca_topic_score_gemma":0.008276348,"teacher_disagreement_score":0.011207489,"about_ca_system_score_codex":0.00062427536,"about_ca_system_score_gemma":0.00046796887,"threshold_uncertainty_score":0.022284508},"labels":[],"label_agreement":null},{"id":"W2099469522","doi":"10.1061/(asce)0733-9429(2009)135:3(234)","title":"Leaks and Water Use Representation in Water Distribution System Models: Finding a Working Equivalence","year":2009,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leak; Equivalence (formal languages); Leakage (economics); Representation (politics); Pipe network analysis; Environmental science; Computer science; Demand management; Mathematical optimization; Petroleum engineering; Environmental engineering; Engineering; Mathematics; Economics; Mechanics","score_opus":0.023262721707026875,"score_gpt":0.2065778779311538,"score_spread":0.18331515622412695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099469522","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":[0.077918366,0.0005219184,0.90527594,0.0026890538,0.00005327478,0.000047596026,0.0002619492,0.00020682048,0.013025055],"genre_scores_gemma":[0.9668804,0.0005203377,0.02859659,0.00012724512,0.000082252125,0.00008163251,0.000097890275,0.00008417209,0.003529413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984635,0.00089200237,0.00007927065,0.00017921011,0.00021761261,0.00016850395],"domain_scores_gemma":[0.9964569,0.0024682872,0.00044659412,0.00032153135,0.00019629342,0.00011033105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028028924,0.0008197133,0.0012355162,0.0009478091,0.00054953183,0.0031307822,0.0015747577,0.001790133,0.0029980538],"category_scores_gemma":[0.01068709,0.0006472761,0.0013237952,0.0013190057,0.0029543417,0.008276055,0.0037291008,0.0025226597,0.00020203387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046262383,0.000036101377,0.0006558706,0.00004900898,0.000023431514,0.000049977552,0.0001875747,0.66086376,0.0002225494,0.3286775,0.00044928075,0.008738617],"study_design_scores_gemma":[0.000008568877,0.000026343203,0.00012033253,0.000015418582,0.00000924169,0.00001226686,0.00005085772,0.8060691,0.00008531837,0.19297312,0.0006166436,0.00001271483],"about_ca_topic_score_codex":0.0055256262,"about_ca_topic_score_gemma":0.0025584225,"teacher_disagreement_score":0.0055256262,"about_ca_system_score_codex":0.0018435234,"about_ca_system_score_gemma":0.0012603117,"threshold_uncertainty_score":0.0148233175},"labels":[],"label_agreement":null},{"id":"W2102244054","doi":"10.1139/cjce-2014-0114","title":"Forecasting breaks in cast iron water mains in the city of Kingston with an artificial neural network model","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Confusion matrix; Mains electricity; Confusion; Environmental science; Training (meteorology); Meteorology; Operations research; Engineering; Computer science; Artificial intelligence; Geography","score_opus":0.014286602609035122,"score_gpt":0.16480502914576647,"score_spread":0.15051842653673136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102244054","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":[0.99461395,0.00005124146,0.002608168,0.00012619645,0.00000954876,0.000021204942,0.0010462967,0.000045490466,0.0014777785],"genre_scores_gemma":[0.99664575,0.000040003437,0.0012145275,0.000006938434,0.0000015998613,0.000008067937,0.0008978771,0.00000459592,0.0011805703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999864,0.00001594384,0.000008736212,0.000039185597,0.000031155167,0.000041058047],"domain_scores_gemma":[0.999537,0.000102552025,0.00006045384,0.000021905833,0.00022088396,0.000057198187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020906539,0.0003753114,0.00022694294,0.0005101402,0.0004425101,0.0007173414,0.00071064226,0.0004784628,0.0008855737],"category_scores_gemma":[0.00112572,0.00028098142,0.00039382497,0.00069765135,0.00026581888,0.00034875184,0.00037600487,0.0004027831,0.00016280702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018370501,0.00006377287,0.15915151,0.00004824593,0.00006551241,0.00033417702,0.00020648801,0.8233313,0.0012337319,0.00043525855,0.0015608212,0.013385482],"study_design_scores_gemma":[0.0000071398526,0.00001453053,0.057391517,0.000008249911,0.000009532427,0.000010988343,0.00031655948,0.94125587,0.0003438199,0.00013837304,0.00049055304,0.000012866661],"about_ca_topic_score_codex":0.86711085,"about_ca_topic_score_gemma":0.90038615,"teacher_disagreement_score":0.86711085,"about_ca_system_score_codex":0.0063996837,"about_ca_system_score_gemma":0.0031320134,"threshold_uncertainty_score":0.26734346},"labels":[],"label_agreement":null},{"id":"W2102953731","doi":"10.1139/s06-007","title":"Case study comparisons of computational fluid dynamics (CFD) modeling versus tracer testing for determining clearwell residence times in drinking water treatment","year":2006,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computational fluid dynamics; TRACER; Baffle; Residence time distribution; Fluent; Residence time (fluid dynamics); Environmental science; Fluid dynamics; Volumetric flow rate; Mechanics; Flow (mathematics); Engineering; Mechanical engineering; Geotechnical engineering","score_opus":0.020268386497971885,"score_gpt":0.2168782706894457,"score_spread":0.19660988419147382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102953731","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":[0.977802,0.00019550862,0.020811439,0.000045796187,0.0000103853,0.000074392774,0.00015277308,0.00007989884,0.0008277783],"genre_scores_gemma":[0.98698694,0.00010498316,0.012409617,0.0000059390854,0.0000018308148,0.00004254619,0.000090539645,0.000011740116,0.00034595895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988385,0.00037876263,0.000070123206,0.0001893341,0.00041239214,0.00011094134],"domain_scores_gemma":[0.9956553,0.0031214785,0.00023963969,0.00025922622,0.0006243364,0.0001000126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018927708,0.0008132128,0.0006625357,0.0007582826,0.00076351344,0.0009982426,0.0011220436,0.0014806921,0.00062638876],"category_scores_gemma":[0.0056740255,0.00047051575,0.0006384425,0.00084458484,0.0006073806,0.00077061774,0.00060468086,0.00030476492,0.00010937094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003549467,0.0010323157,0.049902413,0.00045040483,0.00009716588,0.0012755567,0.0006367428,0.82716304,0.05084338,0.0020779332,0.0006416582,0.06232994],"study_design_scores_gemma":[0.00015256251,0.0019475648,0.014136881,0.00003151511,0.00008694849,0.00044019165,0.00052615674,0.8670125,0.11350898,0.0004748813,0.0015472292,0.00013461683],"about_ca_topic_score_codex":0.06250113,"about_ca_topic_score_gemma":0.066898316,"teacher_disagreement_score":0.06250113,"about_ca_system_score_codex":0.0023632217,"about_ca_system_score_gemma":0.0016003676,"threshold_uncertainty_score":0.12427467},"labels":[],"label_agreement":null},{"id":"W2104466018","doi":"10.7202/045825ar","title":"Analyse en ondelettes de fluctuations de débit en réseau de distribution d’eau potable","year":2011,"lang":"fr","type":"article","venue":"Revue des sciences de l eau","topic":"Water Systems and 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":true,"ca_institutions":"Université Laval","funders":"","keywords":"Humanities; Physics; Art","score_opus":0.04893048282382396,"score_gpt":0.26568488529396234,"score_spread":0.21675440247013839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104466018","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":[0.99247783,0.00014675032,0.0038168847,0.000027031609,0.0000061050346,0.000019958945,0.00096557883,0.00005180967,0.0024881712],"genre_scores_gemma":[0.99109733,0.00012254556,0.0024929834,0.000013509283,0.000004508442,0.000026575937,0.001190415,0.00002825769,0.0050238697],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976546,0.000021709542,0.000007236821,0.00006557446,0.00009616665,0.000043835913],"domain_scores_gemma":[0.9994011,0.0001978095,0.000074572505,0.00003867797,0.00025101154,0.000036799473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042031394,0.00031793345,0.0004641584,0.0010257986,0.0005527307,0.0009753686,0.00042070894,0.00026469977,0.0019854957],"category_scores_gemma":[0.0009538943,0.00017103694,0.00044254615,0.0013969376,0.00038001387,0.00026182737,0.00024595365,0.0002942454,0.000332072],"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":[0.00048481167,0.00008836493,0.80798376,0.00024004576,0.00030025386,0.00043006797,0.0029713726,0.025028925,0.08653489,0.0010162244,0.0011498256,0.07377144],"study_design_scores_gemma":[0.0000035253568,0.00003097497,0.98361695,0.000013896316,0.00001975598,0.000033856224,0.0005031843,0.009826286,0.0036427714,0.000079140664,0.0022104664,0.00001918308],"about_ca_topic_score_codex":0.48673528,"about_ca_topic_score_gemma":0.6025829,"teacher_disagreement_score":0.48673528,"about_ca_system_score_codex":0.0021574867,"about_ca_system_score_gemma":0.00094733917,"threshold_uncertainty_score":0.9678043},"labels":[],"label_agreement":null},{"id":"W2105184576","doi":"10.1139/l08-056","title":"Rebirthing particle swarm optimization algorithm: application to storm water network design","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":24,"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":"Particle swarm optimization; Benchmark (surveying); Local optimum; Algorithm; Mathematical optimization; Computer science; Local search (optimization); Imperialist competitive algorithm; Multi-swarm optimization; Mathematics","score_opus":0.008534448051647402,"score_gpt":0.1570507847321195,"score_spread":0.1485163366804721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105184576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021945316,0.00044008487,0.97453904,0.00016728016,0.000050254694,0.00004983789,0.000016136706,0.00041739538,0.0023745736],"genre_scores_gemma":[0.43659613,0.0007056551,0.5568706,0.00007355823,0.000060496044,0.00014327287,0.000083974264,0.00010760279,0.0053586946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997917,0.00007123524,0.000011912826,0.000030677715,0.00007766368,0.00001680662],"domain_scores_gemma":[0.9996264,0.00018240527,0.000045776083,0.000031854106,0.00009823636,0.000015352765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007119977,0.00065077835,0.0008851679,0.00035536475,0.00034099954,0.0005186316,0.00065416493,0.0009430611,0.0011472699],"category_scores_gemma":[0.001328467,0.00024652696,0.00036838837,0.00042332185,0.0003589234,0.0004523046,0.00051545014,0.00061019644,0.00019536038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041922714,0.000029561874,0.00053207646,0.000056829726,0.000024577144,0.000088120134,0.000053160245,0.91200745,0.0026197638,0.0033691376,0.00057859666,0.080598734],"study_design_scores_gemma":[0.00000976593,0.000027629618,0.00007957136,0.0000029666755,0.0000054191096,0.00002131493,0.0000054357038,0.99726945,0.0007249807,0.0007266932,0.0011230012,0.000003821808],"about_ca_topic_score_codex":0.0040532886,"about_ca_topic_score_gemma":0.0027262475,"teacher_disagreement_score":0.0040532886,"about_ca_system_score_codex":0.0003188751,"about_ca_system_score_gemma":0.0005486782,"threshold_uncertainty_score":0.008059382},"labels":[],"label_agreement":null},{"id":"W2105882449","doi":"10.1061/40854(211)27","title":"Assessment Model of Water Main Conditions","year":2006,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":33,"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":"Analytic hierarchy process; Mains electricity; Environmental science; Engineering; Civil engineering; Computer science; Reliability engineering; Environmental engineering; Operations research","score_opus":0.006775685484707831,"score_gpt":0.19450001069902653,"score_spread":0.1877243252143187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105882449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.115982436,0.00046753517,0.82838625,0.001051591,0.000106519044,0.00048466487,0.0032975127,0.0014628097,0.04876071],"genre_scores_gemma":[0.9369311,0.00043449565,0.036859646,0.00007424552,0.000031887594,0.00068874675,0.001449839,0.00006437399,0.02346566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885345,0.00025182994,0.000068824345,0.00039523726,0.00027558926,0.00015511937],"domain_scores_gemma":[0.9988098,0.000458849,0.00015370602,0.000034212168,0.0004906468,0.000052685195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012735807,0.0011018772,0.0006467205,0.0018496369,0.00065013894,0.00230975,0.0019188146,0.0017746886,0.012798209],"category_scores_gemma":[0.0028761222,0.0004972832,0.0011738323,0.0016136712,0.00068447925,0.002262766,0.0013938315,0.0010834705,0.0013616405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038324415,0.00003447844,0.0023760183,0.00008060926,0.00002585682,0.00017168539,0.00012408292,0.97709286,0.0006874794,0.008491428,0.00073805096,0.01013906],"study_design_scores_gemma":[0.0000060773095,0.000022283533,0.00039810236,0.000008183297,0.000010500068,0.00002164681,0.000049526378,0.9950814,0.00014853939,0.0034548056,0.00079089246,0.00000798768],"about_ca_topic_score_codex":0.030547256,"about_ca_topic_score_gemma":0.014151422,"teacher_disagreement_score":0.030547256,"about_ca_system_score_codex":0.0023589602,"about_ca_system_score_gemma":0.0020605961,"threshold_uncertainty_score":0.06073892},"labels":[],"label_agreement":null},{"id":"W2109481002","doi":"10.1061/40941(247)9","title":"Fire Flow Analysis for Optimal Network Improvement","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Native Mental Health Association of Canada","funders":"","keywords":"Node (physics); Flow (mathematics); Flow network; Maximum flow problem; Engineering; Ranging; Pipe network analysis; Fire protection; Population; Environmental science; Network model; Water flow; Marine engineering; Civil engineering; Computer science; Environmental engineering; Structural engineering; Telecommunications","score_opus":0.009746839312299712,"score_gpt":0.17742524363929302,"score_spread":0.1676784043269933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109481002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09502705,0.00026366406,0.8818411,0.00024274517,0.00004066184,0.00019833061,0.0003785612,0.0004112454,0.02159665],"genre_scores_gemma":[0.875137,0.00040812668,0.116933435,0.0000531472,0.000020081343,0.00016498932,0.00031903211,0.00011555199,0.006848665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999721,0.000076699514,0.000008430934,0.000041317213,0.00009280659,0.000059616912],"domain_scores_gemma":[0.99935526,0.00035181257,0.00007873542,0.00003146506,0.0001632119,0.000019506717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091562606,0.0006333783,0.0005676064,0.0010055351,0.0005127129,0.0008229994,0.0006493429,0.00054870854,0.0041569406],"category_scores_gemma":[0.0023889337,0.0003419593,0.0005897223,0.0006027633,0.00057377986,0.0010693535,0.00042736126,0.00058218517,0.00018497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010473219,0.000008001017,0.0002301304,0.000008740226,0.000002393313,0.0000100255465,0.0000075560974,0.99221706,0.00023825414,0.0023841872,0.0001248382,0.004758326],"study_design_scores_gemma":[0.0000025602465,0.000006935685,0.00010202865,0.0000027990784,0.0000020657817,0.0000023244356,0.000007110718,0.9982023,0.0002768052,0.0010429407,0.00035031102,0.0000017862886],"about_ca_topic_score_codex":0.058414686,"about_ca_topic_score_gemma":0.03957426,"teacher_disagreement_score":0.058414686,"about_ca_system_score_codex":0.0028267133,"about_ca_system_score_gemma":0.0021732685,"threshold_uncertainty_score":0.116149366},"labels":[],"label_agreement":null},{"id":"W2109726136","doi":"10.14796/jwmm.r241-02","title":"Illinois Transient Model: Simulating the Flow Dynamics in Combined Storm Sewer Systems","year":2011,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Illinois at Urbana-Champaign","keywords":"Water hammer; Mechanics; Open-channel flow; Flow (mathematics); Free surface; Potential flow; Transient (computer programming); Bernoulli's principle; Geology; Geotechnical engineering; Engineering; Physics; Computer science","score_opus":0.020129541698439432,"score_gpt":0.18463588075807852,"score_spread":0.16450633905963907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109726136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3661556,0.00047417847,0.5103971,0.0008021563,0.00018812598,0.00039556573,0.0067718355,0.014095682,0.10071982],"genre_scores_gemma":[0.9180855,0.0002712961,0.052894253,0.00011369236,0.000037484715,0.00053290476,0.0044710487,0.0005404508,0.023053255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979836,0.000040189763,0.000009948252,0.000033564436,0.00007891296,0.000039125298],"domain_scores_gemma":[0.99958724,0.00017770621,0.000033773355,0.00003083442,0.00012563351,0.000044911332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025545782,0.0005994079,0.00063315313,0.0005998185,0.00054808,0.00092441385,0.0013482659,0.0010631578,0.008474451],"category_scores_gemma":[0.0009861961,0.00038201248,0.0005647608,0.0007481859,0.00054729515,0.0009882,0.0008447651,0.00084046443,0.0010368505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053406526,0.00003362309,0.0017203329,0.000030126323,0.000011557024,0.00006314137,0.000047544847,0.9862672,0.0013773075,0.0030764127,0.0029636133,0.004355791],"study_design_scores_gemma":[0.000012142824,0.000010292987,0.00016389716,0.000003917171,0.0000035278772,0.0000071639724,0.000009773758,0.9976388,0.00047435513,0.00032703887,0.0013442432,0.000004762005],"about_ca_topic_score_codex":0.026247317,"about_ca_topic_score_gemma":0.021652889,"teacher_disagreement_score":0.026247317,"about_ca_system_score_codex":0.001057697,"about_ca_system_score_gemma":0.0014913334,"threshold_uncertainty_score":0.05218911},"labels":[],"label_agreement":null},{"id":"W2110317858","doi":"10.1139/l06-071","title":"Oblique weir equation using incomplete self-similarity","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Oblique case; Weir; Similarity (geometry); Experimental data; Flow (mathematics); Variable (mathematics); Computer science; Mathematics; Algorithm; Applied mathematics; Mathematical analysis; Statistics; Geometry; Image (mathematics); Artificial intelligence","score_opus":0.010670538453035427,"score_gpt":0.16315422135797134,"score_spread":0.1524836829049359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110317858","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":[0.044558734,0.00033786343,0.94025254,0.00030977605,0.000113380716,0.00016269485,0.00045255572,0.0001536722,0.01365871],"genre_scores_gemma":[0.74160516,0.0015225798,0.23110233,0.00023406347,0.00019172036,0.0006149758,0.0009060812,0.00014696641,0.02367609],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926454,0.00013452228,0.000078137804,0.000104899125,0.0003562798,0.00006155091],"domain_scores_gemma":[0.9989801,0.00034419855,0.00018268361,0.00010541011,0.00035296264,0.00003462604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013825236,0.0004641232,0.0007519005,0.0014560963,0.00038682105,0.0009983168,0.0009036698,0.0008171273,0.003180138],"category_scores_gemma":[0.0032113453,0.00032651733,0.0009922136,0.0010926422,0.0008001099,0.002194218,0.0009560639,0.001103683,0.0005800616],"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":[0.000036946043,0.000057224133,0.004760118,0.00019760815,0.00004060268,0.000322538,0.0004051834,0.40767756,0.009885122,0.53743064,0.0024281116,0.036758423],"study_design_scores_gemma":[0.000008350759,0.000029521236,0.0010270983,0.000013485499,0.00001033836,0.00015023923,0.00004489828,0.9526121,0.0011649731,0.0423119,0.0025951886,0.000031990432],"about_ca_topic_score_codex":0.003478968,"about_ca_topic_score_gemma":0.00222845,"teacher_disagreement_score":0.003478968,"about_ca_system_score_codex":0.00089661335,"about_ca_system_score_gemma":0.0013863662,"threshold_uncertainty_score":0.010638654},"labels":[],"label_agreement":null},{"id":"W2110892531","doi":"10.1680/wama.2007.160.2.83","title":"Estimating time to failure of cast-iron water mains","year":2007,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Water Management","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Mains electricity; Corrosion; Environmental science; Bedding; Geotechnical engineering; Pipe network analysis; Engineering; Reliability engineering; Materials science","score_opus":0.003637109604092326,"score_gpt":0.16769040392988654,"score_spread":0.1640532943257942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110892531","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":[0.97378165,0.00013310395,0.024989096,0.000038480594,0.000006256047,0.000030166142,0.0002752977,0.00011367734,0.00063228206],"genre_scores_gemma":[0.99736685,0.000034020206,0.0021041885,0.0000015026933,0.0000017217623,0.000006450574,0.00023622903,0.0000046799587,0.00024448292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996493,0.00008495903,0.000024403158,0.000074871685,0.000094113646,0.00007225847],"domain_scores_gemma":[0.9954678,0.002930099,0.00077686185,0.00018194049,0.0004260088,0.0002173335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012119984,0.0006535078,0.00040354696,0.0017068798,0.00024201536,0.00043520928,0.00050490303,0.00084268046,0.0008230022],"category_scores_gemma":[0.007710193,0.00032237935,0.00046558597,0.00039286207,0.00033407978,0.00066621706,0.00039531165,0.0003676078,0.00014173791],"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":[0.0005004631,0.0000741622,0.03433761,0.00006385808,0.000058760696,0.00022062776,0.00007097132,0.94426113,0.005075172,0.00065088546,0.0002062609,0.014480139],"study_design_scores_gemma":[0.0000045314573,0.00014048757,0.013483617,0.0000060820553,0.0000125142005,0.00004109883,0.000033016655,0.98264325,0.003062381,0.00047018414,0.00008857702,0.000014249422],"about_ca_topic_score_codex":0.0146084195,"about_ca_topic_score_gemma":0.0101221,"teacher_disagreement_score":0.0146084195,"about_ca_system_score_codex":0.00093200663,"about_ca_system_score_gemma":0.00045181124,"threshold_uncertainty_score":0.029046774},"labels":[],"label_agreement":null},{"id":"W2112469209","doi":"","title":"Thomas Young's research on fluid transients : 200 years on","year":2008,"lang":"en","type":"article","venue":"TU/e Research Portal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":10,"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":"Queen's University; Université de Lille","keywords":"Terminology; German; Mechanics; Classics; Calculus (dental); Engineering; History; Philosophy; Physics","score_opus":0.12645917092815578,"score_gpt":0.3564925623454628,"score_spread":0.230033391417307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112469209","genre_codex":"review","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":[0.008010263,0.6532854,0.02197737,0.07417492,0.016995993,0.000047933863,0.00040396806,0.0002051835,0.22489886],"genre_scores_gemma":[0.1460339,0.5951496,0.01199903,0.025559813,0.027535854,0.00013210466,0.0004976766,0.0005618184,0.19253021],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989002,0.00030027347,0.000060571787,0.00020411848,0.00039112318,0.00014361365],"domain_scores_gemma":[0.99867886,0.0007099629,0.00007284504,0.00009409297,0.00027431926,0.00016993379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017019623,0.0008435425,0.0008852834,0.0022710909,0.0017913423,0.0032411704,0.0008071324,0.0027376541,0.010190771],"category_scores_gemma":[0.0046364116,0.0005110983,0.00052702235,0.0027935305,0.0061504496,0.0052176206,0.002791204,0.004090518,0.003386592],"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":[0.00011471444,0.000053599757,0.00046813645,0.0006152024,0.000022404707,0.00015715144,0.0017573368,0.00082153146,0.00052121945,0.6972637,0.13554469,0.16266035],"study_design_scores_gemma":[0.000006785135,0.00002905098,0.00024841737,0.0005713156,0.000006461517,0.00009729871,0.00018743375,0.00028328627,0.00039339557,0.067085356,0.93107337,0.000017756392],"about_ca_topic_score_codex":0.0042571207,"about_ca_topic_score_gemma":0.0030439964,"teacher_disagreement_score":0.010190771,"about_ca_system_score_codex":0.004573296,"about_ca_system_score_gemma":0.0024395995,"threshold_uncertainty_score":0.03409159},"labels":[],"label_agreement":null},{"id":"W2112752718","doi":"10.1111/j.1467-8667.2010.00673.x","title":"A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks","year":2010,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Water Systems and 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":"École de Technologie Supérieure","funders":"","keywords":"Fuzzy logic; Ranking (information retrieval); Fuzzy inference system; Adaptive neuro fuzzy inference system; Sanitary sewer; Computer science; Index (typography); Expert system; Reliability engineering; Engineering; Fuzzy control system; Civil engineering; Data mining; Artificial intelligence; Environmental engineering","score_opus":0.002102090380668168,"score_gpt":0.16359082881307643,"score_spread":0.16148873843240827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112752718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061532196,0.00021139279,0.92991966,0.00015386989,0.00005638504,0.00026061066,0.00035388535,0.0031859737,0.0043260227],"genre_scores_gemma":[0.6104286,0.00015630574,0.38450205,0.00013811757,0.000028538347,0.00030522599,0.00038679256,0.000055795605,0.0039986516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967027,0.00006732391,0.00004019776,0.000085634434,0.00009353804,0.00004306635],"domain_scores_gemma":[0.9992799,0.00032023826,0.000043127402,0.000041916555,0.00027080803,0.000043997185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009223774,0.00068342057,0.0010787304,0.00087542477,0.0006395287,0.0008652968,0.0010535195,0.0014230093,0.0050781723],"category_scores_gemma":[0.0021354782,0.0003728644,0.00047182766,0.00045023422,0.00022551586,0.0006543479,0.00053493906,0.0005415675,0.0007390882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057906134,0.00025627742,0.0014886985,0.00028486433,0.00009932672,0.00029958453,0.00016575282,0.5238316,0.02017955,0.0017279518,0.0045408327,0.44654647],"study_design_scores_gemma":[0.00006067508,0.0000668873,0.00040757086,0.000021299058,0.000037891106,0.000037077483,0.00002358243,0.9945615,0.0028379778,0.0008350191,0.0010969819,0.000013533973],"about_ca_topic_score_codex":0.014898404,"about_ca_topic_score_gemma":0.017117795,"teacher_disagreement_score":0.014898404,"about_ca_system_score_codex":0.00079481065,"about_ca_system_score_gemma":0.0014340333,"threshold_uncertainty_score":0.02962333},"labels":[],"label_agreement":null},{"id":"W2113656025","doi":"10.1115/fedsm2003-45259","title":"Pipe Systems With Micro-Turbines: Water Hammer Considerations","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Water hammer; Penstock; Turbine; Hydraulic machinery; Hydropower; Marine engineering; Torque; Control theory (sociology); Engineering; Flow (mathematics); Transient (computer programming); Mechanical engineering; Computer science; Mechanics; Structural engineering; Electrical engineering; Control (management)","score_opus":0.007546808592381833,"score_gpt":0.1585956066299788,"score_spread":0.15104879803759697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113656025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12948622,0.0028878474,0.7866258,0.002416796,0.00019507325,0.00006258209,0.00021868286,0.00031213672,0.07779489],"genre_scores_gemma":[0.9626533,0.0019506391,0.019789625,0.00011069243,0.00012881255,0.000049698854,0.00006472631,0.00006790845,0.015184647],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990594,0.000023768582,0.0000032188727,0.00001722259,0.00003568386,0.000014178271],"domain_scores_gemma":[0.9998447,0.00006794666,0.000028104287,0.000017162742,0.000023921733,0.000018155564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014472376,0.00028377378,0.0003020583,0.00026712375,0.00041936856,0.000764528,0.00046801945,0.00062326295,0.006074749],"category_scores_gemma":[0.0007539784,0.00025247375,0.00024101461,0.00020387319,0.0009605276,0.0025456669,0.0006071525,0.00045730133,0.0005024586],"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":[0.000028187293,0.00002592096,0.000967104,0.0001773643,0.000008012497,0.00039561157,0.00015192665,0.4194419,0.0070282035,0.55105853,0.0022791757,0.018438099],"study_design_scores_gemma":[0.000011066709,0.000078013014,0.0010490208,0.000023862116,0.000009503958,0.00021653096,0.00010990997,0.74950814,0.0014978714,0.23505862,0.012423612,0.000013799682],"about_ca_topic_score_codex":0.0016599202,"about_ca_topic_score_gemma":0.0020532168,"teacher_disagreement_score":0.006074749,"about_ca_system_score_codex":0.0004073923,"about_ca_system_score_gemma":0.00036535153,"threshold_uncertainty_score":0.020322084},"labels":[],"label_agreement":null},{"id":"W2114675936","doi":"10.1139/l00-073","title":"A methodology to estimate remaining service life of grey cast iron water mains","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":147,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; American Water Works Association Research Foundation; Water Research Foundation","keywords":"Mains electricity; Service life; Corrosion; Engineering; Reliability (semiconductor); Cast iron; Reliability engineering; Forensic engineering; Materials science","score_opus":0.019755105942456854,"score_gpt":0.21759571367090302,"score_spread":0.19784060772844617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114675936","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":[0.035176564,0.00040982163,0.9608399,0.000036656365,0.000028029765,0.00020672812,0.00051483756,0.0010204586,0.0017670104],"genre_scores_gemma":[0.36492917,0.00041602063,0.6303056,0.0000246195,0.000019717945,0.00041272255,0.001097993,0.00009366568,0.0027004778],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991899,0.00009087979,0.000052484047,0.00021293858,0.00040264582,0.00005115953],"domain_scores_gemma":[0.9983714,0.00036005277,0.00035288994,0.00011530966,0.00075693784,0.00004344731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009559618,0.0009596546,0.0004438499,0.0034176172,0.00040403407,0.00075657194,0.0009619902,0.0007018078,0.0011356777],"category_scores_gemma":[0.0028202864,0.00038896318,0.00073967234,0.0010512703,0.0002673775,0.00076513074,0.00039209428,0.00047867882,0.00059967785],"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":[0.00014602924,0.00012134037,0.033958226,0.000574161,0.0001398413,0.0003191693,0.00036214385,0.3872747,0.09201639,0.005696692,0.0019055406,0.47748575],"study_design_scores_gemma":[0.000022124477,0.00047204757,0.022475246,0.000095927615,0.00005904487,0.0004419961,0.00013736307,0.90131664,0.0616848,0.0028346053,0.010350337,0.00010981041],"about_ca_topic_score_codex":0.008306346,"about_ca_topic_score_gemma":0.00580603,"teacher_disagreement_score":0.008306346,"about_ca_system_score_codex":0.0010041973,"about_ca_system_score_gemma":0.0011769583,"threshold_uncertainty_score":0.01651603},"labels":[],"label_agreement":null},{"id":"W2115175365","doi":"10.1061/(asce)wr.1943-5452.0000109","title":"Transient Modeling of a Full-Scale Distribution System: Comparison with Field Data","year":2010,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":47,"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; Université Laval; University of Toronto; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Water Network; Polytechnique Montréal","keywords":"Transient (computer programming); Field (mathematics); Surge; Environmental science; Transient analysis; Scale (ratio); Mechanics; Transient response; Meteorology; Engineering; Computer science; Mathematics; Physics; Electrical engineering","score_opus":0.014053580359892107,"score_gpt":0.21194850842109975,"score_spread":0.19789492806120765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115175365","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":[0.9357697,0.00009730185,0.057064865,0.00015150108,0.000015743322,0.000088004876,0.0012981828,0.0020421324,0.0034725904],"genre_scores_gemma":[0.99442023,0.00004818332,0.0047742315,0.000008883911,0.0000029757823,0.00005006958,0.00041760335,0.000033216016,0.00024449916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979526,0.0000555349,0.000023115093,0.00005470729,0.000047256442,0.000024201549],"domain_scores_gemma":[0.99858737,0.00084719615,0.00009631476,0.00021017747,0.0002150866,0.000043850116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006087825,0.00052090985,0.00061053,0.0005845134,0.00030800753,0.00050953275,0.00097090943,0.0007549835,0.001602275],"category_scores_gemma":[0.0018085961,0.00027405174,0.00040021847,0.0006419579,0.00032792924,0.0011305958,0.00026428624,0.00060512824,0.00023660072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099989484,0.00013108681,0.0047673592,0.00006204878,0.000015538386,0.00006106723,0.000054984488,0.9838816,0.0025551296,0.00028589484,0.0003574909,0.0077277785],"study_design_scores_gemma":[0.000014519085,0.00005024128,0.0017367718,0.000002752327,0.000003946134,0.000011975373,0.000019177605,0.996962,0.0009448326,0.00011835342,0.0001305599,0.0000048161096],"about_ca_topic_score_codex":0.016982602,"about_ca_topic_score_gemma":0.008266468,"teacher_disagreement_score":0.016982602,"about_ca_system_score_codex":0.0010180882,"about_ca_system_score_gemma":0.00062294165,"threshold_uncertainty_score":0.03376752},"labels":[],"label_agreement":null},{"id":"W2116890893","doi":"10.1061/40685(2003)108","title":"Hydroelectric Potential of the City of Nanaimo BC Water Supply","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Hydroelectricity; Pipeline transport; Water supply; Environmental science; Water resource management; Energy consumption; Population; Hydrology (agriculture); Consumption (sociology); Water flow; Environmental engineering; Engineering; Electrical engineering","score_opus":0.003540682244361639,"score_gpt":0.14912191202496475,"score_spread":0.14558122978060312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116890893","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":[0.8754938,0.00072498753,0.000965882,0.00084536464,0.000025937383,0.00004110798,0.0034591246,0.000025697353,0.11841795],"genre_scores_gemma":[0.96497834,0.00059853145,0.0012111961,0.000046682166,0.000010819282,0.000030126132,0.0012930506,0.000012126372,0.031819094],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988985,0.000012673531,0.0000018790979,0.000011725975,0.000057579222,0.000026232183],"domain_scores_gemma":[0.99990034,0.000011199904,0.000007061178,0.0000026814787,0.000060723807,0.000018111059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008007609,0.00017165582,0.000121416895,0.00068869995,0.0008020083,0.0009975296,0.00024724207,0.00014652668,0.0050536264],"category_scores_gemma":[0.0004148186,0.00011285246,0.00011011581,0.0013336145,0.00014397458,0.00022047956,0.00032667487,0.00019661055,0.00039937563],"study_design_candidate":"simulation_or_modeling","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":[0.0003768572,0.00016345993,0.46711385,0.00043234057,0.00024089335,0.0025091826,0.0015203947,0.22554767,0.008513063,0.027852142,0.044793833,0.22093627],"study_design_scores_gemma":[0.00005286737,0.000084319545,0.5677752,0.00015094661,0.00010038427,0.0004363938,0.0041069505,0.26600337,0.0025308896,0.005968632,0.15270557,0.00008453928],"about_ca_topic_score_codex":0.6529127,"about_ca_topic_score_gemma":0.93077356,"teacher_disagreement_score":0.34708732,"about_ca_system_score_codex":0.005941404,"about_ca_system_score_gemma":0.0030273513,"threshold_uncertainty_score":0.6982628},"labels":[],"label_agreement":null},{"id":"W2117875131","doi":"10.3808/jei.200800107","title":"Modified Penalty Function and Parameterization for Solving Least-Cost Treatment of Wastewater","year":2008,"lang":"en","type":"article","venue":"Journal of Environmental Informatics","topic":"Water Systems and 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":"Penalty method; Wastewater; Function (biology); Mathematical optimization; Mathematics; Applied mathematics; Environmental science; Computer science; Waste management; Engineering; Environmental engineering","score_opus":0.0188389059490291,"score_gpt":0.18410216491805392,"score_spread":0.16526325896902483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117875131","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":[0.0014941879,0.00010086485,0.997863,0.00004706482,0.000019326051,0.000014867324,0.000005643393,0.000060849965,0.00039424183],"genre_scores_gemma":[0.14028311,0.00053936563,0.8549389,0.0001388481,0.00007991249,0.000434794,0.00011989378,0.00030282402,0.0031623614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901986,0.000432035,0.000049733826,0.00011027493,0.0003162099,0.000071815324],"domain_scores_gemma":[0.999522,0.00021906894,0.000051291405,0.000051689993,0.00013373447,0.000022337894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013444166,0.0014761868,0.0010968262,0.000790584,0.00046922424,0.00067419437,0.0014430137,0.0016665055,0.0016117977],"category_scores_gemma":[0.00260051,0.00043937966,0.001069192,0.00089634943,0.0008135173,0.0014468982,0.00092539936,0.0019965237,0.000483589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008292211,0.0000830645,0.00027700397,0.00022315278,0.000054645232,0.00012252487,0.00007590111,0.838492,0.023512026,0.04976015,0.001651208,0.08566541],"study_design_scores_gemma":[0.000006722126,0.000028840965,0.000053842872,0.0000076222304,0.000005397518,0.000026941481,0.0000055948735,0.98931026,0.002452126,0.0061165513,0.0019696434,0.000016461147],"about_ca_topic_score_codex":0.0019969416,"about_ca_topic_score_gemma":0.0012196294,"teacher_disagreement_score":0.0019969416,"about_ca_system_score_codex":0.00053461234,"about_ca_system_score_gemma":0.0010535507,"threshold_uncertainty_score":0.0071099997},"labels":[],"label_agreement":null},{"id":"W2118875090","doi":"10.1061/9780784479360.117","title":"Better Data Equals Better Decisions: New Developments in Multi-Sensor Condition Assessment Technologies","year":2015,"lang":"en","type":"article","venue":"Pipelines 2015","topic":"Water Systems and 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":"Dawson College","funders":"","keywords":"Marine engineering; Environmental science; Software deployment; Lidar; Computer science; Civil engineering; Hydrology (agriculture); Geology; Engineering; Remote sensing; Geotechnical engineering","score_opus":0.16060819351684613,"score_gpt":0.3528992888524845,"score_spread":0.19229109533563837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118875090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074902927,0.027624443,0.931198,0.008792147,0.0011364562,0.00018122963,0.0004995924,0.0028000977,0.020277748],"genre_scores_gemma":[0.19338183,0.018677479,0.77307034,0.0030032315,0.0026576116,0.00029350634,0.00075094885,0.0004997058,0.007665291],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99435645,0.0016629806,0.00029281442,0.0012887213,0.0022347842,0.00016431948],"domain_scores_gemma":[0.97711927,0.014579165,0.0015030263,0.002878794,0.0032324875,0.0006872627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008710038,0.002039938,0.0021137865,0.0037616235,0.00055118423,0.006393299,0.0031923023,0.0028055485,0.006335114],"category_scores_gemma":[0.020105585,0.000960395,0.0011171433,0.0030604927,0.00256811,0.014803589,0.0033664175,0.0041028876,0.0025705344],"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":[0.0002464326,0.00029021574,0.0035639713,0.0006643998,0.00023438326,0.00014697842,0.0005831455,0.027721722,0.004884732,0.07134196,0.011079363,0.87924266],"study_design_scores_gemma":[0.00012638456,0.00054516055,0.0051971804,0.0009850762,0.00018429522,0.00049562054,0.00088642596,0.4532526,0.0076649245,0.37422022,0.15598027,0.0004619121],"about_ca_topic_score_codex":0.0019460679,"about_ca_topic_score_gemma":0.0015927401,"teacher_disagreement_score":0.008710038,"about_ca_system_score_codex":0.00179572,"about_ca_system_score_gemma":0.0010768597,"threshold_uncertainty_score":0.04606366},"labels":[],"label_agreement":null},{"id":"W2119437062","doi":"10.1016/j.jher.2009.02.003","title":"A selective literature review of transient-based leak detection methods","year":2009,"lang":"en","type":"article","venue":"Journal of Hydro-environment Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":500,"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 Water Network; National Research Council","keywords":"Computer science; Work (physics); Transient (computer programming); Field (mathematics); Perspective (graphical); Section (typography); Leak detection; Risk analysis (engineering); Leak; Data science; Engineering; Artificial intelligence; Mechanical engineering; Mathematics; Medicine","score_opus":0.02086165381233797,"score_gpt":0.3208602007297193,"score_spread":0.29999854691738137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119437062","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":[0.0068049678,0.7270407,0.2525375,0.0012147136,0.0014306214,0.00013579978,0.00053623616,0.0007538215,0.009545629],"genre_scores_gemma":[0.061358515,0.747615,0.17904112,0.0010519964,0.0024801912,0.00018635383,0.0014749318,0.00022855752,0.0065634474],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978283,0.0004562162,0.00045100605,0.00040106155,0.0007870795,0.00007630196],"domain_scores_gemma":[0.9760433,0.01569333,0.0010559768,0.0007576762,0.0062541743,0.00019548499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036561934,0.0012264912,0.0018015458,0.010830717,0.0005533105,0.0020656814,0.0021720354,0.0014084625,0.0040104114],"category_scores_gemma":[0.014648546,0.0006053656,0.0014349471,0.010457824,0.000545864,0.0033214493,0.0006656823,0.00087660836,0.0020539165],"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":[0.00010003717,0.00008018864,0.0008479085,0.012019399,0.00019566425,0.00009699024,0.00007261892,0.0021531968,0.0026416944,0.0022989293,0.0077695674,0.97172374],"study_design_scores_gemma":[0.00013951042,0.0017057707,0.016585605,0.02871702,0.0052357716,0.005876816,0.0013479474,0.13498403,0.063808,0.019719595,0.7213403,0.00053956034],"about_ca_topic_score_codex":0.0014875223,"about_ca_topic_score_gemma":0.0020136512,"teacher_disagreement_score":0.010830717,"about_ca_system_score_codex":0.00054943963,"about_ca_system_score_gemma":0.0017006132,"threshold_uncertainty_score":0.019336045},"labels":[],"label_agreement":null},{"id":"W2120355464","doi":"10.1061/(asce)0733-9496(2005)131:2(146)","title":"Impacts of Leaks on Energy Consumption in Pumped Systems with Storage","year":2005,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"University of Toronto","funders":"","keywords":"Leakage (economics); Energy consumption; Energy storage; Storage tank; Energy conservation; Reliability engineering; Environmental science; Energy (signal processing); Engineering; Power (physics); Automotive engineering; Process engineering; Waste management; Electrical engineering; Economics; Mathematics; Statistics","score_opus":0.00914487621720015,"score_gpt":0.19460504294972977,"score_spread":0.18546016673252963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120355464","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":[0.9897613,0.0001325522,0.007653608,0.000101153135,0.000006266176,0.000029410472,0.00018042646,0.00007486622,0.0020604858],"genre_scores_gemma":[0.99900407,0.00006188114,0.0005220965,0.0000060084394,8.795695e-7,0.000010095808,0.00004805787,0.000008279754,0.0003387285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949265,0.00016839056,0.00004109646,0.000050797804,0.00012638094,0.00012060274],"domain_scores_gemma":[0.99792635,0.0014247582,0.00021307268,0.00012539906,0.00025043255,0.000059965085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007080515,0.00048139013,0.0006722013,0.0006120049,0.0005673327,0.0010910694,0.0004971261,0.0005544397,0.0026319993],"category_scores_gemma":[0.0037669754,0.00040108894,0.00048748267,0.0010755002,0.0008921985,0.002551876,0.000999716,0.000451219,0.00013924942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000757023,0.00009570744,0.015116432,0.00015924528,0.000064654974,0.0005557498,0.000091178474,0.96118706,0.008646637,0.0031499113,0.00020203841,0.009974456],"study_design_scores_gemma":[0.000108087144,0.0010212664,0.024339434,0.000037524012,0.00015805507,0.00031847582,0.0005621449,0.94237906,0.023148363,0.006791226,0.0010643905,0.0000719627],"about_ca_topic_score_codex":0.0042032017,"about_ca_topic_score_gemma":0.0036891985,"teacher_disagreement_score":0.0042032017,"about_ca_system_score_codex":0.0013019472,"about_ca_system_score_gemma":0.00058422156,"threshold_uncertainty_score":0.009446323},"labels":[],"label_agreement":null},{"id":"W2121131047","doi":"10.1115/fedsm2003-45244","title":"Energy Dissipation Mechanisms in Water Distribution Systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Dissipation; Transient (computer programming); Dissipative system; Context (archaeology); Energy (signal processing); Computer science; Mechanics; Pipeline (software); Physics; Geology; Thermodynamics","score_opus":0.004838112176681869,"score_gpt":0.1596382553067752,"score_spread":0.15480014313009333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121131047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4604071,0.0039935196,0.51663333,0.00060228416,0.00006301409,0.0001030368,0.00011328386,0.00028456646,0.017799947],"genre_scores_gemma":[0.9932469,0.00060405146,0.0039311456,0.000012534631,0.000005292102,0.000021191414,0.000010020516,0.000014726845,0.0021540688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998357,0.000040148974,0.000011256552,0.000023221646,0.000054907447,0.00003471378],"domain_scores_gemma":[0.99976414,0.00011802396,0.000059904283,0.000017243683,0.000029487934,0.000011246508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032321637,0.0003476771,0.00039431147,0.00059157866,0.00048421056,0.00083065825,0.00046621833,0.0004509602,0.0012277921],"category_scores_gemma":[0.0013422684,0.0002165034,0.00024349017,0.0004067293,0.0008405561,0.0011444401,0.0006110042,0.00028078168,0.00010106592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047279285,0.000026963497,0.001676416,0.00017412288,0.00002108894,0.00028017617,0.00023843166,0.89521533,0.010938113,0.07001267,0.00026296233,0.021106508],"study_design_scores_gemma":[0.000015282081,0.00006537187,0.0012811716,0.000026658068,0.000015709176,0.00011929168,0.0001327379,0.95925736,0.0049544033,0.032477994,0.0016341761,0.000019715808],"about_ca_topic_score_codex":0.0021292008,"about_ca_topic_score_gemma":0.00097157515,"teacher_disagreement_score":0.0021292008,"about_ca_system_score_codex":0.00070699624,"about_ca_system_score_gemma":0.0003132701,"threshold_uncertainty_score":0.005129695},"labels":[],"label_agreement":null},{"id":"W2121786986","doi":"10.1680/jees.2013.0032","title":"Probabilistic design of a riverine early warning source water monitoring system","year":2013,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Warning system; Safeguarding; Water source; Early warning system; Probabilistic logic; Water resource management; Computer science; Telecommunications","score_opus":0.004764493563309369,"score_gpt":0.14285454997896155,"score_spread":0.13809005641565217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121786986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034521416,0.0001122353,0.9600985,0.00021571435,0.000018161738,0.00014830809,0.00011625591,0.0004002726,0.004369145],"genre_scores_gemma":[0.9533725,0.00011047421,0.042732492,0.00005180697,0.000018253513,0.00023482734,0.000057115747,0.000028361626,0.0033940526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864334,0.00037883097,0.000054385404,0.00041364355,0.00031246358,0.0001973156],"domain_scores_gemma":[0.99864703,0.00051815045,0.0003690361,0.000048284954,0.0003472405,0.00007022941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013624473,0.00076269207,0.00089242763,0.0004649096,0.0006856416,0.0013520985,0.001400387,0.0011079594,0.0029997418],"category_scores_gemma":[0.0024569435,0.0008319708,0.00043616057,0.0004458408,0.0007246935,0.0007967068,0.0011834195,0.0006439961,0.0003659511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105075356,0.000021854445,0.00042281553,0.000050101382,0.000018822344,0.00005819843,0.0000430842,0.9815384,0.0032579666,0.0031644506,0.00020800794,0.011111185],"study_design_scores_gemma":[0.000018033403,0.00008203047,0.00017928606,0.000004447841,0.000014132461,0.000014817359,0.0000083815785,0.99787927,0.0005519748,0.0008515372,0.00038843244,0.000007653277],"about_ca_topic_score_codex":0.007271102,"about_ca_topic_score_gemma":0.005302313,"teacher_disagreement_score":0.007271102,"about_ca_system_score_codex":0.0014621455,"about_ca_system_score_gemma":0.0016978581,"threshold_uncertainty_score":0.014457583},"labels":[],"label_agreement":null},{"id":"W2122432832","doi":"10.1061/40941(247)113","title":"Multi-objective Optimization for Monitoring Sensor Placement in Water Distribution Systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":63,"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 Guelph","funders":"Canada Research Chairs","keywords":"Computer science; Multi-objective optimization; Ranking (information retrieval); Genetic algorithm; Wireless sensor network; Benchmark (surveying); Node (physics); Data mining; Pareto principle; Optimization problem; Population; Intrusion detection system; Engineering; Artificial intelligence; Machine learning; Computer network","score_opus":0.01859172971830028,"score_gpt":0.21340561460514548,"score_spread":0.1948138848868452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122432832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0675879,0.00080456195,0.9280482,0.00035465046,0.000045751163,0.00014808853,0.000113805654,0.00016515632,0.002731854],"genre_scores_gemma":[0.80807453,0.0004737609,0.18806955,0.0001006321,0.000034116314,0.0003809974,0.00014673924,0.000060303446,0.002659348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923325,0.0004286692,0.000027452323,0.00008992278,0.00014214512,0.0000786831],"domain_scores_gemma":[0.9988777,0.00076889794,0.00014879201,0.000029368932,0.0001285761,0.000046589117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022025807,0.001381583,0.0011736628,0.0012732325,0.00040276043,0.0009682167,0.0008261112,0.0011519596,0.0012411204],"category_scores_gemma":[0.0036306423,0.0007343094,0.00066536205,0.001193484,0.00073291984,0.0008781282,0.0007355662,0.0007851252,0.00014406617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012358333,0.000008758291,0.000105885476,0.000013299716,0.000011108747,0.000009099925,0.0000044264684,0.9969273,0.0001482776,0.00040161013,0.000049233666,0.0023085237],"study_design_scores_gemma":[0.000005537965,0.00001518289,0.00005868766,0.0000026469115,0.000003214624,0.0000021959202,0.0000047895974,0.9991691,0.0001161865,0.00054994394,0.000070821894,0.0000017980615],"about_ca_topic_score_codex":0.0074863643,"about_ca_topic_score_gemma":0.0047767204,"teacher_disagreement_score":0.0074863643,"about_ca_system_score_codex":0.0014058045,"about_ca_system_score_gemma":0.0011638209,"threshold_uncertainty_score":0.014885604},"labels":[],"label_agreement":null},{"id":"W2123106436","doi":"10.1061/40976(316)498","title":"Prioritising Individual Water Mains for Renewal","year":2008,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2008","topic":"Water Systems and Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Mains electricity; Breakage; Aggregate (composite); Homogeneous; Electricity; Engineering; Forensic engineering; Computer science; Environmental science; Mathematics","score_opus":0.009340418728837561,"score_gpt":0.16464983674343509,"score_spread":0.15530941801459752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123106436","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":[0.56570697,0.0007043559,0.39366844,0.0013508082,0.00018794973,0.0010223348,0.00044616012,0.0034098323,0.033503108],"genre_scores_gemma":[0.9290563,0.0002559947,0.057188507,0.000111349655,0.00003526992,0.00015416056,0.000333672,0.00024385798,0.012620838],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922574,0.000117735966,0.000028501672,0.0001388611,0.00018891106,0.0003002207],"domain_scores_gemma":[0.998505,0.00036901157,0.00019764721,0.00011237385,0.00033380033,0.00048228714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013309498,0.00067389145,0.00086381566,0.001093806,0.00069093774,0.0014860358,0.0018923548,0.0009372927,0.01374567],"category_scores_gemma":[0.0023704097,0.0005422553,0.000696755,0.00062174175,0.0005637777,0.0022828954,0.00217033,0.0008136441,0.0024954989],"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":[0.0010307519,0.00062997715,0.05790423,0.0008319903,0.00013384558,0.0012506854,0.001468859,0.1889748,0.1195121,0.020986533,0.017073393,0.59020287],"study_design_scores_gemma":[0.00016683587,0.0031826387,0.08543437,0.00042977775,0.0003925483,0.0009609142,0.005200058,0.6947457,0.08933476,0.03246529,0.087447986,0.00023909842],"about_ca_topic_score_codex":0.0052983854,"about_ca_topic_score_gemma":0.012533744,"teacher_disagreement_score":0.01374567,"about_ca_system_score_codex":0.0007419639,"about_ca_system_score_gemma":0.0022099528,"threshold_uncertainty_score":0.04598385},"labels":[],"label_agreement":null},{"id":"W2123936582","doi":"10.1061/40685(2003)220","title":"A Framework for Risk Analysis in Potable Water Supply","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Water supply; Potable water; Risk analysis (engineering); Water resource management; Natural disaster; Risk assessment; Risk management; Computer science; Business; Environmental science; Environmental economics; Environmental engineering; Computer security; Finance; Economics","score_opus":0.0061522824271227705,"score_gpt":0.19600846306102973,"score_spread":0.18985618063390697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123936582","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":[0.0029135316,0.0016868871,0.9832618,0.0011793419,0.000086357286,0.000052471732,0.00013287185,0.0000719504,0.01061474],"genre_scores_gemma":[0.563163,0.008708959,0.40634608,0.00059122976,0.0009616415,0.0009186528,0.00053467293,0.00020640387,0.018569339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99737954,0.0013471158,0.00010293232,0.0002670416,0.00071372004,0.00018965638],"domain_scores_gemma":[0.9967603,0.0023165026,0.00025224537,0.00013152015,0.00039881945,0.00014069941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042987857,0.0017068937,0.0013638118,0.0016255473,0.0009336282,0.0033781775,0.002334514,0.0021588544,0.0050452463],"category_scores_gemma":[0.007482705,0.00059866847,0.0015951184,0.0015442263,0.002401226,0.0036160168,0.00209997,0.002786267,0.00058452704],"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":[0.000008594263,0.000022240434,0.00022853792,0.0000792854,0.00004176625,0.00011509211,0.000120444,0.23822546,0.00019284467,0.749574,0.0019422896,0.009449395],"study_design_scores_gemma":[0.000008239761,0.000022654687,0.000119237375,0.000045345492,0.000014867101,0.000044389708,0.000075221935,0.3656491,0.000066905624,0.6268795,0.007059031,0.000015442276],"about_ca_topic_score_codex":0.007917255,"about_ca_topic_score_gemma":0.0046071336,"teacher_disagreement_score":0.007917255,"about_ca_system_score_codex":0.002581503,"about_ca_system_score_gemma":0.0023162232,"threshold_uncertainty_score":0.022734463},"labels":[],"label_agreement":null},{"id":"W2124164983","doi":"10.1061/(asce)cf.1943-5509.0000094","title":"Automated Detection and Location of Leaks in Water Mains Using Infrared Photography","year":2009,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Water Systems and Optimization","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mains electricity; Thermography; Environmental science; Pipeline transport; Leak detection; Leak; Engineering; Environmental engineering; Infrared","score_opus":0.00587241556748353,"score_gpt":0.1821475447423813,"score_spread":0.17627512917489777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124164983","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":[0.60663176,0.00019629986,0.38868725,0.000090150585,0.000016624417,0.000101548474,0.00018512312,0.0014885052,0.002602686],"genre_scores_gemma":[0.98259974,0.0000798438,0.016423643,0.0000056259346,0.00000252945,0.000021824417,0.000049118007,0.000011812415,0.0008058149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986315,0.000022804286,0.0000060427474,0.00004311769,0.000038058668,0.00002685311],"domain_scores_gemma":[0.9997081,0.00012498315,0.00007299094,0.000019820785,0.00006152764,0.000012623994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025725676,0.00048317574,0.00046752745,0.00055768335,0.00024349481,0.00065166963,0.0005243019,0.0005733381,0.0007465976],"category_scores_gemma":[0.0008524956,0.00035904098,0.0003263477,0.00035896344,0.0004041223,0.00069943216,0.00029204253,0.00031873042,0.0002233891],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014876736,0.00009966216,0.0190763,0.000087397035,0.00002586962,0.00015513516,0.000121382516,0.909526,0.025205629,0.00057527015,0.00045782622,0.044520803],"study_design_scores_gemma":[0.0000054722323,0.00003945117,0.004676407,0.0000037415236,0.000005787606,0.000019251,0.00002219105,0.9919756,0.0030365898,0.00010867084,0.00009975955,0.0000071717564],"about_ca_topic_score_codex":0.034581114,"about_ca_topic_score_gemma":0.029603845,"teacher_disagreement_score":0.034581114,"about_ca_system_score_codex":0.001057834,"about_ca_system_score_gemma":0.0008706694,"threshold_uncertainty_score":0.06875968},"labels":[],"label_agreement":null},{"id":"W2124838890","doi":"10.2166/hydro.2011.029","title":"Comparison of four models to rank failure likelihood of individual pipes","year":2011,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":44,"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":"Breakage; Mains electricity; Heuristic; Statistical model; Probabilistic logic; Bayesian probability; Rank (graph theory); Econometrics; Computer science; Environmental science; Statistics; Engineering; Forensic engineering; Mathematics; Artificial intelligence","score_opus":0.04691231159900089,"score_gpt":0.23110680387576993,"score_spread":0.18419449227676904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124838890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38543323,0.00081821176,0.6056287,0.0008907077,0.00012743688,0.00044400696,0.001317119,0.0013297312,0.00401067],"genre_scores_gemma":[0.9027947,0.00028402233,0.091470614,0.00016258223,0.00005881334,0.0004449669,0.0015456827,0.00012725593,0.0031112924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99435836,0.0036043364,0.00033286551,0.0006247803,0.0006390055,0.00044065292],"domain_scores_gemma":[0.9607635,0.033245582,0.0016573208,0.0011396704,0.0026795506,0.00051431643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016259702,0.0017163034,0.0017422114,0.0036600796,0.000743823,0.0018259403,0.0029119346,0.0015559159,0.0035847723],"category_scores_gemma":[0.028790813,0.0006485797,0.0024152775,0.0015585234,0.0009841748,0.0019928871,0.0012491768,0.0012344868,0.0008686764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064618647,0.00023540792,0.011555748,0.00011518163,0.00023107186,0.000055602985,0.00014744661,0.93172574,0.00022119671,0.005095116,0.0012052786,0.04876599],"study_design_scores_gemma":[0.00003820187,0.00012075934,0.0014927097,0.00001643399,0.000038602822,0.000021927428,0.00004119545,0.9954998,0.00011695816,0.002410909,0.00017801432,0.000024491808],"about_ca_topic_score_codex":0.02443218,"about_ca_topic_score_gemma":0.015322727,"teacher_disagreement_score":0.02443218,"about_ca_system_score_codex":0.0023782968,"about_ca_system_score_gemma":0.003021837,"threshold_uncertainty_score":0.08599055},"labels":[],"label_agreement":null},{"id":"W2126820919","doi":"10.1061/9780784479360.075","title":"Metrics for the Rapid Assessment of Transient Severity in Pipelines","year":2015,"lang":"en","type":"article","venue":"Pipelines 2015","topic":"Water Systems and 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":"HydraTek (Canada); University of Toronto","funders":"","keywords":"Transient (computer programming); Transient flow; Momentum (technical analysis); Energy (signal processing); Computer science; Pipeline transport; Key (lock); Energy–momentum relation; Flow (mathematics); Fluid mechanics; Property (philosophy); Statistical physics; Mechanics; Industrial engineering; Physics; Mechanical engineering; Engineering; Electrical engineering","score_opus":0.04481740825030218,"score_gpt":0.2904257825847647,"score_spread":0.24560837433446253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126820919","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":[0.20255738,0.001809457,0.78222656,0.00061634753,0.00012496342,0.00040525332,0.0022618428,0.0020294373,0.007968759],"genre_scores_gemma":[0.8738422,0.00055955356,0.12263544,0.000041285795,0.00007011788,0.0002171812,0.0014872976,0.00016218072,0.0009847105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971306,0.00083283696,0.00038768133,0.00030898277,0.0011944717,0.00014554129],"domain_scores_gemma":[0.98991346,0.003913544,0.0025218257,0.00091286015,0.002270733,0.00046757073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040986347,0.0016574615,0.0013145668,0.0075665237,0.00055016205,0.00236248,0.0008332303,0.0010883025,0.0018350002],"category_scores_gemma":[0.021476777,0.00039589548,0.0006285683,0.0037668434,0.0012113675,0.003727236,0.0025048365,0.0012489061,0.0004154013],"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":[0.00035197614,0.00016319142,0.06551987,0.0006633387,0.000250564,0.00029126395,0.0006300677,0.634318,0.014365423,0.05324037,0.0048203375,0.2253856],"study_design_scores_gemma":[0.00001566714,0.00046985774,0.033040944,0.00014164446,0.000053643198,0.00023285215,0.0005328115,0.91626185,0.0063388953,0.036039267,0.006732357,0.00014026032],"about_ca_topic_score_codex":0.0026174644,"about_ca_topic_score_gemma":0.0016626044,"teacher_disagreement_score":0.0075665237,"about_ca_system_score_codex":0.0011881437,"about_ca_system_score_gemma":0.00080849876,"threshold_uncertainty_score":0.021675944},"labels":[],"label_agreement":null},{"id":"W2127678990","doi":"10.5402/2011/930460","title":"Rapid Filling Analysis of Pipelines with Undulating Profiles by the Method of Characteristics","year":2011,"lang":"en","type":"article","venue":"ISRN Applied Mathematics","topic":"Water Systems and 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":"","keywords":"Water hammer; Pipeline (software); Column (typography); Front (military); Pipeline transport; Transient flow; Flow (mathematics); Grid; Mechanics; Process (computing); Geology; Water column; Computer science; Marine engineering; Geotechnical engineering; Structural engineering; Engineering; Surge; Mechanical engineering; Physics","score_opus":0.02353107529286849,"score_gpt":0.21478345515655223,"score_spread":0.19125237986368374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127678990","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":[0.1374862,0.00022888689,0.8580552,0.00017074797,0.000034211756,0.000071429,0.000083036364,0.00023368192,0.0036366342],"genre_scores_gemma":[0.9506434,0.00023466437,0.045410063,0.000032195978,0.000013288595,0.00009691896,0.00007005673,0.00007717229,0.0034221672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998822,0.000039749655,0.000004847642,0.000014264163,0.000036981775,0.000021927071],"domain_scores_gemma":[0.9995987,0.00022418756,0.00008389958,0.000025057903,0.000051254803,0.000016919234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034985994,0.00039890097,0.0004490264,0.0004540756,0.00036556213,0.00051388814,0.00058393984,0.0006473137,0.00070858444],"category_scores_gemma":[0.0011650815,0.00037489925,0.00055073394,0.00037347557,0.0006503859,0.0005295711,0.0004679073,0.00041265608,0.000103144994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021891241,0.0000090908725,0.0005267429,0.000024977131,0.000006616072,0.000057805253,0.00003839615,0.98696846,0.0035274196,0.005664348,0.00014669183,0.0030075812],"study_design_scores_gemma":[0.0000016925104,0.000005496499,0.000054043754,8.6340333e-7,7.103965e-7,0.0000045330917,0.0000037933237,0.99916637,0.00023266539,0.00039050967,0.00013717207,0.0000021491082],"about_ca_topic_score_codex":0.008505128,"about_ca_topic_score_gemma":0.00375592,"teacher_disagreement_score":0.008505128,"about_ca_system_score_codex":0.0006432453,"about_ca_system_score_gemma":0.001148092,"threshold_uncertainty_score":0.016911268},"labels":[],"label_agreement":null},{"id":"W2127996113","doi":"10.1139/cjce-2014-0187","title":"GA–GHCA model for the optimal design of pumped sewer networks","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":27,"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":"Genetic algorithm; Benchmark (surveying); Cellular automaton; Cover (algebra); Computer science; Mathematical optimization; Optimal design; Engineering; Algorithm; Mathematics; Mechanical engineering; Geology","score_opus":0.01125560790457886,"score_gpt":0.1607848932966125,"score_spread":0.14952928539203364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127996113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012373277,0.00043208792,0.98268706,0.00010973898,0.000052887153,0.000059837163,0.000076508964,0.000241656,0.00396693],"genre_scores_gemma":[0.6899117,0.00078730966,0.30115262,0.00014394468,0.000039981718,0.0005616272,0.00029337982,0.00010820904,0.007001115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995844,0.00013904506,0.000014736501,0.000099998266,0.00010726918,0.00005451593],"domain_scores_gemma":[0.9995053,0.00030082007,0.000035242134,0.000029716557,0.00010836372,0.000020653744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065552484,0.0011354003,0.0011906191,0.0007693164,0.00043539057,0.00085984805,0.0014986157,0.0014176961,0.0029080717],"category_scores_gemma":[0.0014741094,0.0006202113,0.0010968617,0.0007599728,0.00079387624,0.0007911259,0.0006284979,0.0010961964,0.0003324369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000682936,0.0000037397006,0.00006775435,0.000014976731,0.000008737699,0.000010450648,0.0000065710137,0.9945199,0.00026439386,0.0017847583,0.00010698637,0.00320489],"study_design_scores_gemma":[0.000003031243,0.000007239647,0.00002157245,0.0000025129934,0.0000035499775,0.0000034934674,0.0000017960172,0.99854624,0.00009488105,0.0010140911,0.00029979172,0.0000018650474],"about_ca_topic_score_codex":0.019002462,"about_ca_topic_score_gemma":0.013946521,"teacher_disagreement_score":0.019002462,"about_ca_system_score_codex":0.0011560516,"about_ca_system_score_gemma":0.0017710422,"threshold_uncertainty_score":0.037783742},"labels":[],"label_agreement":null},{"id":"W2130079230","doi":"10.1061/41024(340)32","title":"Optimization of Water Tank Design and Location in Water Distribution Systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Sizing; Storage tank; Robustness (evolution); Water hammer; Solver; Volume (thermodynamics); Water storage; Computer science; Engineering; Process engineering; Reliability engineering; Waste management; Mechanical engineering","score_opus":0.006750319033528747,"score_gpt":0.16817973929853344,"score_spread":0.1614294202650047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130079230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19301109,0.00078427675,0.79950637,0.00023637923,0.00002992829,0.00020888961,0.00033879417,0.0005239173,0.00536027],"genre_scores_gemma":[0.88187253,0.00043265888,0.11308885,0.000031686577,0.000011075063,0.00019433367,0.0001732871,0.00009849675,0.0040971236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996691,0.00014080285,0.000014257247,0.00006357482,0.000060529335,0.00005171693],"domain_scores_gemma":[0.9995864,0.00026392212,0.00006714227,0.000013122025,0.000047374207,0.00002207604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007968165,0.0009771945,0.0007695386,0.0006878456,0.00037150583,0.00085380283,0.00056509173,0.00080603786,0.002086415],"category_scores_gemma":[0.0013178054,0.00060347957,0.00040856507,0.0010203316,0.00059418735,0.0008733206,0.0006076953,0.00040459025,0.00022554147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037403523,0.000013616796,0.00024176271,0.00004603231,0.000007083224,0.000020009096,0.000011839925,0.9898901,0.0014853114,0.0006770527,0.00013249261,0.0074373167],"study_design_scores_gemma":[0.000028631832,0.00009573251,0.0002861626,0.000007597947,0.000012575026,0.000017269518,0.000029046603,0.99490947,0.0025437342,0.0013228006,0.00074018695,0.0000068073855],"about_ca_topic_score_codex":0.007683404,"about_ca_topic_score_gemma":0.007525318,"teacher_disagreement_score":0.007683404,"about_ca_system_score_codex":0.0014993872,"about_ca_system_score_gemma":0.0012707973,"threshold_uncertainty_score":0.015277326},"labels":[],"label_agreement":null},{"id":"W2130336298","doi":"10.1061/(asce)0733-9429(2002)128:6(616)","title":"Extended-Period Analysis with a Transient Model","year":2002,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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; Golder Associates (Canada)","funders":"","keywords":"Transient (computer programming); Routing (electronic design automation); Hydraulic machinery; Water hammer; Range (aeronautics); Coupling (piping); Computer science; Steady state (chemistry); Engineering; Simulation; Reliability engineering; Marine engineering; Mechanical engineering; Electronic engineering","score_opus":0.006611783952904357,"score_gpt":0.1626338775082551,"score_spread":0.15602209355535074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130336298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07083085,0.000380776,0.9023152,0.0004159063,0.0000961204,0.00006672011,0.00047855685,0.00046447397,0.024951389],"genre_scores_gemma":[0.9629511,0.0003837742,0.022164738,0.000089207155,0.000047065078,0.0001652785,0.00024122048,0.0001382867,0.013819345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997212,0.000088635345,0.000013199206,0.0000506863,0.000063167776,0.00006305606],"domain_scores_gemma":[0.9992617,0.00034614187,0.00010560668,0.00010610093,0.00013303621,0.00004737029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005999877,0.00051885337,0.0006333378,0.00042651227,0.00036501157,0.0009461517,0.0013128833,0.0010817662,0.0070555895],"category_scores_gemma":[0.0025265361,0.00041355612,0.0010215611,0.00044116567,0.0005455519,0.0018798423,0.0007782532,0.0010952125,0.0006156699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023012068,0.000010695188,0.00021977932,0.0000145858485,0.000012011226,0.00005848155,0.000024084216,0.9867856,0.00049921084,0.01043458,0.0002793292,0.0016386837],"study_design_scores_gemma":[0.0000034344403,0.000007254268,0.000060417562,0.0000022539423,0.000004212959,0.0000075810203,0.000005913086,0.99739313,0.00007567531,0.0021516103,0.0002856035,0.0000028845743],"about_ca_topic_score_codex":0.010158063,"about_ca_topic_score_gemma":0.004041504,"teacher_disagreement_score":0.010158063,"about_ca_system_score_codex":0.0008217393,"about_ca_system_score_gemma":0.00092231156,"threshold_uncertainty_score":0.02360326},"labels":[],"label_agreement":null},{"id":"W2130878320","doi":"10.1139/cjce-2014-0449","title":"Determination of cast iron pipe wall decay coefficient for combined chlorine in a municipal water distribution system","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":25,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chlorine; Piping; Water quality; Cast iron; Chloramine; Environmental science; Disinfectant; Environmental engineering; Chemistry; Materials science; Metallurgy","score_opus":0.011082497080463265,"score_gpt":0.1829167895423536,"score_spread":0.17183429246189033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130878320","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":[0.9979054,0.0000060731354,0.0018858031,0.000005896252,4.4092434e-7,0.000005043764,0.00003824845,0.000023765748,0.00012930391],"genre_scores_gemma":[0.99899477,0.000006349272,0.00084077095,0.0000012853708,9.9453096e-8,0.0000032899436,0.0000486807,0.0000038546877,0.00010089434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998604,0.000021372327,0.000005861705,0.00004100441,0.00004594392,0.000025452084],"domain_scores_gemma":[0.9995968,0.00016794508,0.00006982622,0.00002743336,0.00011763196,0.00002036758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036211353,0.0003435575,0.0002678148,0.000328635,0.00030038858,0.00040441958,0.0003522137,0.00034383996,0.00022963299],"category_scores_gemma":[0.0010079113,0.00028480808,0.00025482292,0.00030606796,0.00023491376,0.00027623348,0.00019439768,0.0003480255,0.000060588798],"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":[0.00024228981,0.00014390642,0.14510086,0.000044773784,0.000049754126,0.000206342,0.00016058025,0.739445,0.101915546,0.00028686185,0.00018193976,0.012222216],"study_design_scores_gemma":[0.000017362254,0.00014371461,0.04622768,0.000005223983,0.000022862463,0.00002152417,0.000082722996,0.91299903,0.040233403,0.00007769706,0.0001479537,0.000020758702],"about_ca_topic_score_codex":0.1436691,"about_ca_topic_score_gemma":0.14053091,"teacher_disagreement_score":0.1436691,"about_ca_system_score_codex":0.002075825,"about_ca_system_score_gemma":0.0013177037,"threshold_uncertainty_score":0.2856657},"labels":[],"label_agreement":null},{"id":"W2131619144","doi":"10.1139/l07-075","title":"Water pipe renewal using a multiobjective optimization approach","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":19,"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":"Pareto principle; Reliability (semiconductor); Multi-objective optimization; Scheduling (production processes); Pipe network analysis; Ranking (information retrieval); Genetic algorithm; Operations research; Reliability engineering; Computer science; Engineering; Operations management","score_opus":0.011570461226065235,"score_gpt":0.15953048264969727,"score_spread":0.14796002142363204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131619144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031025646,0.0006461908,0.95870256,0.00040070163,0.00006405035,0.00018693379,0.00012551744,0.00014718823,0.008701188],"genre_scores_gemma":[0.65972424,0.0008116924,0.3267131,0.00023281452,0.00010194199,0.0010180506,0.00027113664,0.00011852586,0.011008559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992924,0.00034767753,0.000027453738,0.0000947664,0.00014653985,0.0000912612],"domain_scores_gemma":[0.9992632,0.0004941908,0.000071487564,0.00002064329,0.00010961159,0.00004083791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013047502,0.0013154792,0.0016203228,0.0017267631,0.0005471363,0.0016853702,0.0016262718,0.0022122608,0.0034403692],"category_scores_gemma":[0.0018469662,0.0008913909,0.0013663938,0.0014717039,0.000702504,0.0009394765,0.0011488535,0.0009437137,0.00032643517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008165353,0.000018034474,0.00009290442,0.000024041596,0.000019333263,0.000023658124,0.000008053763,0.993845,0.00013909624,0.0022327616,0.000109789544,0.0034791064],"study_design_scores_gemma":[0.000006452939,0.000014458289,0.000032057455,0.000004325573,0.000005170249,0.0000037120947,0.0000056608083,0.9980142,0.000050894825,0.0016522675,0.00020801173,0.0000026397581],"about_ca_topic_score_codex":0.007052564,"about_ca_topic_score_gemma":0.004788129,"teacher_disagreement_score":0.007052564,"about_ca_system_score_codex":0.0016037227,"about_ca_system_score_gemma":0.0015560121,"threshold_uncertainty_score":0.014023066},"labels":[],"label_agreement":null},{"id":"W2132490736","doi":"10.1080/07011784.2013.773658","title":"Application of parallel computing in data mining for contaminant source identification in water distribution systems","year":2013,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"False positive rate; Computer science; Identification (biology); Intrusion; Data mining; Intrusion detection system; Artificial intelligence; Geology","score_opus":0.014364443020754332,"score_gpt":0.1955412515764599,"score_spread":0.1811768085557056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132490736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33027193,0.0014899527,0.6588495,0.0014172016,0.000120222765,0.00034196745,0.0004941238,0.0014130885,0.005602051],"genre_scores_gemma":[0.7718642,0.00054587785,0.22674476,0.00005751409,0.000026722902,0.00010717426,0.0001873085,0.000028305582,0.00043823489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991898,0.0003786672,0.000078170495,0.00012724349,0.00017154175,0.00005466947],"domain_scores_gemma":[0.9975943,0.0017246171,0.00014437018,0.00023662612,0.00024044277,0.00005958253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001633054,0.0005346844,0.00069274,0.0008897951,0.000627933,0.0008344877,0.00065832684,0.0003239577,0.00059261196],"category_scores_gemma":[0.004608426,0.00030811387,0.00051198964,0.0014601091,0.00035757624,0.00068866025,0.00052135694,0.0005836689,0.00010422973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024910324,0.00015309323,0.011059198,0.00019083913,0.00010979032,0.00019518689,0.00010815417,0.83163244,0.0033768816,0.005103787,0.0007794683,0.14704204],"study_design_scores_gemma":[0.000009153681,0.000036622376,0.00093059876,0.000005195906,0.00001084044,0.00003321071,0.000033836128,0.9918984,0.0016138568,0.0049572573,0.00046669532,0.00000427691],"about_ca_topic_score_codex":0.009603881,"about_ca_topic_score_gemma":0.007872299,"teacher_disagreement_score":0.009603881,"about_ca_system_score_codex":0.0007363285,"about_ca_system_score_gemma":0.001823071,"threshold_uncertainty_score":0.019095957},"labels":[],"label_agreement":null},{"id":"W2132867480","doi":"10.1680/jees.2013.0058","title":"Physical and computational modelling of residence and flow development time in a large municipal disinfection clearwell","year":2013,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residence time distribution; TRACER; Flow (mathematics); Residence time (fluid dynamics); Scale (ratio); Environmental science; Volumetric flow rate; Flow conditions; Mechanics; Transient flow; Steady state (chemistry); Hydrology (agriculture); Simulation; Computer science; Geology; Geotechnical engineering; Chemistry; Geography; Physics","score_opus":0.005108883419394761,"score_gpt":0.16138105806974382,"score_spread":0.15627217465034907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132867480","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":[0.99165595,0.00004132204,0.0066144913,0.00006603659,0.0000054934876,0.000029608913,0.0002003318,0.00008590425,0.0013010032],"genre_scores_gemma":[0.9956994,0.00004133883,0.003406082,0.000007831825,0.0000011173177,0.00002872195,0.00011084906,0.00001298522,0.00069167546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987864,0.00001988671,0.0000058065298,0.000030796884,0.000025437552,0.000039398],"domain_scores_gemma":[0.99968016,0.00017220419,0.00004895889,0.000021816128,0.000047326856,0.000029536312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002511978,0.00044893014,0.00035603243,0.0002955153,0.0006075432,0.0008597317,0.00081962236,0.0009436943,0.0011818138],"category_scores_gemma":[0.0007557297,0.0005034452,0.00042797605,0.00031845464,0.00052830135,0.0006628901,0.0003909259,0.0006117958,0.00011126111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008555132,0.00009795561,0.008526027,0.000021355709,0.00000911322,0.00009465867,0.000052391722,0.9804498,0.007306028,0.00050667307,0.00012456538,0.0027259255],"study_design_scores_gemma":[0.000014891599,0.00005004316,0.0025801933,0.0000018783239,0.0000039686793,0.000009408868,0.000032857875,0.9956735,0.0014488584,0.000057782316,0.000117584896,0.000008990608],"about_ca_topic_score_codex":0.10460371,"about_ca_topic_score_gemma":0.07996752,"teacher_disagreement_score":0.10460371,"about_ca_system_score_codex":0.0019767315,"about_ca_system_score_gemma":0.0023373086,"threshold_uncertainty_score":0.2079897},"labels":[],"label_agreement":null},{"id":"W2133129963","doi":"10.5555/1516744.1517180","title":"Simulation as a tool for life cycle cost analysis","year":2008,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Life cycle costing; Mains electricity; Rehabilitation; Computer science; Life-cycle cost analysis; Monte Carlo method; Electricity; Order (exchange); Risk analysis (engineering); Reliability engineering; Operations research; Engineering; Operations management; Business","score_opus":0.03335689129050514,"score_gpt":0.26449586372518974,"score_spread":0.2311389724346846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133129963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046668197,0.00083326874,0.9724505,0.00040956057,0.00013839442,0.00016685069,0.00095430744,0.0020433033,0.018336993],"genre_scores_gemma":[0.2741599,0.003656376,0.7056789,0.00024292643,0.00016169161,0.0016132895,0.0026227173,0.001368343,0.010495857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983165,0.0009145907,0.00009470944,0.00008902441,0.00050905655,0.00007613021],"domain_scores_gemma":[0.99628913,0.002678613,0.00015480851,0.0003410772,0.00046426282,0.0000721711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022848577,0.0014603991,0.0012334835,0.0025648996,0.00053326826,0.0019794558,0.0014410664,0.0012557075,0.012105322],"category_scores_gemma":[0.006335748,0.00079017424,0.0012776594,0.003428517,0.00048218324,0.0018257031,0.00079983316,0.0019462734,0.0021382144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003102138,0.000047616973,0.00039339377,0.00016440166,0.00007635143,0.000052046995,0.000038491333,0.89587873,0.00065342005,0.064057104,0.0042531695,0.034354348],"study_design_scores_gemma":[0.0000151831255,0.00003247037,0.00010554808,0.000059821217,0.000021972055,0.000030931507,0.000014711891,0.954219,0.00071710907,0.02799973,0.016764399,0.000019177716],"about_ca_topic_score_codex":0.0061584706,"about_ca_topic_score_gemma":0.0036954347,"teacher_disagreement_score":0.012105322,"about_ca_system_score_codex":0.0015605816,"about_ca_system_score_gemma":0.0016761641,"threshold_uncertainty_score":0.04049629},"labels":[],"label_agreement":null},{"id":"W2133168389","doi":"10.1061/(asce)0887-3828(2009)23:1(47)","title":"Prediction Models for Annual Break Rates of Water Mains","year":2009,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Water Systems and Optimization","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Mains electricity; Engineering; Electricity; Statistical analysis; Civil engineering; Environmental science; Forensic engineering; Statistics; Mathematics","score_opus":0.011348829705419899,"score_gpt":0.1890686449522204,"score_spread":0.1777198152468005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133168389","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":[0.8698642,0.00024485725,0.12459143,0.00016696779,0.000025927113,0.000056816112,0.0030466411,0.000811588,0.0011915235],"genre_scores_gemma":[0.9879452,0.00012334812,0.008904255,0.000005825311,0.0000059665826,0.000042325406,0.0019360612,0.000022725442,0.0010142992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996656,0.00006473607,0.00002309579,0.00011184943,0.00007431434,0.000060337086],"domain_scores_gemma":[0.9979365,0.00091343117,0.00055897725,0.00009897567,0.0004069373,0.000085207466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013118465,0.0007075436,0.00045799214,0.00088643434,0.00016862832,0.0005396051,0.0007734789,0.000490493,0.0011766443],"category_scores_gemma":[0.003953018,0.00029683337,0.0005678077,0.0007981083,0.00021758964,0.00062467565,0.00031176727,0.00079280615,0.00040596578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006673035,0.00004869955,0.041887656,0.00003828958,0.00003835694,0.00003253446,0.0000494959,0.9414492,0.001261356,0.00039477154,0.00055037055,0.014182572],"study_design_scores_gemma":[0.0000034582813,0.00004038002,0.014482724,0.000003728646,0.000008223971,0.000009490861,0.000023953775,0.98452526,0.00045848396,0.00024090623,0.0001969068,0.0000065241743],"about_ca_topic_score_codex":0.0359242,"about_ca_topic_score_gemma":0.036615197,"teacher_disagreement_score":0.0359242,"about_ca_system_score_codex":0.0010970901,"about_ca_system_score_gemma":0.0006007877,"threshold_uncertainty_score":0.07143021},"labels":[],"label_agreement":null},{"id":"W2133256626","doi":"10.1061/(asce)0733-9496(2002)128:6(441)","title":"Energy and Costs of Leaky Pipes: Toward Comprehensive Picture","year":2002,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":249,"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":"Energy (signal processing); Dimensionless quantity; Leak; Environmental science; Residence time (fluid dynamics); Pipe network analysis; Computer science; Environmental engineering; Simulation; Petroleum engineering; Mechanics; Engineering; Mathematics; Statistics; Geotechnical engineering; Physics","score_opus":0.0141417812223254,"score_gpt":0.1888532464398484,"score_spread":0.174711465217523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133256626","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":[0.600351,0.012282288,0.32476738,0.0035997992,0.00011086036,0.00014272638,0.006330407,0.00028495106,0.052130572],"genre_scores_gemma":[0.9478302,0.012343327,0.030907206,0.00017923741,0.000101886406,0.00013962178,0.0012046986,0.0001296598,0.007164196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9997559,0.000057909947,0.000017442178,0.000036833502,0.00010188288,0.000030033572],"domain_scores_gemma":[0.9992656,0.00037547806,0.00012181691,0.00006775194,0.00013644772,0.00003291603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041408374,0.0008823301,0.0008600816,0.0015739384,0.00033476367,0.0020173457,0.000857102,0.0010972683,0.0036206394],"category_scores_gemma":[0.002376736,0.0005946513,0.0007639294,0.0024090917,0.00057335675,0.00672778,0.0007072577,0.0014489886,0.00034027683],"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":[0.000038781578,0.000060375518,0.0035863197,0.00012815771,0.000041653402,0.00009245461,0.000037912763,0.9193307,0.001290963,0.057181098,0.0013145921,0.016896922],"study_design_scores_gemma":[0.00001840875,0.00009824645,0.013544868,0.000107435015,0.00005930364,0.00018880893,0.00024367092,0.83781636,0.0020789802,0.13438465,0.0114026535,0.00005653032],"about_ca_topic_score_codex":0.0052410015,"about_ca_topic_score_gemma":0.006324332,"teacher_disagreement_score":0.0052410015,"about_ca_system_score_codex":0.0012454279,"about_ca_system_score_gemma":0.0008176441,"threshold_uncertainty_score":0.0121123195},"labels":[],"label_agreement":null},{"id":"W2134050540","doi":"10.1139/er-2014-0069","title":"Contaminant intrusion in water distribution networks: review and proposal of an integrated model for decision making","year":2015,"lang":"en","type":"article","venue":"Environmental Reviews","topic":"Water Systems and Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Intrusion; Environmental science; Water quality; Contamination; Risk analysis (engineering); Computer science; Environmental engineering; Environmental resource management; Business; Ecology","score_opus":0.01562971269948686,"score_gpt":0.23586199409337558,"score_spread":0.22023228139388873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134050540","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":[0.008888967,0.16402262,0.79333574,0.00642325,0.0009904036,0.00026917658,0.0006141889,0.00027199523,0.02518372],"genre_scores_gemma":[0.3268049,0.42399606,0.23397464,0.0014125472,0.0017602533,0.001436425,0.0010767954,0.0001627861,0.009375546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885404,0.0005116402,0.00011495772,0.00017801707,0.00023983023,0.00010157395],"domain_scores_gemma":[0.9983693,0.0011328404,0.00014199365,0.00003928188,0.00027061853,0.00004604696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002225331,0.0020399643,0.0027629186,0.001944199,0.00053981395,0.003103899,0.0033415076,0.0032512548,0.0020959796],"category_scores_gemma":[0.003662525,0.0010316265,0.0023378248,0.003632737,0.0011928941,0.0025651495,0.0016682063,0.002184123,0.0005529217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039166367,0.00010330819,0.0010570256,0.0024999822,0.0002666777,0.0002425115,0.00012400081,0.7524618,0.00038506804,0.16880603,0.004868958,0.06914546],"study_design_scores_gemma":[0.000024365505,0.000110151675,0.00046531178,0.0010538675,0.00023000533,0.00014040501,0.0001128582,0.8453698,0.00029313893,0.115801536,0.036314912,0.00008369228],"about_ca_topic_score_codex":0.013434719,"about_ca_topic_score_gemma":0.007988059,"teacher_disagreement_score":0.013434719,"about_ca_system_score_codex":0.003592225,"about_ca_system_score_gemma":0.0042007877,"threshold_uncertainty_score":0.026713014},"labels":[],"label_agreement":null},{"id":"W2135802175","doi":"10.1061/41173(414)32","title":"Quantification of the Relative Importance of Factors Contributing to Intrusion in a Distribution System Using a Full Factorial Design","year":2011,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2011","topic":"Water Systems and 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; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Body orifice; Intrusion; Leakage (economics); Volume (thermodynamics); Environmental science; Simulation; Geology; Computer science; Engineering; Physics; Mechanical engineering","score_opus":0.02042665514804894,"score_gpt":0.1777552045663373,"score_spread":0.15732854941828836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135802175","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":[0.94975054,0.00017552011,0.045974202,0.0000382774,0.00006534308,0.0023493199,0.00052904844,0.0002369977,0.0008807674],"genre_scores_gemma":[0.8848958,0.00024204359,0.10126159,0.00011323795,0.000043235,0.010135969,0.0008820576,0.00011994603,0.0023060713],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99427223,0.0017255924,0.00058682554,0.0014949245,0.001298091,0.00062229176],"domain_scores_gemma":[0.992931,0.0034613865,0.0009803539,0.0009927604,0.0011529765,0.0004814648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00490142,0.0012266536,0.0020454836,0.0009698277,0.0007285977,0.0015108149,0.0015395662,0.00077935384,0.002244636],"category_scores_gemma":[0.0054960647,0.0005970289,0.0016752388,0.00089088816,0.001091055,0.0009037671,0.000943478,0.0014338524,0.0003192894],"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":[0.024525544,0.008995856,0.011014177,0.0010938615,0.0005842786,0.00015425455,0.000613293,0.02528872,0.8719034,0.0010375096,0.00025640792,0.054532766],"study_design_scores_gemma":[0.0022918028,0.28246292,0.101833686,0.000095675096,0.0020423685,0.00019981225,0.0007262655,0.09426262,0.502605,0.0023196142,0.010726616,0.00043361564],"about_ca_topic_score_codex":0.0011957798,"about_ca_topic_score_gemma":0.0010618541,"teacher_disagreement_score":0.00490142,"about_ca_system_score_codex":0.0016802698,"about_ca_system_score_gemma":0.0017700993,"threshold_uncertainty_score":0.025921524},"labels":[],"label_agreement":null},{"id":"W2135973695","doi":"10.1061/41069(360)139","title":"Quantification and Measurement of Level of Service of Water Distribution Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; American Water Works Association Research Foundation; Water Research Foundation","keywords":"Asset (computer security); Asset management; Reliability (semiconductor); Context (archaeology); Service (business); Computer science; Service level; Environmental economics; Order (exchange); Quality of service; Risk analysis (engineering); IT service continuity; Hierarchy; Reliability engineering; Business; Computer security; Engineering; Finance; Computer network; Marketing","score_opus":0.05504790917796214,"score_gpt":0.20464897915413438,"score_spread":0.14960106997617223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135973695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25314674,0.00019604787,0.7378283,0.00023600912,0.000015084478,0.00007121263,0.00079714326,0.0005559896,0.007153411],"genre_scores_gemma":[0.9658405,0.00012296232,0.033161208,0.000011762315,0.0000049572673,0.000040506773,0.00025417542,0.000029129673,0.0005348527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982868,0.00049824873,0.00009024231,0.00021290503,0.0007390394,0.00017283425],"domain_scores_gemma":[0.99843377,0.00062011473,0.00035850116,0.00025247593,0.0002763144,0.000058785972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012476755,0.0004597702,0.00035510008,0.001331128,0.00030256325,0.0015685495,0.0008124893,0.0008263915,0.00086955883],"category_scores_gemma":[0.0051448043,0.00027453134,0.00047401624,0.0016855675,0.0006768545,0.002166903,0.0010428205,0.00044605185,0.000215515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007806762,0.000055693374,0.02023167,0.00012885976,0.000047225127,0.00005863154,0.00034128432,0.85613227,0.023622736,0.048882928,0.0007611007,0.049659614],"study_design_scores_gemma":[0.0000039372458,0.000065972585,0.005475672,0.000019113875,0.0000124244525,0.000044166627,0.0001698057,0.9736516,0.007830563,0.010975124,0.001727181,0.000024562416],"about_ca_topic_score_codex":0.009681031,"about_ca_topic_score_gemma":0.005276564,"teacher_disagreement_score":0.009681031,"about_ca_system_score_codex":0.001993147,"about_ca_system_score_gemma":0.0009457862,"threshold_uncertainty_score":0.01924938},"labels":[],"label_agreement":null},{"id":"W2136464099","doi":"10.1061/40792(173)39","title":"A Numerical Study of a 2D Multi-Component Corrosion Model in a Water Distribution System","year":2005,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Hydroxide; Ferrous; Corrosion; Turbulence; Redox; Anode; Chemistry; Flow (mathematics); Chemical reaction; Diffusion; Materials science; Inorganic chemistry; Thermodynamics; Mechanics; Metallurgy; Electrode; Physics; Physical chemistry","score_opus":0.012867802325712038,"score_gpt":0.20415379744800366,"score_spread":0.1912859951222916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136464099","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":[0.68504596,0.00075725437,0.28245866,0.0012443042,0.0002089464,0.00021395732,0.0006073935,0.0005465898,0.028916936],"genre_scores_gemma":[0.97982395,0.00018986627,0.013835321,0.0000822014,0.000020983225,0.00013684336,0.00014410623,0.00003685485,0.0057298387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983263,0.000044600623,0.000009953462,0.000035926336,0.00003854069,0.000038309132],"domain_scores_gemma":[0.9996351,0.00015831031,0.000057790352,0.000019794781,0.00008677998,0.000042271335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003081068,0.00068768044,0.0008016473,0.00044065516,0.00078741234,0.001154008,0.0009628463,0.001960438,0.0020603351],"category_scores_gemma":[0.0008250673,0.00043679719,0.00083256245,0.00041185672,0.00090104144,0.0006615887,0.0008269673,0.0008055396,0.00020360822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027391867,0.000021305481,0.0007468105,0.000021630205,0.000006526528,0.00007979881,0.000029165165,0.99620676,0.0010500236,0.0010108905,0.00008172267,0.0007178778],"study_design_scores_gemma":[0.000004003696,0.000011255016,0.00010157531,0.0000014740493,0.0000021209332,0.0000058985484,0.00001055573,0.9995735,0.000106674095,0.00009241192,0.000088160894,0.0000023784357],"about_ca_topic_score_codex":0.026358614,"about_ca_topic_score_gemma":0.007811375,"teacher_disagreement_score":0.026358614,"about_ca_system_score_codex":0.0010008056,"about_ca_system_score_gemma":0.0010066398,"threshold_uncertainty_score":0.052410364},"labels":[],"label_agreement":null},{"id":"W2136582356","doi":"10.1061/9780784412947.093","title":"Simultaneous Optimization of Pipe and Pavement Renewal Scheduling","year":2013,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2013","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Scheduling (production processes); Breakage; Computer science; Pipe network analysis; Engineering; Civil engineering; Operations research; Operations management","score_opus":0.003574838718775101,"score_gpt":0.15090515468709337,"score_spread":0.14733031596831828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136582356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39159572,0.000786528,0.5940302,0.00042592737,0.000099627534,0.00020280277,0.00037445984,0.00046571915,0.012018967],"genre_scores_gemma":[0.9637491,0.00019282194,0.03141245,0.000028673601,0.000023815566,0.00010151626,0.000116246876,0.000051188028,0.00432427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949944,0.00015235152,0.0000165249,0.00010172123,0.000092630944,0.00013733511],"domain_scores_gemma":[0.9992824,0.00043541339,0.000103374776,0.000028301114,0.00006518323,0.00008534216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088755204,0.00091564824,0.0014056654,0.0008033522,0.00033884795,0.00097244704,0.0010871243,0.0013595835,0.002319229],"category_scores_gemma":[0.0023952925,0.0008496346,0.0007570916,0.001172489,0.0006763303,0.0010019696,0.00096251187,0.0007190343,0.00021170845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003482998,0.000013080725,0.00017325337,0.0000173752,0.000010910037,0.00002456953,0.000009116372,0.99479866,0.00041346776,0.0009812476,0.0001127578,0.0034106472],"study_design_scores_gemma":[0.000008937986,0.000037500104,0.00018401146,0.0000018098075,0.0000090892645,0.0000081688895,0.0000085289785,0.9979442,0.0002528809,0.0013352903,0.00020602783,0.0000035817075],"about_ca_topic_score_codex":0.006829445,"about_ca_topic_score_gemma":0.0053029135,"teacher_disagreement_score":0.006829445,"about_ca_system_score_codex":0.0011147764,"about_ca_system_score_gemma":0.001634724,"threshold_uncertainty_score":0.013579369},"labels":[],"label_agreement":null},{"id":"W2136759507","doi":"10.2118/163110-pa","title":"Novel Leak Detection Method for Sulfur Recovery Unit Condensers","year":2013,"lang":"en","type":"article","venue":"Oil and Gas Facilities","topic":"Water Systems and 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":"Shell (Canada)","funders":"","keywords":"Hydrogen sulfide; Sulfur; Sour gas; Waste management; Condenser (optics); Natural-gas processing; Leak; Tube (container); Natural gas; Chemistry; Environmental science; Petroleum engineering; Engineering; Materials science; Metallurgy; Environmental engineering","score_opus":0.016544968355294065,"score_gpt":0.20187917524334317,"score_spread":0.1853342068880491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136759507","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":[0.56970584,0.0018516182,0.42150384,0.0003452691,0.00017732957,0.00029907576,0.00025764172,0.0030057481,0.0028535982],"genre_scores_gemma":[0.8678529,0.0005388767,0.12754178,0.000096915086,0.000028099326,0.000082125596,0.000109675384,0.00004006224,0.0037095076],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992884,0.00007768842,0.000031218464,0.0001441901,0.00040609352,0.000052438383],"domain_scores_gemma":[0.9993135,0.0001020654,0.00013808672,0.000044917833,0.0003624404,0.000039063194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047643707,0.000442431,0.00036733542,0.0008653475,0.00034957204,0.00035208886,0.0008018234,0.00061310537,0.0009941151],"category_scores_gemma":[0.00059131003,0.000268435,0.00021534286,0.00028783426,0.00049599644,0.00064666936,0.00045011152,0.00045190495,0.00039482527],"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":[0.00007506986,0.000020657664,0.0013854988,0.00010287377,0.000005571969,0.000083075436,0.00008343996,0.00024626366,0.98017865,0.00021080104,0.00020869452,0.017399358],"study_design_scores_gemma":[0.000012887352,0.00018856744,0.0022430653,0.000008649808,0.000016513382,0.00028443278,0.00006344744,0.011111214,0.98325586,0.00006474605,0.0027192845,0.000031345324],"about_ca_topic_score_codex":0.0027672828,"about_ca_topic_score_gemma":0.0046634953,"teacher_disagreement_score":0.0027672828,"about_ca_system_score_codex":0.0006813139,"about_ca_system_score_gemma":0.00058353454,"threshold_uncertainty_score":0.005502343},"labels":[],"label_agreement":null},{"id":"W2137282635","doi":"","title":"An experimental and numerical study on hydraulic characteristics and theoretical equations of circular weirs","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"Discharge coefficient; Mechanics; Weir; Computational fluid dynamics; Hydrostatic equilibrium; Curvature; Cylinder; Fluent; Pressure coefficient; Hydraulic jump; Flow (mathematics); Geology; Geotechnical engineering; Mathematics; Geometry; Physics; Thermodynamics; Nozzle","score_opus":0.011752755923649274,"score_gpt":0.18990413828868472,"score_spread":0.17815138236503544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137282635","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":[0.9913135,0.000050548057,0.006972107,0.000027179061,0.000005988593,0.000011684519,0.000115042625,0.00008650273,0.0014174025],"genre_scores_gemma":[0.99701416,0.000038948387,0.002616032,0.0000019319473,0.0000012066021,0.0000060431225,0.000056950586,0.0000029790415,0.00026166282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998178,0.00002316971,0.000014069834,0.000047185044,0.00006735099,0.000030476549],"domain_scores_gemma":[0.999476,0.00022441137,0.0000641786,0.00009134606,0.000118765194,0.000025220676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032615705,0.00018044337,0.00023441056,0.00034038682,0.0003436928,0.00027845832,0.00032177995,0.000354235,0.0009260773],"category_scores_gemma":[0.00081278663,0.00011103198,0.00020083251,0.00046155503,0.00061016565,0.00039146777,0.00019580439,0.00022400549,0.000092922186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052618945,0.00047866662,0.036435846,0.00022696369,0.000027401493,0.0006232148,0.00065749156,0.6014654,0.30612493,0.0068001696,0.0007454926,0.04588829],"study_design_scores_gemma":[0.000027431335,0.00048950844,0.019225102,0.0000111534655,0.000015745836,0.00012466649,0.00020476396,0.916545,0.061686605,0.00054966676,0.0010755571,0.00004488507],"about_ca_topic_score_codex":0.004613985,"about_ca_topic_score_gemma":0.0032388296,"teacher_disagreement_score":0.004613985,"about_ca_system_score_codex":0.00038434207,"about_ca_system_score_gemma":0.00035092203,"threshold_uncertainty_score":0.009174287},"labels":[],"label_agreement":null},{"id":"W2138809084","doi":"10.1061/40976(316)197","title":"Performance of Water Treatment Plant Elements","year":2008,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2008","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Index (typography); Quality (philosophy); Liberian dollar; Environmental science; Computer science; Operations research; Water resource management; Engineering; Business","score_opus":0.006873569535014962,"score_gpt":0.1483476388244804,"score_spread":0.14147406928946543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138809084","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":[0.98438317,0.00013882779,0.007888161,0.000052213414,0.000018461169,0.000064771404,0.00051850517,0.0003386117,0.0065974337],"genre_scores_gemma":[0.9961389,0.00003871087,0.0014106589,0.000008091235,0.0000019056137,0.000013522398,0.00032770645,0.00001980381,0.00204073],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99906605,0.00011667516,0.00006389998,0.00015717241,0.0004539009,0.00014227295],"domain_scores_gemma":[0.9985343,0.00043402938,0.00013320513,0.00014000387,0.00065215514,0.00010630678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097506004,0.00043342405,0.0004883194,0.0006900908,0.00053076394,0.0013416326,0.000496104,0.00068855257,0.0054557626],"category_scores_gemma":[0.0027015759,0.00015317604,0.00043039728,0.0007487365,0.00026811066,0.001028639,0.0006262353,0.0003257006,0.0010730583],"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":[0.004179224,0.0008922725,0.10467985,0.00066900975,0.00022641396,0.0004481274,0.0005362411,0.32020986,0.32624438,0.002169247,0.0034340136,0.2363113],"study_design_scores_gemma":[0.00008102611,0.004984762,0.16414487,0.000050939183,0.00017076888,0.00025383194,0.0012924502,0.42210707,0.39484075,0.0019001701,0.010073679,0.00009971496],"about_ca_topic_score_codex":0.004647452,"about_ca_topic_score_gemma":0.004079428,"teacher_disagreement_score":0.0054557626,"about_ca_system_score_codex":0.001032053,"about_ca_system_score_gemma":0.0004305499,"threshold_uncertainty_score":0.01825136},"labels":[],"label_agreement":null},{"id":"W2138921861","doi":"10.5194/dwes-3-71-2010","title":"I-WARP: Individual Water mAin Renewal Planner","year":2010,"lang":"en","type":"article","venue":"Drinking water engineering and science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"American Water Works Association Research Foundation; Water Research Foundation","keywords":"Breakage; Mains electricity; Foundation (evidence); Engineering; Electricity; Civil engineering; Geotechnical engineering; Environmental science; Forensic engineering; Geography; Computer science; Archaeology","score_opus":0.00524188546840148,"score_gpt":0.16877151204562993,"score_spread":0.16352962657722844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138921861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09730228,0.00034197557,0.82761425,0.0016916238,0.000095792275,0.001052131,0.0020140754,0.002491356,0.06739657],"genre_scores_gemma":[0.853525,0.00022667536,0.120435275,0.00018435168,0.000031709977,0.00067531253,0.00060247234,0.00022445411,0.024094734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967456,0.000090905145,0.000011030096,0.00006450408,0.000059916285,0.0000990086],"domain_scores_gemma":[0.99940646,0.00025139356,0.00007056651,0.00005122438,0.0000780826,0.00014241267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010321632,0.00063120463,0.0007018466,0.0005421961,0.00042192973,0.0010128475,0.0016223838,0.0009673975,0.023361059],"category_scores_gemma":[0.0019725629,0.0004882975,0.0005399632,0.00067333103,0.00056914927,0.0014019635,0.0017678039,0.0012212384,0.0012397836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012892911,0.00008040547,0.0006231843,0.00006106103,0.000020285144,0.000085242296,0.00005564481,0.93822545,0.0003905378,0.023715954,0.0053105797,0.031302687],"study_design_scores_gemma":[0.000027951788,0.00006052564,0.00009815487,0.000008590913,0.000007069348,0.00002397636,0.00004890025,0.9868586,0.00025008517,0.0091194045,0.0034908098,0.0000060547777],"about_ca_topic_score_codex":0.0061011873,"about_ca_topic_score_gemma":0.00586481,"teacher_disagreement_score":0.023361059,"about_ca_system_score_codex":0.0014386395,"about_ca_system_score_gemma":0.0020755136,"threshold_uncertainty_score":0.07815051},"labels":[],"label_agreement":null},{"id":"W2142236504","doi":"10.1061/9780784412312.321","title":"Multi-Objective Rehabilitation Planning of Water Distribution Systems under Climate Change Mitigation Scenarios","year":2012,"lang":"en","type":"article","venue":"World Environmental And Water Resources Congress 2012","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Greenhouse gas; Discounting; Status quo; Environmental science; Climate change; Carbon price; Electricity; Environmental economics; Natural resource economics; Distribution (mathematics); Water conservation; Environmental engineering; Business; Water resources; Computer science; Economics; Engineering","score_opus":0.010210571155328524,"score_gpt":0.19308085120307453,"score_spread":0.182870280047746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142236504","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":[0.8466938,0.0004921209,0.14176841,0.000751517,0.000027029018,0.00026305814,0.00055864255,0.00019422383,0.009251115],"genre_scores_gemma":[0.98776567,0.00011722624,0.009828146,0.00002174112,0.0000035928572,0.0000831766,0.00011421383,0.000015617812,0.0020504752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964356,0.00017285768,0.000009277795,0.0000447604,0.000036519243,0.00009299467],"domain_scores_gemma":[0.9993518,0.00037045457,0.000094513,0.00001630053,0.00006433442,0.0001026753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010200362,0.00084201584,0.00076638587,0.00094992004,0.00050828414,0.0010199333,0.00072528253,0.0010888153,0.0018712014],"category_scores_gemma":[0.001946456,0.0006994735,0.00065295584,0.0007742418,0.00080391695,0.0010758758,0.00066917186,0.00073801103,0.00010618993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002133869,0.000009672556,0.00020894884,0.000005654384,0.000004535615,0.000018448825,0.000005573015,0.9983925,0.00007836681,0.00035549194,0.000039891707,0.00085956353],"study_design_scores_gemma":[0.00001014097,0.000026515132,0.0002065902,0.0000025457452,0.0000047038884,0.000002587764,0.000022875007,0.9989813,0.0001291517,0.00053398253,0.00007642999,0.0000031666361],"about_ca_topic_score_codex":0.061244197,"about_ca_topic_score_gemma":0.067769304,"teacher_disagreement_score":0.061244197,"about_ca_system_score_codex":0.0038229926,"about_ca_system_score_gemma":0.0021802585,"threshold_uncertainty_score":0.12177539},"labels":[],"label_agreement":null},{"id":"W2142957851","doi":"10.1061/41203(425)92","title":"Impact of Pipe Material Selection on Greenhouse Gas Mitigation in Water Networks under Uncertain Discount Rates and Carbon Prices","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":true,"ca_institutions":"Queen's University","funders":"","keywords":"Greenhouse gas; Carbon tax; Carbon price; Environmental science; Capital cost; Electricity; Fossil fuel; Natural resource economics; Environmental engineering; Environmental economics; Waste management; Economics; Engineering","score_opus":0.010474640785699934,"score_gpt":0.20808084339946326,"score_spread":0.19760620261376333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142957851","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":[0.99479693,0.00005637958,0.0036214932,0.000053990716,0.0000024675305,0.000023813587,0.00011228084,0.000019749312,0.0013129121],"genre_scores_gemma":[0.9982874,0.000036289526,0.0013570304,0.000004757418,3.5808227e-7,0.000005167774,0.00004761342,0.0000030812782,0.00025838838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994999,0.00014300305,0.000015481388,0.00006207254,0.00012758863,0.00015184362],"domain_scores_gemma":[0.99768806,0.0015737659,0.00030889822,0.000064860884,0.00027320813,0.00009126096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013837038,0.00047750236,0.00029611177,0.0004349799,0.00042216628,0.0008051597,0.0005055303,0.0003704249,0.00057768443],"category_scores_gemma":[0.0041356804,0.0003265304,0.00035888047,0.0005601085,0.0006160608,0.00070392276,0.00037014304,0.0004658203,0.000034178476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012066817,0.000015581592,0.0066257273,0.000015906104,0.000014541811,0.000043910204,0.00001505104,0.98847556,0.001013669,0.00043189223,0.000039168568,0.0031882916],"study_design_scores_gemma":[0.000023625289,0.00019767182,0.011831441,0.000007930812,0.000039422168,0.000017631053,0.0001665016,0.98156,0.0051700207,0.00068941823,0.00027838312,0.0000178794],"about_ca_topic_score_codex":0.34054357,"about_ca_topic_score_gemma":0.3909571,"teacher_disagreement_score":0.34054357,"about_ca_system_score_codex":0.007505976,"about_ca_system_score_gemma":0.0035814953,"threshold_uncertainty_score":0.6771228},"labels":[],"label_agreement":null},{"id":"W2143193920","doi":"10.1109/cec.2008.4630922","title":"An improved Genetic Algorithm for pump scheduling in water injection systems for oilfield","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Huazhong University of Science and Technology; University of Calgary","keywords":"Centrifugal pump; Genetic algorithm; Computer science; Mathematical optimization; Scheduling (production processes); Plunger pump; Engineering; Plunger; Mathematics; Mechanical engineering","score_opus":0.011852197966491274,"score_gpt":0.20292935348594884,"score_spread":0.19107715551945756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143193920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036815256,0.00026501185,0.9591836,0.00013622867,0.00005422943,0.00007376539,0.000039095696,0.00033672797,0.0030961107],"genre_scores_gemma":[0.50374657,0.0003157457,0.4919216,0.00014503754,0.00004467038,0.00026951396,0.00018624113,0.00008061323,0.003289997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969137,0.0001136754,0.000011262378,0.000046491998,0.0000965404,0.00004057889],"domain_scores_gemma":[0.9996537,0.0002013525,0.00003500991,0.000013753058,0.00008230189,0.000013785466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079035875,0.00069472514,0.0006992762,0.00064043206,0.00039261757,0.0004933383,0.0008183172,0.00095494726,0.0012461042],"category_scores_gemma":[0.0013857108,0.0003482457,0.0004924405,0.00072501745,0.0004450137,0.0005478494,0.00037999664,0.00066270126,0.00018520438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028153474,0.000025190131,0.00025662148,0.000019678226,0.000016580812,0.000024199644,0.00002234188,0.9676934,0.0010375885,0.0031500778,0.00044258172,0.02728365],"study_design_scores_gemma":[0.000013619267,0.000021407446,0.00006062242,0.0000018595521,0.000005330941,0.0000068730337,0.0000022510003,0.9988508,0.00019496378,0.00050575734,0.0003343324,0.0000021672984],"about_ca_topic_score_codex":0.010094617,"about_ca_topic_score_gemma":0.008204477,"teacher_disagreement_score":0.010094617,"about_ca_system_score_codex":0.00093404687,"about_ca_system_score_gemma":0.0017408644,"threshold_uncertainty_score":0.020071685},"labels":[],"label_agreement":null},{"id":"W2143755090","doi":"10.1109/tsp.2002.806996","title":"Algebraic theory of optimal filterbanks","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Water Systems and 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":"Mathematics; Algebraic number; Mathematical optimization; Applied mathematics; Discrete mathematics; Algebra over a field; Pure mathematics; Mathematical analysis","score_opus":0.012716961829077507,"score_gpt":0.19955981582273405,"score_spread":0.18684285399365655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143755090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01775707,0.00049132603,0.9615952,0.0004724951,0.000068834524,0.000032754644,0.00019635289,0.000086275024,0.01929975],"genre_scores_gemma":[0.5806199,0.0023152798,0.3958099,0.0004979201,0.00059581804,0.00029922122,0.00077175215,0.00019129114,0.01889898],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987985,0.000318021,0.00009522968,0.00017987509,0.00048755805,0.00012074319],"domain_scores_gemma":[0.9988167,0.00050634745,0.00017642866,0.000083627536,0.00034239315,0.00007438582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012073496,0.00066322944,0.0005587587,0.0012404462,0.0006132351,0.0019615402,0.0005395881,0.00066774106,0.0042919847],"category_scores_gemma":[0.0030400713,0.0003145156,0.00076659734,0.0009514259,0.0018257491,0.0021381858,0.0010973781,0.0014148153,0.00073199114],"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":[0.000011681018,0.000009117362,0.00010280654,0.00003364536,0.000009449452,0.000027155478,0.00004077733,0.017237548,0.0011570833,0.9700558,0.00063101295,0.010683943],"study_design_scores_gemma":[0.000012072921,0.000066373526,0.00022276993,0.000028561795,0.000015195462,0.00007659206,0.000055295342,0.13874945,0.0013037692,0.8483292,0.011122696,0.000018109331],"about_ca_topic_score_codex":0.00080581027,"about_ca_topic_score_gemma":0.0009346705,"teacher_disagreement_score":0.0042919847,"about_ca_system_score_codex":0.0013774309,"about_ca_system_score_gemma":0.00073870656,"threshold_uncertainty_score":0.014358103},"labels":[],"label_agreement":null},{"id":"W2143834761","doi":"10.1115/imece2013-64850","title":"The Influence of Geometry, Wall Thickness, and Material on the Acoustic Resonance Predictions in Closed-Ended Water-Filled Piping","year":2013,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Manitoba","funders":"","keywords":"Piping; Resonance (particle physics); Acoustics; Acoustic resonance; Materials science; Sound pressure; Acoustic wave; Structural engineering; Mechanics; Physics; Engineering; Mechanical engineering","score_opus":0.005191399160892373,"score_gpt":0.17184468270218398,"score_spread":0.1666532835412916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143834761","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":[0.95733756,0.0001209972,0.04124196,0.00002096239,0.000010093379,0.00001260851,0.000029520636,0.00013402458,0.001092247],"genre_scores_gemma":[0.9959566,0.000045317804,0.0037336615,0.0000020339492,0.0000010092746,0.000005865362,0.000015040191,0.000010227237,0.00023027146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997788,0.000058278263,0.000013199097,0.000040467738,0.000089376146,0.00001985416],"domain_scores_gemma":[0.9994332,0.0003541387,0.000076766555,0.000050457016,0.00007056558,0.00001495084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004150433,0.00036476267,0.00023543778,0.00025154292,0.00018767516,0.00035183132,0.0004848359,0.00041988818,0.0004764927],"category_scores_gemma":[0.0014417074,0.00032999285,0.00022154118,0.00014497666,0.0007104686,0.00047975316,0.00031086677,0.00018532926,0.000116966396],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005307483,0.00009362163,0.003807526,0.00022427477,0.000016971315,0.0006287695,0.00027307033,0.5319516,0.43879625,0.0011760929,0.0001220906,0.02237901],"study_design_scores_gemma":[0.000028062661,0.0006155335,0.0047368277,0.00003240409,0.000031818483,0.0001155978,0.00012504516,0.69509906,0.2980464,0.00058238534,0.000552399,0.00003446487],"about_ca_topic_score_codex":0.0006294109,"about_ca_topic_score_gemma":0.00058747607,"teacher_disagreement_score":0.0006294109,"about_ca_system_score_codex":0.00014661138,"about_ca_system_score_gemma":0.00013635764,"threshold_uncertainty_score":0.0021950006},"labels":[],"label_agreement":null},{"id":"W2143895206","doi":"10.1109/nafips.2004.1336311","title":"A fuzzy expert system for deterioration modeling of buried metallic pipes","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Expert system; Knowledge base; Data mining; Field (mathematics); Fuzzy set; Fuzzy logic; Computer science; Subject-matter expert; Process (computing); Legal expert system; Data modeling; Knowledge-based systems; Set (abstract data type); Engineering; Machine learning; Artificial intelligence; Database; Mathematics","score_opus":0.012127495567547542,"score_gpt":0.21605941412771135,"score_spread":0.2039319185601638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143895206","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":[0.011851298,0.00009709595,0.98527527,0.00007527541,0.000017905619,0.000058907233,0.00009820105,0.00079021265,0.0017358166],"genre_scores_gemma":[0.50417405,0.00024650467,0.4909868,0.000086935506,0.000036156776,0.00029219655,0.00037493778,0.00004870194,0.0037536984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999548,0.00013027115,0.000040275456,0.000115103656,0.00013001064,0.000036280366],"domain_scores_gemma":[0.9995289,0.00020554071,0.00003572886,0.000039733524,0.00016818545,0.0000218413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091258076,0.0005366083,0.00076249003,0.0005306013,0.0005380379,0.0009998702,0.00097416685,0.0012824789,0.002978599],"category_scores_gemma":[0.0023982797,0.00030501143,0.0005266864,0.00035844636,0.00028810857,0.00078009313,0.000492946,0.0007040516,0.000726991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025046364,0.00011272186,0.0012492526,0.00019817839,0.00007198241,0.0004127827,0.0003232276,0.80779415,0.014849138,0.012948571,0.0023190216,0.1594705],"study_design_scores_gemma":[0.000015364329,0.000023229906,0.0001284303,0.0000112059215,0.000012849368,0.00003321928,0.000013147963,0.9950995,0.0012343603,0.001867011,0.0015536518,0.000007993815],"about_ca_topic_score_codex":0.0071227606,"about_ca_topic_score_gemma":0.0059092315,"teacher_disagreement_score":0.0071227606,"about_ca_system_score_codex":0.00057590543,"about_ca_system_score_gemma":0.001005377,"threshold_uncertainty_score":0.0141626},"labels":[],"label_agreement":null},{"id":"W2147057091","doi":"10.5942/jawwa.2013.105.0061","title":"Contaminant intrusion in water distribution systems","year":2013,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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":"University of Toronto; University of British Columbia, Okanagan Campus","funders":"University of British Columbia","keywords":"Intrusion; Transient (computer programming); Dispersion (optics); Environmental science; Water quality; Petroleum engineering; Eulerian path; Lagrangian; Computer science; Engineering; Geology; Mathematics","score_opus":0.002046476443527861,"score_gpt":0.15534181626354038,"score_spread":0.1532953398200125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147057091","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":[0.9339479,0.00025264538,0.06109863,0.00016636134,0.000012915603,0.000043576736,0.00010087131,0.00019417492,0.004182872],"genre_scores_gemma":[0.9975331,0.00008755064,0.0018839826,0.0000070604215,0.000002342592,0.0000092487735,0.000027759303,0.000007983646,0.0004407849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996612,0.00007841918,0.000015954583,0.000061329956,0.0001108648,0.00007227153],"domain_scores_gemma":[0.99966013,0.00014709725,0.000084028376,0.000020423453,0.00006443975,0.000023855902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024470806,0.0003010681,0.00041833564,0.00045214948,0.00049353816,0.00091096567,0.00037240473,0.0005660639,0.0005490158],"category_scores_gemma":[0.0013039713,0.00024647478,0.00036820682,0.00086978456,0.0006187679,0.00091975747,0.00083565584,0.00036384215,0.000048034155],"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":[0.00006828582,0.000038448346,0.0072045433,0.000038122955,0.000025111563,0.00018108798,0.000064506625,0.97446436,0.006008345,0.0028531705,0.00012602303,0.008928092],"study_design_scores_gemma":[0.0000161169,0.00008069447,0.0019135041,0.0000039168285,0.000013586748,0.00005183339,0.00004501847,0.9925592,0.0033735905,0.0014355093,0.00049955596,0.0000075158696],"about_ca_topic_score_codex":0.02835437,"about_ca_topic_score_gemma":0.009978538,"teacher_disagreement_score":0.02835437,"about_ca_system_score_codex":0.0021121115,"about_ca_system_score_gemma":0.0011988693,"threshold_uncertainty_score":0.056378663},"labels":[],"label_agreement":null},{"id":"W2147676699","doi":"10.1061/41024(340)97","title":"Data Mining as a Tool to Identify Contaminant Sources in Water Distribution Systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Guelph","funders":"Canada Research Chairs","keywords":"Robustness (evolution); Computer science; Data mining; Intrusion; Event (particle physics); Backflow; Data modeling; Wireless sensor network; Engineering; Database","score_opus":0.017798505896015816,"score_gpt":0.24671967645991957,"score_spread":0.22892117056390376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147676699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07707091,0.00074410124,0.9156833,0.0010001004,0.000045448898,0.00030572675,0.001294544,0.0022426604,0.0016131847],"genre_scores_gemma":[0.43932804,0.00046876544,0.55833566,0.00011266261,0.00003598709,0.00031533733,0.00087180885,0.000037424725,0.0004942719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911493,0.00035887834,0.00012020793,0.000115714676,0.00025809676,0.000032167514],"domain_scores_gemma":[0.9952356,0.0033900489,0.00046899353,0.00034456942,0.0004734149,0.00008742575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018011685,0.0007560819,0.00078537845,0.0023529967,0.00051600684,0.00089456537,0.0009931102,0.0006663399,0.00050003454],"category_scores_gemma":[0.0069044465,0.00042577786,0.00049251626,0.0017609306,0.000438282,0.0012400912,0.0007193308,0.0006920377,0.00015783579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048752158,0.00043759958,0.026105257,0.0007875143,0.00027163676,0.000674143,0.0003647496,0.4824081,0.0074776625,0.010273409,0.0040758145,0.4666366],"study_design_scores_gemma":[0.0000386707,0.0001216176,0.0025717602,0.000046525016,0.000032346776,0.0002011009,0.00011063487,0.97725874,0.0061578318,0.009850529,0.0035912783,0.000019088147],"about_ca_topic_score_codex":0.0026151026,"about_ca_topic_score_gemma":0.0021070985,"teacher_disagreement_score":0.0026151026,"about_ca_system_score_codex":0.00044204842,"about_ca_system_score_gemma":0.00081003644,"threshold_uncertainty_score":0.009525597},"labels":[],"label_agreement":null},{"id":"W2148988638","doi":"10.1109/pmaps.2010.5528964","title":"Application of continuous probabilistic networks to leak detection in pipe-type cable installations","year":2010,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Probabilistic logic; Leak detection; Leak; Computer science; Continuous variable; Nonlinear system; Quantization (signal processing); Algorithm; Engineering; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.003849553252926304,"score_gpt":0.18142100835760294,"score_spread":0.17757145510467665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148988638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07236097,0.00023565239,0.92574584,0.0000834299,0.00001557299,0.000024454257,0.00007979072,0.00027165283,0.0011826406],"genre_scores_gemma":[0.9196461,0.00017210224,0.07936118,0.0000155429,0.00001746136,0.000028869088,0.00007294582,0.000030165746,0.00065565587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906105,0.0004676049,0.000031034317,0.00018660072,0.00019923456,0.000054376458],"domain_scores_gemma":[0.99674904,0.002475721,0.00026141896,0.00012599365,0.00032837223,0.00005947377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001247524,0.0005730041,0.00044090542,0.00073000474,0.00026319263,0.000628141,0.00064808136,0.000538416,0.00089179474],"category_scores_gemma":[0.004829919,0.00028108942,0.00031570438,0.0008348822,0.0007196125,0.0011844892,0.0007615214,0.000549448,0.00009334938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025655513,0.0000356419,0.002765584,0.00011257265,0.00002742495,0.00012494356,0.000081622165,0.9295828,0.004954423,0.0060757413,0.00027453617,0.05570826],"study_design_scores_gemma":[0.0000046382197,0.000038485192,0.00048261168,0.0000035446326,0.000004594214,0.00002707286,0.000011635633,0.994918,0.0018024666,0.002503545,0.0001956222,0.000007811817],"about_ca_topic_score_codex":0.0028895386,"about_ca_topic_score_gemma":0.0016172526,"teacher_disagreement_score":0.0028895386,"about_ca_system_score_codex":0.0006649211,"about_ca_system_score_gemma":0.0003116279,"threshold_uncertainty_score":0.006597638},"labels":[],"label_agreement":null},{"id":"W2151039274","doi":"10.1061/40753(171)135","title":"Sustainable Infrastructure: Reconstruction of the Little Mountain Reservoir","year":2005,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Capital Regional District","funders":"","keywords":"Demolition; Constructability; Sustainability; Schedule; Metropolitan area; Civil engineering; Geography; Environmental planning; Environmental science; Engineering; Archaeology; Computer science","score_opus":0.0034724094704961174,"score_gpt":0.17138767263977728,"score_spread":0.16791526316928115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151039274","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":[0.82849944,0.0004212481,0.04796292,0.0024791062,0.000040365816,0.00015725363,0.0004837375,0.00016687345,0.11978906],"genre_scores_gemma":[0.9788209,0.00023150054,0.009627417,0.000027757276,0.000003005744,0.000023515948,0.00016485646,0.000020763879,0.011080281],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997564,0.00005065247,0.00000426118,0.000013109997,0.00010033351,0.000075238044],"domain_scores_gemma":[0.9998828,0.00001557992,0.000011046638,0.000009167825,0.000034366705,0.000046970672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022852715,0.00022018483,0.00014651117,0.00061770173,0.0008615124,0.0014673127,0.00060358597,0.00069543155,0.0029650892],"category_scores_gemma":[0.0007018841,0.00019644208,0.00018845555,0.0007985659,0.0004739081,0.00047582504,0.0008706295,0.0004491893,0.00030371753],"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":[0.000239208,0.00015554292,0.032492608,0.00017398414,0.0000397263,0.006447558,0.0013908796,0.6531983,0.012458314,0.104924254,0.008924942,0.17955461],"study_design_scores_gemma":[0.00016230159,0.00095437345,0.055315036,0.0002444994,0.00008227475,0.0033644394,0.017514104,0.7055189,0.01406661,0.046567526,0.15604976,0.00016012718],"about_ca_topic_score_codex":0.10708477,"about_ca_topic_score_gemma":0.3001886,"teacher_disagreement_score":0.10708477,"about_ca_system_score_codex":0.002124287,"about_ca_system_score_gemma":0.0052346713,"threshold_uncertainty_score":0.21292293},"labels":[],"label_agreement":null},{"id":"W2151190411","doi":"10.1109/ccece.1999.808208","title":"Development of an intelligent control system for a municipal water distribution network","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Systems engineering; Expert system; Software engineering; Process (computing); Model-based design; Development (topology); Object-oriented programming; Engineering; Artificial intelligence; Simulation","score_opus":0.0112903789847533,"score_gpt":0.19454324242541693,"score_spread":0.18325286344066363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151190411","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":[0.051415212,0.00015252699,0.9241585,0.0003303996,0.0001066009,0.0005781903,0.00012729921,0.010129592,0.013001767],"genre_scores_gemma":[0.73893434,0.00018835005,0.25052273,0.00014152746,0.000040891657,0.0004817183,0.00031395076,0.00012872243,0.009247709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996605,0.000047066937,0.000020069236,0.00010689245,0.00013460663,0.00003091781],"domain_scores_gemma":[0.99966764,0.00010294349,0.000042057138,0.000031597483,0.00012785026,0.000027868835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065986236,0.00047059916,0.00042490324,0.0004699776,0.0006744607,0.0011244307,0.0007622496,0.0006621763,0.003839229],"category_scores_gemma":[0.0011924846,0.00021755978,0.0002771005,0.00032136485,0.00038961187,0.00058576325,0.00031891037,0.00048919406,0.0006080988],"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":[0.00044327503,0.00034523942,0.0048645283,0.0003919144,0.00008294355,0.00094660354,0.0007287094,0.37634212,0.08078059,0.016360244,0.008434523,0.5102793],"study_design_scores_gemma":[0.000112389745,0.00020792658,0.0013328417,0.000028807452,0.00006281538,0.00014246933,0.000076368524,0.95266503,0.026683643,0.0017971541,0.016860835,0.000029766063],"about_ca_topic_score_codex":0.008966155,"about_ca_topic_score_gemma":0.008427035,"teacher_disagreement_score":0.008966155,"about_ca_system_score_codex":0.00084114465,"about_ca_system_score_gemma":0.001535605,"threshold_uncertainty_score":0.017827928},"labels":[],"label_agreement":null},{"id":"W2152547838","doi":"10.1002/cjce.5450800316","title":"<i>z</i>‐Transform and adaptive signal processing in analysis of tracer data","year":2002,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and 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":"TRACER; A priori and a posteriori; Residence time distribution; Computer science; Flow (mathematics); Interpretation (philosophy); Transfer function; Time domain; Signal processing; Field (mathematics); Data processing; Residence time (fluid dynamics); Algorithm; Data mining; Mathematics; Mechanics; Engineering; Physics; Digital signal processing; Electrical engineering","score_opus":0.017082590896998446,"score_gpt":0.17354452484150096,"score_spread":0.1564619339445025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152547838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019474175,0.00030842327,0.9788497,0.00016718893,0.000043505082,0.0000217393,0.000025116504,0.00026639906,0.00084378803],"genre_scores_gemma":[0.23923677,0.0007860821,0.7564576,0.00011572149,0.00016934417,0.00009555571,0.000117338874,0.00013907465,0.0028825665],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996971,0.0001029371,0.000019815841,0.000055202712,0.000100213,0.000024843615],"domain_scores_gemma":[0.99922216,0.00052739657,0.00005585927,0.000050294755,0.00012461946,0.00001968619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091415754,0.00050926884,0.0003346957,0.0010409193,0.00018457255,0.00073806907,0.00054631603,0.0007355378,0.0017635328],"category_scores_gemma":[0.0018055144,0.00020940516,0.00041940177,0.001237829,0.0011767038,0.0006309352,0.00032902125,0.00052598154,0.0006363759],"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":[0.0006395566,0.0001385247,0.0015793568,0.00032739487,0.0001252554,0.00038747367,0.00015839627,0.23419344,0.16768767,0.05630473,0.0024858918,0.53597224],"study_design_scores_gemma":[0.00001841705,0.00009233517,0.0008939985,0.0000096650265,0.000016948356,0.00012024129,0.000020052983,0.9635381,0.024628075,0.008304335,0.0023376234,0.000020164269],"about_ca_topic_score_codex":0.0021009003,"about_ca_topic_score_gemma":0.0013696091,"teacher_disagreement_score":0.0021009003,"about_ca_system_score_codex":0.00031257767,"about_ca_system_score_gemma":0.0005308287,"threshold_uncertainty_score":0.0058995485},"labels":[],"label_agreement":null},{"id":"W2153020912","doi":"10.1061/(asce)0733-9429(2002)128:6(635)","title":"Observations of Air–Water Interaction in a Rapidly Filling Horizontal Pipe","year":2002,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":65,"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":"Air entrainment; Body orifice; Air water; Drainage; Entrainment (biomusicology); Pipe flow; Orifice plate; Oscillation (cell signaling); Outflow; Airflow; Water flow; Geotechnical engineering; Geology; Environmental science; Mechanics; Engineering; Turbulence; Chemistry; Physics; Acoustics; Mechanical engineering","score_opus":0.015168795830801482,"score_gpt":0.18709647767734638,"score_spread":0.1719276818465449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153020912","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":[0.99807525,0.00003466786,0.0014556126,0.000008995277,0.0000022180525,0.000010300633,0.00005895695,0.00004225104,0.0003116477],"genre_scores_gemma":[0.998585,0.000036165482,0.0010097702,0.000007935665,0.0000045631664,0.000009146761,0.00007146453,0.00000787415,0.00026821633],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998653,0.000015171822,0.0000049654227,0.000029743254,0.0000488629,0.000035853136],"domain_scores_gemma":[0.99974734,0.00008422136,0.00005358308,0.000021066426,0.00004912354,0.000044620174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017464289,0.00016064013,0.00029471537,0.0003600758,0.00040613883,0.00020535143,0.00025119804,0.00035067037,0.0007039221],"category_scores_gemma":[0.00042943374,0.00022882827,0.00019497277,0.00020880763,0.00042742828,0.00030790447,0.00047557725,0.000460672,0.0001275779],"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":[0.00024585228,0.000073833944,0.021772673,0.00006332987,0.000017591303,0.00074891065,0.00071467314,0.00088730874,0.9685172,0.00010357982,0.00014278034,0.0067123594],"study_design_scores_gemma":[0.00008486652,0.0026651532,0.4975666,0.000031465755,0.000069760245,0.0010441006,0.0018408558,0.02214503,0.47117966,0.00031562755,0.0029818097,0.000075064694],"about_ca_topic_score_codex":0.0014433984,"about_ca_topic_score_gemma":0.0019502614,"teacher_disagreement_score":0.0014433984,"about_ca_system_score_codex":0.00011641605,"about_ca_system_score_gemma":0.00019303345,"threshold_uncertainty_score":0.0028699636},"labels":[],"label_agreement":null},{"id":"W2153199019","doi":"10.1111/j.1467-8667.2008.00564.x","title":"Using Evolutionary Optimization Techniques for Scheduling Water Pipe Renewal Considering a Short Planning Horizon","year":2008,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique; Université Laval; National Research Council Canada","funders":"","keywords":"Sorting; Genetic algorithm; Mathematical optimization; Time horizon; Pareto principle; Computer science; Multi-objective optimization; Scheduling (production processes); Function (biology); Operations research; Engineering; Algorithm; Mathematics","score_opus":0.014819172204490146,"score_gpt":0.20383710780225084,"score_spread":0.1890179355977607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153199019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23018892,0.00057223765,0.7600446,0.0003924024,0.000107855485,0.00012665069,0.00006600748,0.0001856512,0.008315606],"genre_scores_gemma":[0.89142805,0.00021313573,0.10489366,0.000073079376,0.000038775648,0.00014015251,0.00007166172,0.00004743177,0.0030940895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997414,0.00008880255,0.000010962969,0.00003351998,0.00006784874,0.000057515466],"domain_scores_gemma":[0.99906045,0.00070283556,0.00007983485,0.000023156686,0.00008688267,0.000046780195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009207026,0.00060949114,0.0010000131,0.00071941095,0.00048215088,0.0007522208,0.0007655202,0.0012457833,0.0021304747],"category_scores_gemma":[0.0027272424,0.0007813635,0.0006151924,0.00092704257,0.0004403384,0.0007944351,0.000554329,0.0007145035,0.00011243109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024054658,0.00001901922,0.00012510906,0.000009850269,0.00001177021,0.000018367762,0.000008829176,0.9915554,0.0002724883,0.0007225395,0.00007014884,0.007162408],"study_design_scores_gemma":[0.000008860679,0.000019522991,0.00005910436,0.0000018083241,0.0000055099854,0.0000030832234,0.000004118206,0.99932337,0.00009076079,0.00040514604,0.00007703252,0.0000016767448],"about_ca_topic_score_codex":0.009421993,"about_ca_topic_score_gemma":0.010486484,"teacher_disagreement_score":0.009421993,"about_ca_system_score_codex":0.0008367399,"about_ca_system_score_gemma":0.0011216577,"threshold_uncertainty_score":0.018734336},"labels":[],"label_agreement":null},{"id":"W2153874890","doi":"10.1061/(asce)1076-0342(2007)13:1(1)","title":"Quantifying Effectiveness of Cathodic Protection in Water Mains: Case Studies","year":2007,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Cathodic protection; Mains electricity; Electricity; Environmental science; Forensic engineering; Engineering; Electrical engineering; Voltage; Anode","score_opus":0.019698550832957795,"score_gpt":0.2638252527567854,"score_spread":0.24412670192382763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153874890","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":[0.9908833,0.00018136927,0.0062094103,0.00007829281,0.000005946449,0.00019183781,0.0001781349,0.000022769214,0.0022489508],"genre_scores_gemma":[0.99431145,0.0001687623,0.0046527716,0.000007700103,0.0000044751323,0.00007610667,0.000087923145,0.000004720096,0.00068604795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99768555,0.0011596669,0.00011740257,0.00024828254,0.00047641998,0.0003126024],"domain_scores_gemma":[0.9833371,0.013236549,0.0011853403,0.00087439234,0.0010906539,0.00027593493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035334912,0.0010523745,0.0007259093,0.0018963126,0.00092653226,0.0010349079,0.0020941822,0.002532,0.0017069003],"category_scores_gemma":[0.009123167,0.0005044201,0.0011795554,0.0024474638,0.0014849296,0.0016273073,0.0011837628,0.0010380355,0.00016595078],"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":[0.0016786322,0.004118672,0.09357183,0.0010546381,0.00046860598,0.004163355,0.0025606365,0.8169802,0.010775635,0.012457327,0.0019452958,0.05022519],"study_design_scores_gemma":[0.0005391559,0.0101110395,0.059012514,0.00018732791,0.00043454184,0.0014192369,0.00813065,0.8469574,0.057268225,0.009511006,0.0061884783,0.00024058128],"about_ca_topic_score_codex":0.012893063,"about_ca_topic_score_gemma":0.018051535,"teacher_disagreement_score":0.012893063,"about_ca_system_score_codex":0.0028334213,"about_ca_system_score_gemma":0.00079257425,"threshold_uncertainty_score":0.025636017},"labels":[],"label_agreement":null},{"id":"W2156269504","doi":"10.1080/00221686.2011.621740","title":"Rigid-plug elastic-water model for transient pipe flow with entrapped air pocket","year":2011,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and 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":"National Natural Science Foundation of China","keywords":"Water hammer; Mechanics; Air water; Transient (computer programming); Interpolation (computer graphics); Spark plug; Water flow; Plug flow; Flow (mathematics); Current (fluid); Water damage; Materials science; Transient flow; Geotechnical engineering; Geology; Computer science; Mechanical engineering; Composite material; Physics; Engineering; Surge","score_opus":0.062189274786443365,"score_gpt":0.2701025961385989,"score_spread":0.2079133213521555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156269504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12518035,0.00028397766,0.8602181,0.000318921,0.000083306826,0.00009707396,0.0002829129,0.0005916534,0.01294364],"genre_scores_gemma":[0.97316056,0.00024473068,0.012145391,0.00005002554,0.000018761904,0.00014491199,0.00014449743,0.00007234466,0.014018777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998739,0.000021534675,0.0000067641645,0.000029257872,0.000038972474,0.000029598632],"domain_scores_gemma":[0.99978215,0.00009343081,0.00003791767,0.000019273533,0.000043314736,0.000023980696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002724046,0.0006623694,0.0007674883,0.0003843126,0.00042875618,0.00068086316,0.0014498677,0.0015777481,0.0021487784],"category_scores_gemma":[0.0007764291,0.0004874318,0.00060040446,0.0003162472,0.0010641183,0.0011813809,0.00076022564,0.0006983415,0.0003774646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051407063,0.000022796936,0.0002965034,0.000037865884,0.000005456284,0.0001227393,0.000048006394,0.9867951,0.0043508364,0.005109075,0.00024262849,0.002917617],"study_design_scores_gemma":[0.0000029917587,0.000007836057,0.000037885166,0.0000011675149,0.0000012117853,0.0000064475043,0.0000060951834,0.9991775,0.00025496693,0.00037738253,0.00012344655,0.0000030601602],"about_ca_topic_score_codex":0.0077269534,"about_ca_topic_score_gemma":0.0038640425,"teacher_disagreement_score":0.0077269534,"about_ca_system_score_codex":0.0005198427,"about_ca_system_score_gemma":0.00092454633,"threshold_uncertainty_score":0.015363991},"labels":[],"label_agreement":null},{"id":"W2156593712","doi":"10.1061/(asce)0733-9496(2008)134:6(556)","title":"The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms","year":2008,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":584,"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":"Battle; Intrusion; Wireless sensor network; Water supply; Computer science; Component (thermodynamics); Engineering; Operations research; Risk analysis (engineering); Environmental engineering; Business","score_opus":0.01654972021817333,"score_gpt":0.18735245182814245,"score_spread":0.17080273160996912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156593712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00986807,0.005200427,0.960067,0.016829085,0.00036429166,0.00007153087,0.00003676486,0.00014240923,0.0074204844],"genre_scores_gemma":[0.34738496,0.017193042,0.6234151,0.0024936113,0.0011069063,0.0003848195,0.00012942601,0.00017479654,0.007717271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973035,0.001698334,0.000100740144,0.00020918064,0.00057389995,0.00011440977],"domain_scores_gemma":[0.9951265,0.0034476977,0.00022223408,0.0003413937,0.00073110615,0.00013113514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056274305,0.00067115726,0.0008726826,0.00045066405,0.0006476553,0.0015896184,0.0011062757,0.0017348891,0.0010919726],"category_scores_gemma":[0.010513181,0.00048338762,0.0004123716,0.0008275077,0.0025089218,0.0040724017,0.0015061114,0.0022594559,0.0003569092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001364055,0.00007272993,0.0011736426,0.000588426,0.00007868571,0.00008781602,0.00038556187,0.3478855,0.0027214475,0.29144716,0.011086371,0.34433618],"study_design_scores_gemma":[0.00005040975,0.00024199356,0.00027229328,0.00015201667,0.000029554041,0.00012359834,0.0003652118,0.6028163,0.0015860484,0.331129,0.06320218,0.000031450152],"about_ca_topic_score_codex":0.0017940934,"about_ca_topic_score_gemma":0.001884338,"teacher_disagreement_score":0.0056274305,"about_ca_system_score_codex":0.0010078475,"about_ca_system_score_gemma":0.0016019805,"threshold_uncertainty_score":0.029761016},"labels":[],"label_agreement":null},{"id":"W2158788929","doi":"10.1631/jzus.a1200227","title":"Two-dimensional pipe leakage through a line crack in water distribution systems","year":2013,"lang":"en","type":"article","venue":"Journal of Zhejiang University. Science A","topic":"Water Systems and 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":"Leakage (economics); Body orifice; Mechanics; Hydraulic head; Water pipe; Materials science; Computation; Geotechnical engineering; Environmental science; Geology; Engineering; Mathematics; Mechanical engineering; Physics","score_opus":0.007634379843409615,"score_gpt":0.18440717863511547,"score_spread":0.17677279879170585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158788929","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":[0.60316813,0.0005967785,0.39023027,0.00030235812,0.000027241025,0.000076732234,0.00023537413,0.00023323494,0.0051298942],"genre_scores_gemma":[0.9917442,0.00012100324,0.0066386717,0.000016984905,0.000004206244,0.00002610709,0.00003714595,0.00001546089,0.0013961139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987113,0.000042846223,0.0000070526876,0.000031107,0.000023834693,0.000024061112],"domain_scores_gemma":[0.9995178,0.00029817497,0.000077825294,0.000015733189,0.00006665142,0.000023903971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029322042,0.000320312,0.0004988073,0.00041521798,0.00035608895,0.00061852444,0.00037200324,0.0010628257,0.0010434242],"category_scores_gemma":[0.0013552113,0.00039859134,0.00041078846,0.000355952,0.0006748263,0.00079474173,0.00053997553,0.0004252785,0.00007335494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049896604,0.000016582162,0.0024234136,0.00005886455,0.0000090718495,0.00015677241,0.00006225803,0.989944,0.0025792613,0.0018741812,0.00010344212,0.0027223222],"study_design_scores_gemma":[0.0000039752026,0.000009816284,0.00028788953,0.0000031543216,0.0000019234706,0.000014678397,0.000013450424,0.9990387,0.00025484592,0.00030579616,0.000062277046,0.0000035395553],"about_ca_topic_score_codex":0.010088734,"about_ca_topic_score_gemma":0.0045761964,"teacher_disagreement_score":0.010088734,"about_ca_system_score_codex":0.00055379485,"about_ca_system_score_gemma":0.000470302,"threshold_uncertainty_score":0.020060062},"labels":[],"label_agreement":null},{"id":"W2159653255","doi":"10.1139/l09-041","title":"Probabilistic design of a riverine early warning source water monitoring systemA paper submitted to the Journal of Environmental Engineering and Science.","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Warning system; Safeguarding; Probabilistic logic; Water source; Early warning system; Water resource management; Environmental engineering; Engineering; Computer science; Telecommunications","score_opus":0.004877079703553081,"score_gpt":0.14541964354737485,"score_spread":0.14054256384382177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159653255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031488713,0.00016652839,0.9631962,0.00024323573,0.00001733618,0.00013002047,0.000116841635,0.00021660297,0.004424484],"genre_scores_gemma":[0.9369768,0.00016880737,0.058147267,0.000049431168,0.000019037394,0.00023418195,0.000087133914,0.00003644979,0.004280913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885035,0.0003308944,0.00003583389,0.00031706784,0.00031113217,0.00015476249],"domain_scores_gemma":[0.9986242,0.0006552332,0.00030757612,0.000040557075,0.00031628157,0.000056101664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013403299,0.00074719265,0.00079015654,0.00038420325,0.00056008634,0.0010990561,0.0010029537,0.0008831103,0.0030898915],"category_scores_gemma":[0.0025578984,0.00078746636,0.00043496955,0.00037479633,0.00073580246,0.0006747613,0.0009606787,0.00057987886,0.00027932276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009137439,0.000021363034,0.00042445475,0.00005464786,0.000021673453,0.00005099656,0.000043865668,0.9773751,0.0035661724,0.004674257,0.0002926053,0.013383487],"study_design_scores_gemma":[0.00001545079,0.00007472088,0.0002037472,0.0000041823637,0.000015086831,0.000013740855,0.000007357882,0.9973847,0.00058337516,0.0011771742,0.00051405653,0.0000063680445],"about_ca_topic_score_codex":0.007284849,"about_ca_topic_score_gemma":0.0067933896,"teacher_disagreement_score":0.007284849,"about_ca_system_score_codex":0.0014722605,"about_ca_system_score_gemma":0.0018592338,"threshold_uncertainty_score":0.014484882},"labels":[],"label_agreement":null},{"id":"W2160673397","doi":"10.1139/cjce-2015-0253","title":"Computational dynamic models and experiments in the fluid–structure interaction of pipe systems","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidade de Lisboa; Civil Engineering Research and Innovation for Sustainability","keywords":"Multiphysics; Fluid–structure interaction; Finite element method; Water hammer; Interaction model; Transient (computer programming); Rheology; Computational fluid dynamics; Structural engineering; Computational model; Experimental data; Mechanics; Computer science; Engineering; Mechanical engineering; Materials science; Simulation; Physics; Mathematics","score_opus":0.012252783674594578,"score_gpt":0.1960522117647333,"score_spread":0.18379942809013872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160673397","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":[0.9466188,0.00022911852,0.043506924,0.00021639053,0.000046382964,0.00016713538,0.00061751984,0.00034146474,0.008256216],"genre_scores_gemma":[0.99074966,0.00013396327,0.0077988734,0.00001557998,0.000007794104,0.00015568316,0.00021856447,0.000030877236,0.0008889634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993617,0.00012833597,0.000042842734,0.00010134779,0.0002630344,0.00010274047],"domain_scores_gemma":[0.99878556,0.0007043825,0.00010590625,0.0001702696,0.00015629019,0.00007759839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000590454,0.0005548603,0.000849115,0.0005534144,0.00070291595,0.0007725088,0.0010687547,0.0011634617,0.0021423337],"category_scores_gemma":[0.0016033761,0.00044778045,0.00049547147,0.0006612852,0.0011211241,0.0008822746,0.0008192725,0.00074235507,0.00027230618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021594611,0.00038031008,0.001814293,0.00014553846,0.000012476036,0.00015639751,0.00022187817,0.9553044,0.034558423,0.0028638088,0.00035357822,0.0039729862],"study_design_scores_gemma":[0.000026654225,0.00018455861,0.001191384,0.000006585107,0.0000046113546,0.000019261679,0.000050433293,0.98901945,0.008775421,0.0003520214,0.00035454961,0.000014966029],"about_ca_topic_score_codex":0.0027280115,"about_ca_topic_score_gemma":0.0014519708,"teacher_disagreement_score":0.0027280115,"about_ca_system_score_codex":0.0006148504,"about_ca_system_score_gemma":0.0006424788,"threshold_uncertainty_score":0.0071668625},"labels":[],"label_agreement":null},{"id":"W2162061043","doi":"10.1109/camsap.2009.5413259","title":"Particle filters with approximation steps","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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","funders":"","keywords":"Particle filter; Approximation error; Parametric statistics; Approximation algorithm; Computer science; Particle (ecology); Approximation theory; Algorithm; Filter (signal processing); Applied mathematics; Mathematical optimization; Mathematics; Mathematical analysis; Statistics","score_opus":0.005187425871052945,"score_gpt":0.15611414977253674,"score_spread":0.1509267239014838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162061043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011479427,0.00015257814,0.9861602,0.00018617636,0.00007587516,0.000043169908,0.000029339259,0.00021733594,0.0016557787],"genre_scores_gemma":[0.48382282,0.00044106005,0.5080857,0.00026702756,0.00012798878,0.00032699775,0.00018497129,0.000096772,0.0066466103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878806,0.00034722278,0.00006999326,0.00020648484,0.0004607838,0.00012740587],"domain_scores_gemma":[0.9947019,0.0037894074,0.0003290648,0.0005405235,0.000540347,0.00009869496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022891576,0.0008631245,0.0011572967,0.00043659736,0.0006295152,0.0013081982,0.0013648364,0.0022247052,0.0021633347],"category_scores_gemma":[0.015652912,0.0006148067,0.00066892005,0.00069406204,0.0011635482,0.0021628034,0.0011598002,0.0018980434,0.0006536408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001291658,0.000040638784,0.00077851757,0.000059807422,0.00004179155,0.000058041187,0.000052995638,0.9363387,0.0013047125,0.02284268,0.0007934074,0.03755957],"study_design_scores_gemma":[0.000013857341,0.0000212677,0.000096757605,0.000004833402,0.000005579955,0.000010042683,0.0000047634844,0.99581534,0.00078721537,0.0025736867,0.0006616901,0.000005055271],"about_ca_topic_score_codex":0.01332538,"about_ca_topic_score_gemma":0.00882263,"teacher_disagreement_score":0.01332538,"about_ca_system_score_codex":0.0011297297,"about_ca_system_score_gemma":0.0018373058,"threshold_uncertainty_score":0.026495636},"labels":[],"label_agreement":null},{"id":"W2162071203","doi":"10.1061/(asce)0733-9496(2007)133:2(137)","title":"Cross Correlation of Demands in Water Distribution Network Design","year":2007,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Queen's University; University of Toronto","funders":"Canadian Water Network","keywords":"Correlation; Unison; Network planning and design; Standard deviation; Econometrics; Statistics; Environmental science; Operations research; Computer science; Simulation; Mathematics; Telecommunications","score_opus":0.009917463506799984,"score_gpt":0.21415451030613716,"score_spread":0.20423704679933719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162071203","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":[0.43696383,0.00026359898,0.5579402,0.00033922106,0.000029509552,0.00006618484,0.00010866768,0.00009406816,0.0041946736],"genre_scores_gemma":[0.9811298,0.000089443514,0.017978588,0.000038144266,0.0000135061155,0.000037994683,0.00007305334,0.00003315444,0.00060627586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99458754,0.0032022682,0.0002640155,0.00060087553,0.0009948055,0.0003505628],"domain_scores_gemma":[0.97383827,0.019729186,0.0024913817,0.0011737082,0.0024046602,0.00036264915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008056449,0.0008318246,0.0008551376,0.00096074503,0.00044229248,0.0013953269,0.00066793134,0.00091796834,0.0011960793],"category_scores_gemma":[0.026094213,0.0012760339,0.0005629473,0.0010967598,0.0008675845,0.0025307862,0.0017872076,0.0011088749,0.00014236952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083649604,0.00003132066,0.0062660463,0.000018494226,0.000037269758,0.00006461006,0.00004877907,0.9826507,0.000566918,0.0042377007,0.000088110515,0.0059064156],"study_design_scores_gemma":[0.0000067293845,0.0000768515,0.0023589844,0.000004409948,0.000011788685,0.000040147577,0.000021956026,0.99356186,0.0007064256,0.0030739591,0.00012497212,0.000011935407],"about_ca_topic_score_codex":0.0023507937,"about_ca_topic_score_gemma":0.002961332,"teacher_disagreement_score":0.008056449,"about_ca_system_score_codex":0.0014023988,"about_ca_system_score_gemma":0.0010282535,"threshold_uncertainty_score":0.04260707},"labels":[],"label_agreement":null},{"id":"W2162651188","doi":"10.1109/ccece.2005.1557424","title":"Optimal scheduling of gas pipeline operation using genetic algorithms","year":2006,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gas compressor; Scheduling (production processes); Computer science; Compressor station; Pipeline (software); Genetic algorithm; Pipeline transport; Mathematical optimization; Artificial neural network; Selection (genetic algorithm); Job shop scheduling; Operating cost; Engineering; Mathematics; Artificial intelligence; Embedded system; Machine learning; Routing (electronic design automation)","score_opus":0.010462085153956044,"score_gpt":0.2038457001788802,"score_spread":0.19338361502492415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162651188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2727909,0.0006156992,0.7119052,0.00046118977,0.000077729346,0.0001724034,0.000089971785,0.00050450803,0.013382368],"genre_scores_gemma":[0.8709353,0.00023641349,0.12677722,0.000048635102,0.000019588631,0.00011145976,0.00008189017,0.00003640639,0.0017529967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967635,0.00013587571,0.000010328586,0.000042666554,0.0000779684,0.000056878187],"domain_scores_gemma":[0.99953973,0.00029773935,0.00005446865,0.000019005,0.00006935979,0.000019803598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072227966,0.00054403197,0.00054951006,0.0008660544,0.00038918716,0.0007070616,0.0005416155,0.0007843008,0.0012185461],"category_scores_gemma":[0.0022136886,0.000436737,0.0004334734,0.00062539615,0.00067736494,0.00052179093,0.0002921854,0.0003792426,0.00011497371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018700459,0.000013916563,0.00011591613,0.0000075746793,0.0000049642426,0.0000111170275,0.000009720609,0.9928687,0.00027065646,0.0011722118,0.00008569083,0.005420827],"study_design_scores_gemma":[0.000011207632,0.000012937581,0.000042780895,0.0000017167066,0.0000025123688,0.0000022328195,0.000005660825,0.998738,0.0001627107,0.0008722481,0.00014620123,0.0000016854735],"about_ca_topic_score_codex":0.016961513,"about_ca_topic_score_gemma":0.011122165,"teacher_disagreement_score":0.016961513,"about_ca_system_score_codex":0.0014216731,"about_ca_system_score_gemma":0.0015938529,"threshold_uncertainty_score":0.03372556},"labels":[],"label_agreement":null},{"id":"W2162815230","doi":"10.14796/jwmm.r241-01","title":"Numeric Modeling of Water Mains Filling Considering Air Pressurization","year":2011,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Cabin pressurization; Mains electricity; Environmental science; Mechanics; Materials science; Engineering; Composite material; Electrical engineering; Physics","score_opus":0.026500999951983524,"score_gpt":0.1848230562226925,"score_spread":0.15832205627070897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162815230","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":[0.5192218,0.00078056986,0.41906774,0.00069958495,0.00015049416,0.00015555104,0.0007307898,0.00084401504,0.058349505],"genre_scores_gemma":[0.97155094,0.00037384193,0.01859568,0.000040018153,0.000030019495,0.00012043683,0.00015959915,0.000066577406,0.0090628425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999031,0.000023329627,0.0000062844756,0.000018478888,0.000025426387,0.000023374701],"domain_scores_gemma":[0.9998385,0.00007643265,0.000026993459,0.000011032104,0.00003146761,0.000015582647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018221748,0.00047448007,0.00066589075,0.00030565736,0.0005273382,0.0009079462,0.0006564095,0.0012685928,0.0019450855],"category_scores_gemma":[0.00061522843,0.00029332333,0.0006778674,0.0003218088,0.00075180765,0.0004887256,0.0005122408,0.00045161575,0.00024945196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001933491,0.000015251111,0.00046142406,0.000025974332,0.0000039854353,0.00007978606,0.000033426084,0.99163276,0.002606897,0.0035332588,0.00012603232,0.0014619762],"study_design_scores_gemma":[0.0000042444653,0.000010078629,0.00016820303,0.000002628759,0.0000023970574,0.000011938914,0.000007731512,0.9986833,0.00028023426,0.00048502427,0.0003415136,0.0000026855298],"about_ca_topic_score_codex":0.010963294,"about_ca_topic_score_gemma":0.0040179687,"teacher_disagreement_score":0.010963294,"about_ca_system_score_codex":0.00078960264,"about_ca_system_score_gemma":0.0010606075,"threshold_uncertainty_score":0.021798968},"labels":[],"label_agreement":null},{"id":"W2163074094","doi":"10.1061/41073(361)146","title":"Selection Method for Rehabilitation of Water Distribution Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Analytic hierarchy process; Computer science; Rehabilitation; Robustness (evolution); Duration (music); Process (computing); Risk analysis (engineering); Reliability engineering; Operations research; Engineering; Business","score_opus":0.004187848909467818,"score_gpt":0.21548883720478093,"score_spread":0.2113009882953131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163074094","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":[0.023477057,0.0002731467,0.97034407,0.00013017852,0.000034659508,0.00033522866,0.00005762436,0.00025568274,0.005092432],"genre_scores_gemma":[0.47570732,0.00040291392,0.51700926,0.000065811466,0.00003914428,0.00077527383,0.00016177853,0.000086897744,0.005751566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980154,0.00088439614,0.00008352021,0.00016478484,0.00071950286,0.00013237476],"domain_scores_gemma":[0.99864215,0.0007226905,0.00017333658,0.000056024506,0.00035013014,0.000055693476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020419357,0.00080270716,0.00081617135,0.0028021631,0.000717408,0.0009339,0.00073648116,0.00063071394,0.005000984],"category_scores_gemma":[0.0032069173,0.00026178767,0.00082994974,0.0018279708,0.000489721,0.0010756851,0.0007024023,0.00041025714,0.00042791283],"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":[0.00018565924,0.00016510008,0.0022294275,0.00039218357,0.00009717997,0.00022385598,0.00022356807,0.5451979,0.0067319577,0.020807046,0.0028868755,0.42085925],"study_design_scores_gemma":[0.000052342562,0.00021044284,0.0010173542,0.000045977064,0.00004987461,0.00012650587,0.0001614357,0.97900224,0.0039163893,0.009335656,0.0060545267,0.000027294442],"about_ca_topic_score_codex":0.0022779494,"about_ca_topic_score_gemma":0.003082351,"teacher_disagreement_score":0.005000984,"about_ca_system_score_codex":0.0011702948,"about_ca_system_score_gemma":0.0012504684,"threshold_uncertainty_score":0.01672995},"labels":[],"label_agreement":null},{"id":"W2165911542","doi":"10.1061/40994(321)93","title":"Water Loss Levels from Transmission Mains in Urban Environments","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Mains electricity; Leakage (economics); Pipeline transport; Electricity; Environmental science; Leak; Water supply; Electric power transmission; Engineering; Environmental engineering; Electrical engineering; Economics","score_opus":0.012068214484753443,"score_gpt":0.16477293003104948,"score_spread":0.15270471554629603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165911542","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":[0.99830604,0.00007893891,0.00077492016,0.0000149363705,8.3363216e-7,0.0000053973304,0.00015557586,0.000026969234,0.0006362812],"genre_scores_gemma":[0.9992106,0.00006980597,0.00019216545,0.0000025537713,6.580574e-7,0.0000036395818,0.00014200153,0.000003623436,0.00037501115],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999637,0.000093150804,0.000012350855,0.000054418444,0.000121064775,0.00008194355],"domain_scores_gemma":[0.99931824,0.00024879276,0.00017156692,0.0000512945,0.00016866862,0.000041486026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039041176,0.00026512638,0.00027176362,0.0006114493,0.0002647889,0.00042577417,0.00018637071,0.00021314487,0.000795662],"category_scores_gemma":[0.0010427202,0.0001639364,0.00020370979,0.0011841203,0.00046074734,0.00043861743,0.00039592112,0.0001243804,0.00013955275],"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":[0.003015535,0.00039278768,0.49842724,0.00028552022,0.00020321102,0.00185032,0.0013903544,0.39666674,0.041878965,0.0014563127,0.002353036,0.052080013],"study_design_scores_gemma":[0.00009130208,0.0018750271,0.8083666,0.000024258396,0.0002026253,0.0006448262,0.0031896883,0.099233575,0.08222193,0.0012380303,0.0028286872,0.00008342845],"about_ca_topic_score_codex":0.027093055,"about_ca_topic_score_gemma":0.03786583,"teacher_disagreement_score":0.027093055,"about_ca_system_score_codex":0.0011551974,"about_ca_system_score_gemma":0.00026921413,"threshold_uncertainty_score":0.053870678},"labels":[],"label_agreement":null},{"id":"W2168388487","doi":"10.1061/9780784413692.136","title":"Tacoma's Pipeline Assessment Project: Replacing the Right Mains at the Right Time","year":2014,"lang":"en","type":"article","venue":"Pipelines 2014","topic":"Water Systems and 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":"Hydrogenics (Canada)","funders":"","keywords":"Mains electricity; Pipeline (software); Prioritization; Capital (architecture); Water supply; Service (business); Electricity; Service life; Computer science; Environmental science; Engineering; Business; Environmental engineering; Electrical engineering; Reliability engineering","score_opus":0.0052553224592673505,"score_gpt":0.21651683171083905,"score_spread":0.2112615092515717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168388487","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":[0.6801058,0.00064230023,0.2231177,0.0029186113,0.00028057586,0.0019361525,0.004559939,0.0019796,0.08445934],"genre_scores_gemma":[0.8758859,0.00048184654,0.09962963,0.00018376246,0.000026249872,0.0004487206,0.0023857239,0.00016343751,0.020794708],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954635,0.00011951769,0.000010968046,0.00006920234,0.00018577297,0.00006817716],"domain_scores_gemma":[0.9991565,0.00014769769,0.000065199754,0.000052793926,0.00044889818,0.00012901984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011356026,0.0006180963,0.00025377117,0.0007835919,0.00082632067,0.00096650835,0.0006326486,0.00059484947,0.003721072],"category_scores_gemma":[0.0020019857,0.00021190272,0.00026301807,0.00081186055,0.00053082564,0.0008215338,0.00076657947,0.0006527991,0.00037346472],"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":[0.0017812565,0.0015224603,0.028013773,0.00042858516,0.000099892,0.00097443303,0.0008652359,0.1925183,0.03222828,0.023247093,0.09657336,0.6217473],"study_design_scores_gemma":[0.00057366735,0.004048291,0.059204914,0.00016961877,0.00021134011,0.00063513906,0.0033860628,0.6614762,0.055020463,0.012992562,0.20211913,0.00016264607],"about_ca_topic_score_codex":0.080101274,"about_ca_topic_score_gemma":0.13608597,"teacher_disagreement_score":0.080101274,"about_ca_system_score_codex":0.0019658678,"about_ca_system_score_gemma":0.006281131,"threshold_uncertainty_score":0.15927011},"labels":[],"label_agreement":null},{"id":"W2168430446","doi":"10.1061/9780784412312.295","title":"A New Optimization Framework That Includes Water Conservation Strategies to Reduce Demand in Water Distribution Networks","year":2012,"lang":"en","type":"article","venue":"World Environmental And Water Resources Congress 2012","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Upgrade; Water conservation; Reuse; Environmental economics; Demand management; Peak demand; Computer science; Water resources; Environmental science; Electricity; Engineering; Waste management; Economics","score_opus":0.007891050717314866,"score_gpt":0.1877782704870081,"score_spread":0.17988721976969324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168430446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048279483,0.0003680548,0.9878996,0.00036189673,0.00008682586,0.000055377568,0.00009371046,0.0000727907,0.006233721],"genre_scores_gemma":[0.42750672,0.0020065657,0.5512947,0.0005524414,0.00041817193,0.0006305226,0.00038365525,0.00024518752,0.016962089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918205,0.00035693633,0.000025244632,0.00014461733,0.00021394981,0.00007724835],"domain_scores_gemma":[0.9996427,0.00018635004,0.000044186516,0.000022332624,0.000073420815,0.000031003532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014871738,0.0017581208,0.0014730575,0.00074348855,0.00047501657,0.0015072089,0.0016919546,0.0015841873,0.0032870453],"category_scores_gemma":[0.0014647599,0.0006046638,0.0012233375,0.0011650998,0.00066568167,0.0022322012,0.0011171321,0.0013555354,0.000328294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008520782,0.000040054365,0.00010714372,0.000054535165,0.000026395537,0.000028667337,0.00001396844,0.972089,0.0004903412,0.017871385,0.00086817256,0.008401771],"study_design_scores_gemma":[0.000008598866,0.000028665865,0.000058112328,0.000008041899,0.000010758192,0.00001211222,0.0000078175235,0.9908744,0.00012373956,0.0070147077,0.0018476789,0.0000052780615],"about_ca_topic_score_codex":0.0045367647,"about_ca_topic_score_gemma":0.0054826615,"teacher_disagreement_score":0.0045367647,"about_ca_system_score_codex":0.0013063406,"about_ca_system_score_gemma":0.0015717206,"threshold_uncertainty_score":0.010996282},"labels":[],"label_agreement":null},{"id":"W2170187758","doi":"10.1061/41203(425)41","title":"An ArcGIS Tool for Rapid Contaminant Source Identification","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Identification (biology); Intrusion; Computer science; Node (physics); Geographic information system; Intrusion detection system; Data mining; ALARM; Real-time computing; Remote sensing; Engineering; Geography; Geology","score_opus":0.017854458543312222,"score_gpt":0.19351412740746848,"score_spread":0.17565966886415627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170187758","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":[0.004143804,0.00028976993,0.6200009,0.0004207239,0.00021938255,0.0005548492,0.13660726,0.21576987,0.021993412],"genre_scores_gemma":[0.03782904,0.00055012695,0.80859464,0.00020790887,0.00007739282,0.0023131496,0.11986196,0.018969264,0.011596513],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992717,0.00013031304,0.000107875276,0.0001415351,0.00029673128,0.00005184706],"domain_scores_gemma":[0.99850684,0.00050230935,0.0001324633,0.00022925332,0.00056085765,0.000068251524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016164628,0.0014631742,0.0011189049,0.004552862,0.0005881217,0.0017479964,0.0022597527,0.0007670248,0.062971815],"category_scores_gemma":[0.0040536523,0.0011976258,0.0011657764,0.0067436993,0.00030711232,0.0021298788,0.0015499686,0.0014480243,0.020974947],"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":[0.00029063772,0.00014817435,0.0045317356,0.0012008196,0.000222016,0.00046800717,0.0007468769,0.038440432,0.003884968,0.023568142,0.67146343,0.25503471],"study_design_scores_gemma":[0.00025112275,0.000051205127,0.0052549513,0.00026782454,0.00007847339,0.0004536979,0.00044905365,0.11031852,0.008252248,0.025076868,0.8493888,0.0001572054],"about_ca_topic_score_codex":0.010588008,"about_ca_topic_score_gemma":0.010155723,"teacher_disagreement_score":0.062971815,"about_ca_system_score_codex":0.00070413016,"about_ca_system_score_gemma":0.0023594822,"threshold_uncertainty_score":0.21066171},"labels":[],"label_agreement":null},{"id":"W2170960572","doi":"10.1061/(asce)wr.1943-5452.0000564","title":"Performance Index for Water Distribution Networks under Multiple Loading Conditions","year":2015,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Redundancy (engineering); Reliability engineering; Computer science; Index (typography); Metric (unit); Efficient energy use; Sensitivity (control systems); Resilience (materials science); Range (aeronautics); Reliability (semiconductor); Performance indicator; Performance metric; Data mining; Engineering","score_opus":0.016949084387885487,"score_gpt":0.211785518583182,"score_spread":0.19483643419529653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170960572","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":[0.49992543,0.0010712933,0.4729299,0.00041829862,0.00011089467,0.00025067033,0.0015664723,0.0011625448,0.022564443],"genre_scores_gemma":[0.9770233,0.00017662859,0.021194624,0.000017135968,0.000031218362,0.00007498058,0.0006586531,0.00004663533,0.0007767146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827003,0.00043570087,0.00014514715,0.0002633896,0.00068034715,0.00020534292],"domain_scores_gemma":[0.99471277,0.0028103623,0.0009925544,0.00040818815,0.0009222651,0.0001538371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026118401,0.0010288355,0.0007002098,0.004610815,0.00047817716,0.001274509,0.00073518563,0.0007687565,0.0016169093],"category_scores_gemma":[0.0081521645,0.00013093986,0.00050201314,0.0028183656,0.000688489,0.0022897185,0.0010636167,0.0004359043,0.0003013613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016814437,0.00008820008,0.012598616,0.00013169729,0.000084706895,0.00014558832,0.00011125329,0.91551584,0.0049701487,0.009243233,0.0013427947,0.055599857],"study_design_scores_gemma":[0.0000070029646,0.00027992454,0.009463284,0.000019962681,0.000030837382,0.0001964664,0.00010705802,0.9764847,0.005036005,0.006751782,0.001584916,0.000038164744],"about_ca_topic_score_codex":0.0018781179,"about_ca_topic_score_gemma":0.0014683099,"teacher_disagreement_score":0.004610815,"about_ca_system_score_codex":0.0017146072,"about_ca_system_score_gemma":0.0005529997,"threshold_uncertainty_score":0.0138129},"labels":[],"label_agreement":null},{"id":"W2171831453","doi":"10.1061/(asce)1084-0699(2007)12:6(706)","title":"Discussion of “Optimal Pumping from Skimming Wells” by S. V. Rao, Sudhir Kumar, Shashank Shekhar, and D. Chakraborty","year":2007,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Water Systems and 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","funders":"","keywords":"Geology","score_opus":0.0024476537203440693,"score_gpt":0.16438327479989445,"score_spread":0.1619356210795504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171831453","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030395921,0.016208831,0.43953243,0.40187967,0.008814884,0.0002054895,0.00059002615,0.00021524499,0.10215748],"genre_scores_gemma":[0.83174455,0.014468957,0.08520152,0.03272817,0.008911728,0.0003316846,0.00017450104,0.00020539358,0.026233483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99847335,0.0007255953,0.0000853091,0.0002409973,0.00034624303,0.00012840166],"domain_scores_gemma":[0.9988318,0.0007715882,0.00007969001,0.000054139953,0.00021284644,0.00004988805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026085628,0.00056673336,0.001114682,0.00043426067,0.0015952482,0.0025878649,0.002014971,0.005401689,0.004948735],"category_scores_gemma":[0.0068818578,0.0005575917,0.0014861185,0.0008900987,0.002594722,0.004205108,0.001737648,0.003639898,0.00050206936],"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":[0.00011126833,0.000028158594,0.000289214,0.0002736531,0.00004164274,0.00042721618,0.00012614465,0.05121842,0.0011412484,0.90276617,0.032695614,0.010881246],"study_design_scores_gemma":[0.000076977594,0.000078640755,0.00045847608,0.00021945695,0.000042860258,0.00024117646,0.00024965513,0.097376786,0.0023900736,0.8457514,0.053059474,0.00005505109],"about_ca_topic_score_codex":0.0022526027,"about_ca_topic_score_gemma":0.0023754565,"teacher_disagreement_score":0.005401689,"about_ca_system_score_codex":0.0018060412,"about_ca_system_score_gemma":0.0022563213,"threshold_uncertainty_score":0.01655519},"labels":[],"label_agreement":null},{"id":"W2172076781","doi":"10.1139/cjce-2014-0441","title":"Optimal design of hydraulic structures with hybrid differential evolution multiple particle swarm optimization","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"Particle swarm optimization; Differential (mechanical device); Geotechnical engineering; Hydraulic structure; Optimal design; Differential evolution; Geology; Engineering; Environmental science; Computer science; Algorithm","score_opus":0.01055850800385725,"score_gpt":0.15915917648901554,"score_spread":0.1486006684851583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172076781","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04930846,0.0005068076,0.9394398,0.0003036339,0.00009868724,0.00008350498,0.00006691078,0.00021178594,0.009980589],"genre_scores_gemma":[0.8963013,0.00026178063,0.09708052,0.00013153921,0.000048095386,0.00036544664,0.00013962178,0.00005894214,0.0056126635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996959,0.00012453925,0.000014851826,0.000047274818,0.00007599774,0.000041417486],"domain_scores_gemma":[0.99940073,0.00038723517,0.00006625937,0.000024976127,0.000086305845,0.000034540764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077227654,0.0008695033,0.001154693,0.00062523165,0.00035872686,0.00096272375,0.0008092067,0.0013950373,0.0016631054],"category_scores_gemma":[0.0016970201,0.0007558344,0.00085009297,0.0005181807,0.0007702928,0.0006493778,0.0011330518,0.0006895485,0.00024216356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001557888,0.000011216381,0.00009269298,0.00001806825,0.0000115124685,0.000016579677,0.0000097945085,0.99535024,0.00023835116,0.00077424844,0.000115454044,0.003346218],"study_design_scores_gemma":[0.0000049752202,0.000011808088,0.000028590091,0.0000016526151,0.0000016028054,0.0000012863535,0.0000024337014,0.99951446,0.000044357657,0.00031223104,0.00007542916,0.0000012340129],"about_ca_topic_score_codex":0.004628912,"about_ca_topic_score_gemma":0.003296,"teacher_disagreement_score":0.004628912,"about_ca_system_score_codex":0.00059817056,"about_ca_system_score_gemma":0.00090345676,"threshold_uncertainty_score":0.00920397},"labels":[],"label_agreement":null},{"id":"W2180706791","doi":"10.14796/jwmm.r227-21","title":"An Efficient Finite-Volume Scheme for Modeling Water Hammer Flows","year":2007,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Finite volume method; Water hammer; Hammer; Volume (thermodynamics); Range (aeronautics); Petroleum engineering; Petroleum; Environmental science; Engineering; Mechanical engineering; Geology; Mechanics; Aerospace engineering; Physics; Thermodynamics","score_opus":0.016924766378436498,"score_gpt":0.21963271659679595,"score_spread":0.20270795021835947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180706791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008085168,0.00024836624,0.98543465,0.0001270852,0.000113671325,0.00010493547,0.00013915154,0.0003502372,0.005396703],"genre_scores_gemma":[0.39196143,0.00049221003,0.59417784,0.00012794972,0.000075244236,0.00078811566,0.00046411017,0.00025451908,0.011658622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967706,0.00009446751,0.000022194341,0.000029033885,0.00013925026,0.00003814533],"domain_scores_gemma":[0.99953794,0.00021254177,0.00004779698,0.000049710285,0.000115323564,0.000036693727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081315654,0.0006859805,0.0010174162,0.00055001315,0.0005849177,0.0008965331,0.0018566737,0.0017164921,0.0037395235],"category_scores_gemma":[0.0017718013,0.0004720069,0.0007744838,0.00069706654,0.0008758732,0.0009129681,0.0010864193,0.0012607708,0.0007646282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050530027,0.000036548772,0.00022162465,0.00006496797,0.000010685934,0.00005024124,0.000049424838,0.9459964,0.003908354,0.030622793,0.0008301161,0.018158235],"study_design_scores_gemma":[0.000007137388,0.000006798434,0.000013586039,0.0000034108798,0.0000011650554,0.000004928262,0.0000021059602,0.9972371,0.0002768444,0.0014163264,0.0010277942,0.000002716907],"about_ca_topic_score_codex":0.007325375,"about_ca_topic_score_gemma":0.004915821,"teacher_disagreement_score":0.007325375,"about_ca_system_score_codex":0.0008703741,"about_ca_system_score_gemma":0.0015260552,"threshold_uncertainty_score":0.014565468},"labels":[],"label_agreement":null},{"id":"W2185024761","doi":"10.1007/978-3-319-01092-2_5","title":"Sensor Placement Under Nodal Demand Uncertainty for Water Distribution Systems","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Water Systems and 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 Guelph; Conestoga College","funders":"","keywords":"Distribution (mathematics); Critical infrastructure; Water supply; Business; Water security; Environmental economics; Environmental planning; Environmental resource management; Computer science; Environmental science; Computer security; Water resources; Economics; Environmental engineering","score_opus":0.012834410628173127,"score_gpt":0.1840038950048541,"score_spread":0.17116948437668097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185024761","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":[0.002082921,0.005066944,0.94699514,0.00061028753,0.0005517537,0.00002355868,0.00015069339,0.00036869128,0.04415004],"genre_scores_gemma":[0.38042316,0.037242137,0.3986135,0.0006149826,0.0020746302,0.00020784514,0.00092038425,0.0010784618,0.17882492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999741,0.000052427225,0.000008695649,0.000054013915,0.00012550842,0.000018294879],"domain_scores_gemma":[0.99985766,0.000066382476,0.000009681967,0.000019580282,0.00004048081,0.000006155477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032336096,0.0010761265,0.00079706754,0.00028318728,0.00022951809,0.00081931544,0.00089150167,0.0008139155,0.005236678],"category_scores_gemma":[0.000746545,0.0005155799,0.000422693,0.0010717348,0.0005921473,0.0011167635,0.00077462586,0.001340577,0.0016621335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035561705,0.000023516664,0.00011600042,0.0002741581,0.000031250576,0.000083104,0.000060307306,0.6654068,0.0035327824,0.070257,0.02819553,0.23198396],"study_design_scores_gemma":[0.000008942339,0.000042402327,0.00022131437,0.000084745676,0.000014311693,0.00011547051,0.000056166347,0.7875894,0.0020854862,0.15793158,0.05182543,0.000024840376],"about_ca_topic_score_codex":0.002222568,"about_ca_topic_score_gemma":0.0030840808,"teacher_disagreement_score":0.005236678,"about_ca_system_score_codex":0.00083917915,"about_ca_system_score_gemma":0.0005002441,"threshold_uncertainty_score":0.01751846},"labels":[],"label_agreement":null},{"id":"W2187075633","doi":"10.1139/er-2013-0034","title":"Application of decision support systems in water management","year":2013,"lang":"en","type":"article","venue":"Environmental Reviews","topic":"Water Systems and Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Calgary","funders":"","keywords":"Direct-sequence spread spectrum; Decision support system; Environmental resource management; Water resources; Computer science; Asset management; Environmental science; Engineering; Risk analysis (engineering); Business; Ecology; Spread spectrum; Data mining; Telecommunications; Biology","score_opus":0.005627227110561772,"score_gpt":0.18126522645721593,"score_spread":0.17563799934665417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187075633","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":[0.01953965,0.16412432,0.6590811,0.015439672,0.0025219766,0.00036564472,0.00036658044,0.0007110929,0.13784999],"genre_scores_gemma":[0.48814076,0.19174643,0.2992149,0.0031612397,0.0030696907,0.0003924399,0.00051174435,0.00010801076,0.013654703],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99710673,0.0014324122,0.00018095259,0.00022340272,0.00094819337,0.0001084072],"domain_scores_gemma":[0.99758995,0.0017086731,0.00011851914,0.00012980102,0.0003829199,0.000070160895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002823661,0.000803361,0.0007274606,0.0016046927,0.0005609016,0.0030966895,0.0009256908,0.0016816597,0.003938253],"category_scores_gemma":[0.0055415966,0.0002484112,0.00054686284,0.0025113786,0.0010616549,0.0019005871,0.0016518869,0.0016275584,0.00145736],"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":[0.00010297354,0.00018381423,0.0020590958,0.002245919,0.00018063873,0.0003058052,0.00044080068,0.07374091,0.0031258613,0.22399236,0.0105441995,0.6830776],"study_design_scores_gemma":[0.00006698465,0.0004423353,0.0015880432,0.0018696293,0.00012075108,0.00035942098,0.00066069124,0.1068904,0.006867878,0.37460297,0.5064105,0.00012041276],"about_ca_topic_score_codex":0.001497354,"about_ca_topic_score_gemma":0.0010704936,"teacher_disagreement_score":0.003938253,"about_ca_system_score_codex":0.0013693178,"about_ca_system_score_gemma":0.0012022085,"threshold_uncertainty_score":0.014933169},"labels":[],"label_agreement":null},{"id":"W2191248515","doi":"10.1007/s12205-015-1163-9","title":"Optimization of reinforced concrete retaining walls via hybrid firefly algorithm with upper bound strategy","year":2015,"lang":"en","type":"article","venue":"KSCE Journal of Civil Engineering","topic":"Water Systems and 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 Saskatchewan","funders":"University of Tehran; National Research Foundation","keywords":"Firefly algorithm; Harmony search; Firefly protocol; Mathematical optimization; Computer science; Optimization algorithm; Upper and lower bounds; Process (computing); Optimization problem; Algorithm; Branch and bound; Hybrid algorithm (constraint satisfaction); Engineering; Mathematics","score_opus":0.008978948313949726,"score_gpt":0.18512727417013178,"score_spread":0.17614832585618206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191248515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049755502,0.00021818548,0.9464184,0.000063304826,0.000023318638,0.00003185335,0.000017218668,0.00017807662,0.0032940062],"genre_scores_gemma":[0.67094105,0.00021169925,0.3238437,0.00007088819,0.000028409317,0.00019426172,0.000056299286,0.00006190028,0.004591867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998233,0.00004548086,0.000006628268,0.000022937907,0.00007197075,0.00002964776],"domain_scores_gemma":[0.9998566,0.000068053705,0.00002836719,0.000012349468,0.000025497127,0.000009159028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004945302,0.0005842513,0.0007723902,0.00041532883,0.00023422357,0.0004456747,0.0007655542,0.00070600177,0.0009321296],"category_scores_gemma":[0.00046499405,0.00030903082,0.000631328,0.00031196736,0.00035675667,0.00041460356,0.0005031056,0.00036685317,0.00017069675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030742456,0.000026928392,0.00024484293,0.000028027494,0.000025290276,0.00002946984,0.000021001837,0.96789026,0.0059407363,0.0035170717,0.00024655482,0.021999134],"study_design_scores_gemma":[0.0000058537057,0.000022718763,0.000045296285,0.0000017562957,0.0000031822917,0.0000055469195,0.0000021937876,0.99875104,0.0007160782,0.0002790715,0.00016478823,0.0000025194295],"about_ca_topic_score_codex":0.0015967503,"about_ca_topic_score_gemma":0.0013176284,"teacher_disagreement_score":0.0015967503,"about_ca_system_score_codex":0.0002886527,"about_ca_system_score_gemma":0.0005613201,"threshold_uncertainty_score":0.003174901},"labels":[],"label_agreement":null},{"id":"W2213062117","doi":"10.14796/jwmm.r235-24","title":"A Network Learning Approach for Asset Management in Water Distribution Infrastructure","year":2009,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Asset management; Asset (computer security); Computer science; Distribution (mathematics); Business; Finance; Computer security; Mathematics","score_opus":0.008537999065967784,"score_gpt":0.1922452154226092,"score_spread":0.18370721635664142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2213062117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027038777,0.00064283377,0.9677243,0.0010261898,0.000074411844,0.00004123498,0.0001090649,0.00013153431,0.0032117388],"genre_scores_gemma":[0.8071099,0.0013642051,0.17673914,0.00028151492,0.00044424518,0.00030088198,0.00035595,0.00009177633,0.0133123025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941933,0.00030321235,0.000022040587,0.00012329732,0.00006989124,0.00006216848],"domain_scores_gemma":[0.9959352,0.003463154,0.00015892912,0.00006287685,0.00026526296,0.00011454159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019945295,0.00083166966,0.0013207934,0.0010120609,0.0007102929,0.0011974974,0.001916054,0.002045397,0.0031548655],"category_scores_gemma":[0.006066641,0.0006355409,0.000669031,0.0013344664,0.0010939128,0.0023519683,0.0012586135,0.0017918922,0.00028173602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016246793,0.000027647666,0.00026630692,0.00001759405,0.000017252827,0.000019221972,0.000017305363,0.98140544,0.00004762945,0.007915412,0.00038042726,0.009869579],"study_design_scores_gemma":[0.0000019943766,0.0000025483373,0.000014957141,0.0000011029875,0.0000015753634,0.0000010669725,0.0000016556827,0.99676406,0.000010730906,0.0031430682,0.000056209956,0.0000010661608],"about_ca_topic_score_codex":0.019402372,"about_ca_topic_score_gemma":0.014908442,"teacher_disagreement_score":0.019402372,"about_ca_system_score_codex":0.0017248442,"about_ca_system_score_gemma":0.0010548437,"threshold_uncertainty_score":0.038578928},"labels":[],"label_agreement":null},{"id":"W2214911374","doi":"10.2166/wpt.2015.109","title":"Pumps: energy efficiency &amp; performance indicators","year":2015,"lang":"en","type":"article","venue":"Water Practice & Technology","topic":"Water Systems and 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":true,"ca_institutions":"HydraTek (Canada)","funders":"","keywords":"Efficient energy use; Normalization (sociology); Work (physics); Context (archaeology); Energy consumption; Environmental economics; Energy conservation; Risk analysis (engineering); Engineering; Computer science; Business; Economics; Mechanical engineering; Electrical engineering; Geography","score_opus":0.010429647398497057,"score_gpt":0.2091313570226332,"score_spread":0.19870170962413614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2214911374","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":[0.070121124,0.017350787,0.43516093,0.008923249,0.000992374,0.0016297165,0.025984742,0.009966476,0.42987058],"genre_scores_gemma":[0.5021458,0.015447053,0.40405932,0.0011648297,0.0005669968,0.0015180482,0.013659301,0.0016045271,0.05983415],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9877703,0.0026971395,0.00075719797,0.0005542534,0.0077766823,0.00044443976],"domain_scores_gemma":[0.9906053,0.003518403,0.0018184735,0.00049181544,0.0032460238,0.0003200342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077189426,0.0016243722,0.0007938312,0.009384772,0.00069665315,0.00635271,0.0009843671,0.0012319149,0.01083338],"category_scores_gemma":[0.01768925,0.0003932161,0.00057395047,0.021290073,0.0014680002,0.0052498146,0.0022366852,0.0017626272,0.006596255],"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":[0.00025453785,0.00048270746,0.03153742,0.0022141011,0.00009258424,0.00011752018,0.001318538,0.014253452,0.004115899,0.0789133,0.07418868,0.7925113],"study_design_scores_gemma":[0.00003385804,0.00067487924,0.08785611,0.001350779,0.0001149525,0.00071316946,0.0041869315,0.024321323,0.018952899,0.038170047,0.8232934,0.00033158757],"about_ca_topic_score_codex":0.0048664245,"about_ca_topic_score_gemma":0.0034008569,"teacher_disagreement_score":0.01083338,"about_ca_system_score_codex":0.0028173272,"about_ca_system_score_gemma":0.0026875534,"threshold_uncertainty_score":0.04082215},"labels":[],"label_agreement":null},{"id":"W2216797510","doi":"","title":"Aging Water Infrastructure, an Emerging Crisis","year":2008,"lang":"en","type":"article","venue":"Proceedings of Water Down Under 2008","topic":"Water Systems and 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":"Breakage; Water infrastructure; Water pipe; Forensic engineering; Environmental science; Engineering; Business; Water supply; Computer science; Environmental engineering; Mechanical engineering","score_opus":0.011206606467447294,"score_gpt":0.19865112966186005,"score_spread":0.18744452319441277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216797510","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":[0.41053686,0.028259784,0.013062817,0.3946594,0.0019978182,0.000052121246,0.0043945634,0.00046310516,0.14657353],"genre_scores_gemma":[0.9412863,0.024011118,0.0039337636,0.010863003,0.00054278196,0.000018995424,0.0011396402,0.000064010535,0.018140484],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970466,0.00003529781,0.000011683399,0.00003755967,0.00013723237,0.0000734573],"domain_scores_gemma":[0.99922585,0.00008439912,0.00009242595,0.000020997388,0.00036838822,0.00020786918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006397655,0.00021127412,0.00028751997,0.00046282078,0.0010020336,0.0025869554,0.00042460384,0.0014946237,0.00570148],"category_scores_gemma":[0.001882521,0.00010715371,0.00010603795,0.0016495938,0.0009818148,0.0031703822,0.0008903294,0.0011769857,0.0005203719],"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":[0.0002430035,0.00014946189,0.04887667,0.00050285173,0.00004188933,0.0025048521,0.0020453152,0.013469677,0.00242994,0.1536087,0.40008765,0.37603998],"study_design_scores_gemma":[0.000029489584,0.00015652103,0.07638818,0.0005050164,0.00004652655,0.0014081184,0.034649193,0.025799258,0.0010524447,0.15362824,0.7062564,0.000080725484],"about_ca_topic_score_codex":0.09495927,"about_ca_topic_score_gemma":0.13699903,"teacher_disagreement_score":0.09495927,"about_ca_system_score_codex":0.0037823443,"about_ca_system_score_gemma":0.0041344254,"threshold_uncertainty_score":0.18881309},"labels":[],"label_agreement":null},{"id":"W2234842157","doi":"10.1061/(asce)cp.1943-5487.0000552","title":"Developed Swarm Optimizer: A New Method for Sizing Optimization of Water Distribution Systems","year":2016,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Water Systems and 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":"Global Institute for Water Security","funders":"","keywords":"Sizing; Mathematical optimization; Computer science; Metaheuristic; Particle swarm optimization; Distribution (mathematics); Engineering; Algorithm; Mathematics","score_opus":0.011543142754610288,"score_gpt":0.22799728304925387,"score_spread":0.21645414029464358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2234842157","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":[0.0032844455,0.00021267145,0.9919993,0.000068655216,0.00006583926,0.000042077336,0.000051274354,0.00033316147,0.003942537],"genre_scores_gemma":[0.17907736,0.0005179785,0.80845326,0.00009336741,0.00008912512,0.00044125583,0.00024207482,0.00026023097,0.010825322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997906,0.00006519287,0.0000107630685,0.000024851506,0.000097330434,0.00001127819],"domain_scores_gemma":[0.9998419,0.000063148895,0.000018631956,0.000016166956,0.000053304553,0.0000069075713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005305935,0.0008854184,0.00069749716,0.00054947444,0.0002549804,0.00060194265,0.0007193845,0.00063828344,0.0032337676],"category_scores_gemma":[0.00077516935,0.00040152523,0.0006234058,0.0005814443,0.0003354929,0.00040177046,0.00039774246,0.0006859782,0.0006867861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036692385,0.000029100567,0.00031426575,0.0001292162,0.00006347292,0.0000720628,0.000040033552,0.8760928,0.0044883993,0.010320847,0.0032298001,0.10518335],"study_design_scores_gemma":[0.000007708241,0.000012007554,0.000046584322,0.000004290123,0.0000037556797,0.000007221368,0.0000016933923,0.99628913,0.00036679552,0.00066903303,0.0025895957,0.000002252751],"about_ca_topic_score_codex":0.0036416037,"about_ca_topic_score_gemma":0.004077522,"teacher_disagreement_score":0.0036416037,"about_ca_system_score_codex":0.00036906486,"about_ca_system_score_gemma":0.00076924526,"threshold_uncertainty_score":0.010818005},"labels":[],"label_agreement":null},{"id":"W2237745738","doi":"10.1371/journal.pntd.0003262.g003","title":"HOSP reservoir competence for WNV.","year":2015,"lang":"en","type":"paratext","venue":"Figshare","topic":"Water Systems and 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":"Computational biology; Biology","score_opus":0.05533616566158093,"score_gpt":0.2500272436534923,"score_spread":0.1946910779919114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2237745738","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50091404,0.0007787231,0.049614325,0.0004112205,0.00017865041,0.00043774152,0.13901244,0.0032298253,0.30542302],"genre_scores_gemma":[0.83916634,0.0002469817,0.026258167,0.00007854226,0.0000086556765,0.00016222098,0.059937086,0.0006332676,0.073508695],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999114,0.000011546059,0.000003967649,0.000027149019,0.00003061939,0.000015316597],"domain_scores_gemma":[0.9998921,0.00002394204,0.000020954516,0.000013835753,0.000026053907,0.000022965536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019077769,0.00027487418,0.0001310423,0.00026581914,0.00013776874,0.00031736775,0.00022857031,0.00011833665,0.041530132],"category_scores_gemma":[0.0004552556,0.00012674827,0.00021375463,0.00020401184,0.00008196331,0.0003323054,0.00020438488,0.00026344848,0.007772166],"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":[0.0016370979,0.00040635598,0.13241686,0.0013658853,0.00027567378,0.00024643328,0.00023249985,0.1047943,0.12823829,0.013709737,0.34776574,0.2689111],"study_design_scores_gemma":[0.00012574518,0.0014259786,0.44533908,0.00016929417,0.00014778512,0.0005287087,0.0006466998,0.16653372,0.09374055,0.004871494,0.28636995,0.0001009706],"about_ca_topic_score_codex":0.019055698,"about_ca_topic_score_gemma":0.04548799,"teacher_disagreement_score":0.041530132,"about_ca_system_score_codex":0.0005871517,"about_ca_system_score_gemma":0.00034571218,"threshold_uncertainty_score":0.13893211},"labels":[],"label_agreement":null},{"id":"W2238355453","doi":"10.2166/hydro.2015.174","title":"A hybrid cuckoo–harmony search algorithm for optimal design of water distribution systems","year":2015,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Global Institute for Water Security, University of Saskatchewan","keywords":"Harmony search; Cuckoo search; Heuristics; Computer science; Mathematical optimization; Meta heuristic; Algorithm; Mathematics; Artificial intelligence","score_opus":0.027251927499551708,"score_gpt":0.22182829170729007,"score_spread":0.19457636420773836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2238355453","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":[0.027736854,0.0008659837,0.96328497,0.00016647814,0.000050931067,0.00015223431,0.00005633567,0.00032603106,0.007360149],"genre_scores_gemma":[0.66208416,0.00049483165,0.33035067,0.00019074994,0.00005375595,0.00069839705,0.0001550548,0.00012679886,0.0058455826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951065,0.00020503167,0.000018452023,0.000057607467,0.00016700444,0.000041242234],"domain_scores_gemma":[0.99953353,0.0002711541,0.000048615067,0.000024904588,0.000100960504,0.000020929328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011193004,0.00094993954,0.0010610956,0.0011310299,0.00046991554,0.0007192728,0.0013627958,0.0012284853,0.0018841182],"category_scores_gemma":[0.0015852962,0.0006001213,0.00061338657,0.0012148393,0.00065641507,0.00057667546,0.00077275466,0.0005089799,0.0002680639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020560874,0.0000123177915,0.00013194162,0.000039246115,0.000025447154,0.000019794596,0.00002332598,0.97989583,0.0005323248,0.0028291894,0.0004117559,0.016058207],"study_design_scores_gemma":[0.000010246694,0.000020390058,0.000044595698,0.000004513663,0.0000038270264,0.00000631669,0.0000044365884,0.9984988,0.00017212392,0.00077412085,0.00045724952,0.0000033424772],"about_ca_topic_score_codex":0.008024775,"about_ca_topic_score_gemma":0.0063084043,"teacher_disagreement_score":0.008024775,"about_ca_system_score_codex":0.00074837165,"about_ca_system_score_gemma":0.0013243153,"threshold_uncertainty_score":0.015956163},"labels":[],"label_agreement":null},{"id":"W2241666343","doi":"10.1002/j.1551-8701.2002.tb01654.x","title":"Walkerton Inquiry Reveals Troubled System","year":2002,"lang":"en","type":"article","venue":"Opflow","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"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":"Tragedy (event); Commission; Business; Political science; Risk analysis (engineering); Environmental planning; Sociology; Environmental science; Social science; Finance","score_opus":0.020140703348858195,"score_gpt":0.18943586716671623,"score_spread":0.16929516381785803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2241666343","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":[0.13290599,0.0047407555,0.003918671,0.13995577,0.0015837784,0.00011220099,0.0024915382,0.00049071683,0.71380067],"genre_scores_gemma":[0.648528,0.004419439,0.0015510318,0.040116332,0.00024654117,0.000035617722,0.00074141694,0.0002709567,0.3040907],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974011,0.0001579877,0.000050561244,0.00018487446,0.0016504221,0.0005550444],"domain_scores_gemma":[0.99765414,0.0003418412,0.0001516964,0.00010939585,0.0015200797,0.00022284937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090986275,0.00031889335,0.00022292483,0.00086457114,0.007531995,0.0037603993,0.00082647265,0.0023326788,0.012901536],"category_scores_gemma":[0.0045207236,0.00030695746,0.00015514945,0.0015053228,0.0036418163,0.0017558174,0.0015516542,0.0032372216,0.0012501588],"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":[0.000116980074,0.000037431466,0.013904406,0.00020147795,0.0000224884,0.003517603,0.021593798,0.0020632257,0.0017553313,0.11506967,0.78427225,0.057445403],"study_design_scores_gemma":[0.00000781289,0.000021226702,0.014299455,0.00016695203,0.000010957166,0.00024148378,0.016091898,0.00080923055,0.0007333847,0.007897689,0.9596746,0.000045201694],"about_ca_topic_score_codex":0.9171833,"about_ca_topic_score_gemma":0.9607587,"teacher_disagreement_score":0.08281672,"about_ca_system_score_codex":0.041828062,"about_ca_system_score_gemma":0.033972327,"threshold_uncertainty_score":0.30348527},"labels":[],"label_agreement":null},{"id":"W2244868365","doi":"10.14796/jwmm.r206-20","title":"Connecting Pipes and Plants: Concurrent Hydrodynamic Simulation of the Hydraulic Performance of a Collection System and a Wastewater Treatment Plant","year":2000,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Wastewater; Sewage treatment; Environmental science; Bar (unit); Downstream (manufacturing); Engineering; Waste management; Environmental engineering; Civil engineering; Operations management; Geography; Meteorology","score_opus":0.008926188026171476,"score_gpt":0.1804759272231128,"score_spread":0.1715497391969413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2244868365","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":[0.9727633,0.000025386402,0.021538746,0.00009023965,0.000020879099,0.00015548224,0.00028602334,0.0002714893,0.0048483917],"genre_scores_gemma":[0.9917846,0.000030751602,0.0059980876,0.000018630953,0.0000044875082,0.00012628468,0.00017822791,0.000026900052,0.0018321243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997348,0.00006873067,0.000015421963,0.0000524254,0.00005115781,0.000077547134],"domain_scores_gemma":[0.9988464,0.00073447276,0.00010903301,0.000050160616,0.00012994702,0.0001299257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006154657,0.0005560213,0.0007994758,0.0005526891,0.00082572363,0.0010731524,0.001103138,0.0017846777,0.003416292],"category_scores_gemma":[0.0020237842,0.000853913,0.00076080905,0.0007891926,0.0010329349,0.00097610115,0.000906497,0.001094313,0.00019409617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009249095,0.00006293539,0.0016601973,0.000013283932,0.0000071138156,0.000066523564,0.000052998683,0.9953968,0.0010393098,0.00044566568,0.000071472285,0.0010912414],"study_design_scores_gemma":[0.000026325994,0.00006343823,0.00037120096,0.0000013922173,0.0000050546146,0.0000053583294,0.000032288488,0.99855024,0.0006916464,0.00016421439,0.00008335754,0.0000055497353],"about_ca_topic_score_codex":0.032186754,"about_ca_topic_score_gemma":0.015697312,"teacher_disagreement_score":0.032186754,"about_ca_system_score_codex":0.0016257882,"about_ca_system_score_gemma":0.0022286302,"threshold_uncertainty_score":0.06399882},"labels":[],"label_agreement":null},{"id":"W2247602019","doi":"10.1139/cjce-2013-0431","title":"Predicting the structural condition of individual sanitary sewer pipes with random forests","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":83,"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 Guelph","funders":"Fonds De La Recherche Scientifique - FNRS; Canada Research Chairs","keywords":"Random forest; Receiver operating characteristic; Cut-off; Set (abstract data type); False positive rate; Cutoff; Upstream (networking); Computer science; Data mining; Engineering; Artificial intelligence; Machine learning; Voltage","score_opus":0.0033379617529677077,"score_gpt":0.14941542049255355,"score_spread":0.14607745873958583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2247602019","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":[0.52928317,0.00037479316,0.46357107,0.0003208258,0.000046460453,0.00017420619,0.0018931277,0.002884347,0.0014520907],"genre_scores_gemma":[0.9298077,0.000065888344,0.06737524,0.000044796532,0.000021558953,0.00006820221,0.0019963642,0.000039668495,0.00058042404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993666,0.00017074191,0.000040328177,0.00021371113,0.00010596796,0.00010265949],"domain_scores_gemma":[0.99811375,0.0011807239,0.00019456407,0.00013397823,0.00031747218,0.000059557355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018179276,0.00074597815,0.00057329115,0.0014957108,0.00031723527,0.00069006125,0.0009721317,0.00082443544,0.00082699175],"category_scores_gemma":[0.0039981073,0.00041807906,0.0011194343,0.00070518546,0.00030722184,0.00060808606,0.00041662593,0.000581986,0.0004274199],"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":[0.00023469079,0.00015747892,0.03086763,0.00006342694,0.000099114244,0.00018316074,0.00006911593,0.876504,0.0041570114,0.00053872634,0.0016589705,0.08546658],"study_design_scores_gemma":[0.0000039316797,0.000019865236,0.002357575,0.000006306777,0.000009130251,0.000020433063,0.000017587492,0.99599576,0.0007960711,0.0006184726,0.00014838309,0.0000064797346],"about_ca_topic_score_codex":0.017102055,"about_ca_topic_score_gemma":0.018722013,"teacher_disagreement_score":0.017102055,"about_ca_system_score_codex":0.0005932347,"about_ca_system_score_gemma":0.0008146321,"threshold_uncertainty_score":0.034005046},"labels":[],"label_agreement":null},{"id":"W2256751671","doi":"","title":"Event-based network rehabilitation planing and asset management","year":2012,"lang":"en","type":"article","venue":"WDSA 2012: 14th Water Distribution Systems Analysis Conference, 24-27 September 2012 in Adelaide, South Australia","topic":"Water Systems and 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":"Rehabilitation; Asset management; Event (particle physics); Schedule; Asset (computer security); Computer science; Risk analysis (engineering); Business; Operations research; Operations management; Engineering; Finance; Computer security","score_opus":0.02066106943727186,"score_gpt":0.2358337376979455,"score_spread":0.21517266826067363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256751671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086810105,0.00021545662,0.90179956,0.0005189581,0.00006134613,0.00031666024,0.0005652125,0.00094157946,0.00877111],"genre_scores_gemma":[0.8884203,0.00022141547,0.10672964,0.0000614763,0.000029699035,0.0002053416,0.00050645036,0.000051050185,0.0037747773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945647,0.00020316313,0.000027907492,0.00011843758,0.00010355283,0.000090558955],"domain_scores_gemma":[0.9993686,0.00033214418,0.00010343142,0.00003519359,0.00010212906,0.000058531285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010051749,0.0007644588,0.0007494811,0.0007472368,0.0004482825,0.0012999406,0.0011469568,0.00081572775,0.004462945],"category_scores_gemma":[0.0018482589,0.00047072544,0.00052219,0.0008418887,0.00044818036,0.0012522056,0.0007321195,0.00085082836,0.00024538164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022688648,0.000022376807,0.00029567836,0.000010313568,0.000009082061,0.000013588693,0.000007711735,0.9923558,0.00011017748,0.0010390707,0.00022241547,0.0058909734],"study_design_scores_gemma":[0.000003332707,0.0000067040814,0.000086834705,0.0000011673001,0.000002530305,0.0000021429148,0.000008317854,0.9988593,0.00008844406,0.00077511853,0.00016462435,0.0000015044741],"about_ca_topic_score_codex":0.031211833,"about_ca_topic_score_gemma":0.03306424,"teacher_disagreement_score":0.031211833,"about_ca_system_score_codex":0.0017459897,"about_ca_system_score_gemma":0.0015319239,"threshold_uncertainty_score":0.062060297},"labels":[],"label_agreement":null},{"id":"W2257320654","doi":"10.14796/jwmm.r225-24","title":"Assessment of Unaccounted-for Water in Municipal Water Networks Using GIS and Modeling","year":2006,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Environmental science; Water resource management; Environmental planning","score_opus":0.01967087746942647,"score_gpt":0.2383086618676089,"score_spread":0.21863778439818243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2257320654","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":[0.8031688,0.0008446594,0.1801868,0.0004313514,0.000019132369,0.00011454182,0.0018216695,0.00041471186,0.0129983835],"genre_scores_gemma":[0.98304784,0.0004221216,0.015494298,0.0000051058746,0.000004034133,0.0000395904,0.000304328,0.000020478596,0.0006622463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994019,0.0002658882,0.000029913708,0.000054009295,0.00020808572,0.00004017651],"domain_scores_gemma":[0.99886143,0.0007500985,0.000125165,0.00007242157,0.00016728847,0.000023657252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007082823,0.0005939034,0.00035593606,0.001507422,0.00041398904,0.0016624406,0.0006140837,0.0005147701,0.0008090933],"category_scores_gemma":[0.0032654265,0.00037219268,0.0005754026,0.0027188396,0.00042833155,0.0024273505,0.0005218392,0.0002555168,0.000088259316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035670142,0.00002759226,0.020731986,0.00007504832,0.0000651752,0.00012583654,0.00011633893,0.9498678,0.0007301939,0.011330631,0.00022271123,0.016671045],"study_design_scores_gemma":[0.000003067175,0.00002424162,0.004843391,0.00001563878,0.000020262303,0.000037463713,0.00018581866,0.98823756,0.0010340128,0.0043526343,0.0012352826,0.000010596249],"about_ca_topic_score_codex":0.03297848,"about_ca_topic_score_gemma":0.027234,"teacher_disagreement_score":0.03297848,"about_ca_system_score_codex":0.0027263188,"about_ca_system_score_gemma":0.0016265134,"threshold_uncertainty_score":0.06557304},"labels":[],"label_agreement":null},{"id":"W2259935044","doi":"10.2166/hydro.2013.038","title":"Analytical and experimental investigation of chlorine decay in water supply systems under unsteady hydraulic conditions","year":2013,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"Engineering and Physical Sciences Research Council","keywords":"Water quality; Environmental science; Water supply; Mechanics; Environmental engineering; Physics","score_opus":0.009227684895850036,"score_gpt":0.20439720792803576,"score_spread":0.1951695230321857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259935044","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":[0.977749,0.00031183346,0.020429133,0.000083999614,0.000018812938,0.000048961974,0.00026163596,0.00011811022,0.0009785874],"genre_scores_gemma":[0.99585694,0.00018658109,0.0033457766,0.00001754196,0.0000029859023,0.000017123235,0.00008218377,0.000007903215,0.0004830876],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994641,0.00006136169,0.000026182708,0.0000926388,0.00029573933,0.00005999801],"domain_scores_gemma":[0.99836415,0.00076033344,0.00020302531,0.00013653384,0.00049839803,0.000037538648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074918225,0.00022226607,0.00035489586,0.00030953152,0.00034988427,0.0003289306,0.00048501528,0.0005865124,0.0008940729],"category_scores_gemma":[0.0022918156,0.00017248848,0.00024543732,0.000419076,0.00074309285,0.0005010675,0.00035765296,0.00057615084,0.00013870515],"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":[0.00026234178,0.00017304841,0.0045773704,0.00041085886,0.000020157444,0.0001479594,0.0004410391,0.025722947,0.9565159,0.0007326964,0.00022822837,0.010767398],"study_design_scores_gemma":[0.000025245174,0.0010414097,0.005826256,0.000016075099,0.000017015014,0.00009350095,0.00027579733,0.098516844,0.89291006,0.00020859849,0.0010248262,0.000044351636],"about_ca_topic_score_codex":0.004014184,"about_ca_topic_score_gemma":0.0038368024,"teacher_disagreement_score":0.004014184,"about_ca_system_score_codex":0.00080183864,"about_ca_system_score_gemma":0.00055332767,"threshold_uncertainty_score":0.007981658},"labels":[],"label_agreement":null},{"id":"W2268718671","doi":"10.14796/jwmm.r246-22","title":"Lackawanna WWTP Elimination - A study of Potential Impacts on Smokes Creek and the Buffalo Sewer System","year":2013,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Conestoga College","funders":"University at Buffalo","keywords":"Environmental science; Wastewater; Combined sewer; Sanitary sewer; Environmental engineering; Marine engineering; Engineering; Civil engineering; Hydrology (agriculture); Geotechnical engineering; Ecology; Biology","score_opus":0.008519157123238455,"score_gpt":0.1848021795095289,"score_spread":0.17628302238629046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2268718671","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":[0.99902976,0.0000049137716,0.00014091571,0.0000085933325,8.361856e-7,0.000016051994,0.000033484495,0.0000030433293,0.00076249253],"genre_scores_gemma":[0.99666446,0.000027962311,0.0005578318,0.000008249894,8.292073e-7,0.000030122914,0.0001263026,0.0000034568761,0.0025808862],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977094,0.00004161429,0.0000087561675,0.000029653645,0.00008842185,0.000060635397],"domain_scores_gemma":[0.99982834,0.000043618064,0.000025769828,0.000009399375,0.000060154765,0.00003268946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026654292,0.00019521831,0.00039334584,0.0002538394,0.0011761597,0.00070652476,0.00032421353,0.0003318745,0.0011437873],"category_scores_gemma":[0.0003177065,0.00020137345,0.00045874936,0.00036834166,0.000313028,0.00040591825,0.00034510955,0.00030542898,0.0001492364],"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":[0.0023271139,0.0031861342,0.5915055,0.00028559298,0.00029458984,0.003642738,0.0015587563,0.11348941,0.23576006,0.0019463524,0.0012053477,0.044798348],"study_design_scores_gemma":[0.00015611915,0.0024223528,0.8059874,0.000014560753,0.00017665463,0.00018811818,0.0032089965,0.14102636,0.043487854,0.00028133974,0.0030038203,0.000046471618],"about_ca_topic_score_codex":0.1917526,"about_ca_topic_score_gemma":0.420906,"teacher_disagreement_score":0.1917526,"about_ca_system_score_codex":0.00252032,"about_ca_system_score_gemma":0.0027658606,"threshold_uncertainty_score":0.38127297},"labels":[],"label_agreement":null},{"id":"W2269158034","doi":"10.4236/wjet.2015.34017","title":"Conjunctive Use of Engineering and Optimization in Water Distribution System Design","year":2015,"lang":"en","type":"article","venue":"World Journal of Engineering and Technology","topic":"Water Systems and 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":"Metric (unit); Genetic algorithm; Engineering design process; Computer science; Process (computing); Reliability (semiconductor); Optimization problem; Field (mathematics); Multi-objective optimization; Engineering optimization; Network planning and design; Mathematical optimization; Operations research; Reliability engineering; Industrial engineering; Engineering; Operations management; Mathematics; Machine learning; Algorithm","score_opus":0.01100714786063129,"score_gpt":0.16961084882225913,"score_spread":0.15860370096162785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269158034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00911711,0.0006973288,0.9734554,0.00073532655,0.00006963303,0.00017675696,0.00002008168,0.00008631428,0.015641972],"genre_scores_gemma":[0.27778503,0.0015007446,0.71729535,0.00034095347,0.00008533197,0.00042486584,0.00003266338,0.00006171153,0.00247328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98900676,0.0069681695,0.0005934619,0.000732508,0.002446642,0.00025240937],"domain_scores_gemma":[0.9921112,0.0059133396,0.00082578365,0.00046508224,0.0005839836,0.000100525795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007971985,0.001984928,0.0013352005,0.0015959677,0.0012418802,0.003285549,0.0017506434,0.0012912048,0.0028153881],"category_scores_gemma":[0.012842512,0.0011017933,0.0012615193,0.002407047,0.0036968004,0.003018809,0.0029137656,0.001932614,0.00043180396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012773105,0.00026273474,0.0015543726,0.00075205846,0.00022584423,0.00038260067,0.0004048237,0.60923344,0.003834112,0.24379353,0.0008734179,0.13855542],"study_design_scores_gemma":[0.000047802805,0.00033241967,0.00059925317,0.00014825497,0.00009509897,0.0001295976,0.00018828071,0.8641653,0.0026777023,0.1229675,0.008589416,0.00005929523],"about_ca_topic_score_codex":0.004583216,"about_ca_topic_score_gemma":0.009746701,"teacher_disagreement_score":0.007971985,"about_ca_system_score_codex":0.0022940603,"about_ca_system_score_gemma":0.0033487284,"threshold_uncertainty_score":0.04216039},"labels":[],"label_agreement":null},{"id":"W2269863802","doi":"10.1139/cjce-2015-0374","title":"Bayesian model averaging for the prediction of water main failure for small to large Canadian municipalities","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":22,"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 British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Bayesian probability; Mains electricity; Regression analysis; Bayesian network; Predictive modelling; Computer science; Econometrics; Engineering; Reliability engineering; Machine learning; Mathematics; Artificial intelligence","score_opus":0.017859055884679637,"score_gpt":0.1740922469637853,"score_spread":0.15623319107910566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269863802","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":[0.6123962,0.0008330345,0.3811066,0.0005999957,0.00003947769,0.00012251969,0.000857255,0.0009069744,0.0031379745],"genre_scores_gemma":[0.9754151,0.00024365254,0.022518376,0.00003285218,0.000016760572,0.0000467547,0.0005568687,0.000033474167,0.0011361301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931765,0.0002502031,0.000026902522,0.00014097273,0.0001481572,0.00011612666],"domain_scores_gemma":[0.99813986,0.0011410343,0.00015505867,0.00009530493,0.00038527852,0.00008348793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022259874,0.00076509616,0.000877574,0.0008999244,0.0007718991,0.00068735867,0.0013384707,0.0005044112,0.0011246128],"category_scores_gemma":[0.006530077,0.00044232092,0.00065550423,0.00095327874,0.00043978632,0.00058693794,0.0006492355,0.0007005939,0.00014902992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057663754,0.000029136472,0.0073970347,0.000023975466,0.00005023684,0.00004142439,0.000053161923,0.96509904,0.00038347725,0.002086693,0.0006077669,0.024170378],"study_design_scores_gemma":[0.0000017917315,0.0000054199654,0.0014869958,0.0000014097246,0.0000053149865,0.000002250376,0.000010063522,0.99785125,0.00006077531,0.0004593191,0.00011159543,0.000003845149],"about_ca_topic_score_codex":0.72017926,"about_ca_topic_score_gemma":0.6060582,"teacher_disagreement_score":0.27982074,"about_ca_system_score_codex":0.0028734691,"about_ca_system_score_gemma":0.005717606,"threshold_uncertainty_score":0.56293726},"labels":[],"label_agreement":null},{"id":"W2271243649","doi":"10.2166/aqua.2014.050","title":"Tracer study to verify hydraulic limits and determine water residence times in a distribution system: Part I","year":2014,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":true,"ca_institutions":"Université Laval; Roche (Canada)","funders":"","keywords":"Icon; Residence; Citation; Art history; Library science; Computer science; Art; Sociology; Demography","score_opus":0.01719988231685385,"score_gpt":0.25406462441439287,"score_spread":0.23686474209753902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2271243649","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":[0.896151,0.005659134,0.05797753,0.0006709898,0.00028727658,0.0035252937,0.0058811377,0.0016769515,0.028170766],"genre_scores_gemma":[0.96078795,0.0023515462,0.019230083,0.00030779385,0.00003107905,0.00075394794,0.002225931,0.00018800335,0.014123676],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99919075,0.00015305696,0.00005940016,0.00016452576,0.0003139522,0.00011825927],"domain_scores_gemma":[0.99867934,0.00037965045,0.00014667037,0.00009968873,0.0005950703,0.00009962353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016066702,0.000538931,0.00085208873,0.00118703,0.0011328888,0.0012516249,0.0008889883,0.001123665,0.0037052948],"category_scores_gemma":[0.0024580774,0.00031884774,0.00073826284,0.0015724405,0.0006428828,0.001137533,0.00057582883,0.0007775124,0.0009000984],"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":[0.004853951,0.001932276,0.1363657,0.002374889,0.0003040623,0.0017942971,0.0026221073,0.026852312,0.5903587,0.004101523,0.015979253,0.21246095],"study_design_scores_gemma":[0.001296485,0.017839817,0.16651589,0.0006179727,0.0007834144,0.001624333,0.004349995,0.04952793,0.6291878,0.0034766104,0.1242988,0.00048087],"about_ca_topic_score_codex":0.05022971,"about_ca_topic_score_gemma":0.04121638,"teacher_disagreement_score":0.05022971,"about_ca_system_score_codex":0.0035060449,"about_ca_system_score_gemma":0.0037861245,"threshold_uncertainty_score":0.099874675},"labels":[],"label_agreement":null},{"id":"W2276327515","doi":"10.14796/jwmm.r235-09","title":"A Multi-Objective Optimization Decision Support Model for Renewal Planning of Sewer Networks","year":2009,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Decision support system; Operations research; Decision model; Engineering; Artificial intelligence; Machine learning","score_opus":0.02067233455560273,"score_gpt":0.24019466877866055,"score_spread":0.21952233422305784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2276327515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03911924,0.00048718302,0.94999504,0.00035092907,0.00007832919,0.00016947769,0.000502942,0.00036361255,0.008933219],"genre_scores_gemma":[0.69667804,0.0012585376,0.28323627,0.00010967496,0.000066314424,0.0010666448,0.0007540747,0.00009720853,0.016733276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996464,0.00010384847,0.000020248146,0.00006334305,0.000112288995,0.00005388533],"domain_scores_gemma":[0.99970466,0.00013858017,0.000040220813,0.000011222633,0.00008293692,0.000022384502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072774687,0.000895587,0.0014333096,0.0005440183,0.0006458665,0.0015799415,0.0015525883,0.0015884504,0.0031260771],"category_scores_gemma":[0.0010191925,0.0007267645,0.0010563255,0.0010835959,0.0005757243,0.000963741,0.0008505025,0.0015686218,0.0003792549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064440724,0.000008593297,0.00008212996,0.00001557347,0.000008766971,0.000017797542,0.000008939254,0.9945834,0.00017964838,0.0020253267,0.00009977601,0.0029635993],"study_design_scores_gemma":[0.0000038367125,0.000008077802,0.000043971828,0.0000025343795,0.0000043929813,0.0000035109979,0.0000037991842,0.9989003,0.00007980439,0.0005316182,0.000415837,0.0000021876092],"about_ca_topic_score_codex":0.019966347,"about_ca_topic_score_gemma":0.018983493,"teacher_disagreement_score":0.019966347,"about_ca_system_score_codex":0.0018524941,"about_ca_system_score_gemma":0.0026825569,"threshold_uncertainty_score":0.03970021},"labels":[],"label_agreement":null},{"id":"W2277841867","doi":"10.82308/31084","title":"Reliability analysis of spillway gate systems","year":2014,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Water Systems and 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":"U.S. Army Corps of Engineers; Hydro-Québec","keywords":"Spillway; Reliability (semiconductor); Reliability engineering; Spare part; Engineering; Service (business); Computer science; Operations management; Geotechnical engineering","score_opus":0.008488116451660643,"score_gpt":0.1859405748343593,"score_spread":0.17745245838269866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277841867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26635423,0.0007279103,0.7257898,0.00011138802,0.000019824962,0.0000882324,0.0003439284,0.0006997296,0.0058649667],"genre_scores_gemma":[0.97455764,0.0002909441,0.02330107,0.000010952589,0.000009021554,0.000031834163,0.00022863464,0.000037584006,0.001532377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995788,0.000093116425,0.00002129602,0.00006541644,0.00020040829,0.00004092665],"domain_scores_gemma":[0.9993505,0.00028872435,0.0001227783,0.00007003494,0.000152662,0.000015362906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048349865,0.0005281576,0.0003083475,0.00076364406,0.00020601547,0.0005870238,0.00048424336,0.0002595549,0.0012736747],"category_scores_gemma":[0.0018830595,0.00017447212,0.00040451525,0.0004764212,0.0003161652,0.0007413402,0.00037727,0.00032763302,0.00015241897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049588794,0.000014582402,0.0030031612,0.00009740743,0.00002663329,0.0001299791,0.000083141495,0.9509032,0.012636633,0.0057193236,0.0002655438,0.027070861],"study_design_scores_gemma":[0.0000022139034,0.000060886472,0.0020051037,0.0000084322155,0.000008063383,0.00005306846,0.000025279533,0.99156207,0.0029922565,0.0022644033,0.0010117559,0.000006472205],"about_ca_topic_score_codex":0.004889515,"about_ca_topic_score_gemma":0.0034253646,"teacher_disagreement_score":0.004889515,"about_ca_system_score_codex":0.0005907645,"about_ca_system_score_gemma":0.0005396082,"threshold_uncertainty_score":0.009722114},"labels":[],"label_agreement":null},{"id":"W2279707523","doi":"10.1088/1742-6596/656/1/012056","title":"Francis turbine draft tube modelling for prediction of pressure fluctuations on prototype","year":2015,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Water Systems and 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":"BC Hydro; European Commission","keywords":"Draft tube; Turbine; Francis turbine; Computer science; Scale (ratio); Mechanical engineering; Hull; Hyperbole; Marine engineering; Engineering; Physics","score_opus":0.038776657606317315,"score_gpt":0.22266075863570917,"score_spread":0.18388410102939184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279707523","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":[0.67009026,0.0002443752,0.322774,0.00016462088,0.00005437387,0.000096032214,0.0003915595,0.0009834805,0.00520126],"genre_scores_gemma":[0.99007404,0.00006968345,0.008665028,0.0000061444807,0.0000049548607,0.000046150853,0.00013314947,0.000033592336,0.0009673173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998733,0.00004011508,0.000006346249,0.000020101515,0.000047524438,0.000012658174],"domain_scores_gemma":[0.9996568,0.00019162048,0.000039204762,0.00003067551,0.000066876986,0.0000149033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023126575,0.00045713678,0.0005511448,0.00021809478,0.00021313412,0.00053613534,0.0006394501,0.00091963424,0.0011482215],"category_scores_gemma":[0.001173152,0.00019478657,0.00046241004,0.00021580761,0.00038637785,0.00046450517,0.00028286348,0.0006228719,0.00022247639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086545006,0.000023697188,0.00088680966,0.00006081222,0.0000091000375,0.00009221054,0.000046274312,0.9785144,0.012297969,0.0007767583,0.0002976479,0.0069076936],"study_design_scores_gemma":[0.0000027025644,0.000031676147,0.0003946889,0.000002035044,0.000002031445,0.000007398959,0.00000656498,0.9977202,0.0016109488,0.00009649929,0.00012094342,0.0000042374895],"about_ca_topic_score_codex":0.0066436897,"about_ca_topic_score_gemma":0.0034808614,"teacher_disagreement_score":0.0066436897,"about_ca_system_score_codex":0.00030197276,"about_ca_system_score_gemma":0.00048247614,"threshold_uncertainty_score":0.013209999},"labels":[],"label_agreement":null},{"id":"W2280098121","doi":"10.2166/hydro.2015.144","title":"Water distribution network sectorisation using graph theory and many-objective optimisation","year":2015,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"HydraTek (Canada)","funders":"Irish Research Council; Science Foundation Ireland","keywords":"Lexicographical order; Partition (number theory); Mathematical optimization; Computer science; Graph partition; Heuristic; Graph; Graph theory; Mathematics; Theoretical computer science","score_opus":0.011566432699747449,"score_gpt":0.20052547811761165,"score_spread":0.1889590454178642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280098121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0151890805,0.00022227266,0.9793911,0.00012567452,0.000017078833,0.00009045895,0.000055361394,0.00011280947,0.0047962475],"genre_scores_gemma":[0.46400273,0.0006789563,0.53077024,0.00008670077,0.000028154383,0.00034187146,0.00025565512,0.00014269943,0.003692934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993944,0.00031155584,0.000019290916,0.000079025725,0.00013914744,0.000056571796],"domain_scores_gemma":[0.99929917,0.00048752132,0.00006104993,0.000038852944,0.00008702178,0.000026351821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095582113,0.0010743636,0.0009726431,0.0014197322,0.00052395667,0.0012140232,0.00077582186,0.0008230912,0.002641531],"category_scores_gemma":[0.0020412703,0.0005535012,0.000941547,0.0014258919,0.00095456117,0.0014673119,0.0011281703,0.0008479717,0.00022855961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009486248,0.000009089306,0.00011176397,0.000037286485,0.000010834565,0.000011446594,0.00001375904,0.98574114,0.00035013506,0.0048145778,0.00012575457,0.0087646535],"study_design_scores_gemma":[0.000005465146,0.000011376892,0.00005165646,0.0000054121074,0.0000040334066,0.0000057113552,0.000015928443,0.99137914,0.00022939769,0.007889785,0.00039929966,0.0000028796826],"about_ca_topic_score_codex":0.006535372,"about_ca_topic_score_gemma":0.007834715,"teacher_disagreement_score":0.006535372,"about_ca_system_score_codex":0.001590667,"about_ca_system_score_gemma":0.0015438418,"threshold_uncertainty_score":0.012994707},"labels":[],"label_agreement":null},{"id":"W2281262592","doi":"10.1115/1.4032781","title":"A Study of Acoustic Wave Resonance in Water-Filled Tubes With Different Wall Thicknesses and Materials","year":2016,"lang":"en","type":"article","venue":"Journal of Nuclear Engineering and Radiation Science","topic":"Water Systems and 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":"Computational fluid dynamics; Turbulence; Mechanics; Amplitude; Resonance (particle physics); Acoustics; Acoustic resonance; Materials science; Range (aeronautics); Tube (container); Flow (mathematics); Physics; Optics; Composite material","score_opus":0.007276629768251962,"score_gpt":0.18501748689601052,"score_spread":0.17774085712775856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281262592","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":[0.99144465,0.00016725673,0.0075565157,0.000020279167,0.000009272172,0.00001610095,0.00006424927,0.00009668405,0.0006251343],"genre_scores_gemma":[0.99480283,0.00008456777,0.004534105,0.000007955101,0.0000032936061,0.000019420115,0.00006964155,0.000019507954,0.00045870413],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969995,0.00005130269,0.000017430291,0.00006406229,0.0001284622,0.00003887635],"domain_scores_gemma":[0.9986268,0.0008725611,0.00019898717,0.00008956328,0.0001652619,0.000046739835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004979069,0.00039148476,0.00053133664,0.00046906905,0.00034644658,0.00034269178,0.00047051732,0.00062413147,0.0007801162],"category_scores_gemma":[0.0014202759,0.00029825006,0.00025250865,0.00033008348,0.0008445575,0.0005454126,0.00031108764,0.0003203902,0.00016303445],"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":[0.00047224603,0.000101677884,0.0025621979,0.00016860336,0.000013491144,0.00029091182,0.00040028157,0.021361632,0.96718353,0.0005499027,0.00008410884,0.006811338],"study_design_scores_gemma":[0.000036111644,0.001097008,0.014784519,0.000035066758,0.00004156199,0.0002147442,0.00024558703,0.10618746,0.8761155,0.00024040027,0.0009553589,0.000046611098],"about_ca_topic_score_codex":0.0004934526,"about_ca_topic_score_gemma":0.0005575752,"teacher_disagreement_score":0.0007801162,"about_ca_system_score_codex":0.00020533556,"about_ca_system_score_gemma":0.0001633117,"threshold_uncertainty_score":0.002633214},"labels":[],"label_agreement":null},{"id":"W2283767481","doi":"10.1002/j.1551-8701.2008.tb01955.x","title":"Pipe End‐Covers Reduce Contamination","year":2008,"lang":"en","type":"article","venue":"Opflow","topic":"Water Systems and 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":"Cover (algebra); Mains electricity; Engineering; Environmental science; Mechanical engineering; Electrical engineering","score_opus":0.00961091324544786,"score_gpt":0.18185714119320304,"score_spread":0.17224622794775518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283767481","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":[0.77532226,0.0032899538,0.08979333,0.001688931,0.0002599091,0.0004978697,0.0012278995,0.0014597187,0.1264601],"genre_scores_gemma":[0.9311699,0.0019373172,0.022780044,0.00037768163,0.000046889316,0.000109242406,0.00060756644,0.00020440508,0.04276705],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99857485,0.00015188534,0.000032892018,0.00009235006,0.0009274861,0.0002205998],"domain_scores_gemma":[0.99896896,0.00015497553,0.00019227294,0.00008288631,0.0005455066,0.00005531528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048721483,0.00049779937,0.00031030623,0.0007216302,0.00091445557,0.00086917815,0.00064801716,0.0006093459,0.006301491],"category_scores_gemma":[0.0012439815,0.00021292207,0.00052927237,0.00045985132,0.0005036127,0.00050888455,0.0009900111,0.00036513267,0.0012959958],"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":[0.0009386937,0.00042450245,0.04570681,0.0014940648,0.00012978041,0.00054938893,0.0010527046,0.082719944,0.42056262,0.009503749,0.026713317,0.41020435],"study_design_scores_gemma":[0.00010150942,0.0026147761,0.21105486,0.000318654,0.00029600458,0.0006140137,0.0010981235,0.021018533,0.40144104,0.0048085223,0.3565242,0.000109828106],"about_ca_topic_score_codex":0.055535264,"about_ca_topic_score_gemma":0.15230528,"teacher_disagreement_score":0.055535264,"about_ca_system_score_codex":0.0021868371,"about_ca_system_score_gemma":0.002146326,"threshold_uncertainty_score":0.11042398},"labels":[],"label_agreement":null},{"id":"W2286680207","doi":"","title":"Validation of log logistic distribution to model water demand using UK and North American data","year":2016,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Weibull distribution; Log-logistic distribution; Statistic; Statistics; Probability distribution; Goodness of fit; Econometrics; Probability density function; Consumption (sociology); Stochastic modelling; Logistic function; Normal distribution; Distribution (mathematics); Mathematics; Environmental science; Distribution fitting","score_opus":0.0530834535736505,"score_gpt":0.2428057203921103,"score_spread":0.1897222668184598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2286680207","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":[0.97461414,0.00012841505,0.020785876,0.00017155848,0.000021684282,0.00009934461,0.0012386892,0.00020758795,0.0027326036],"genre_scores_gemma":[0.9886517,0.000089116846,0.00849522,0.000026718382,0.0000042715465,0.000097974975,0.0018080916,0.000050823804,0.00077611353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99648154,0.0023077896,0.00022253947,0.00037224905,0.00040217565,0.00021363769],"domain_scores_gemma":[0.9727964,0.022230586,0.0008267766,0.001077098,0.002869928,0.0001992634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009454202,0.0005274284,0.0005059222,0.0016295066,0.0004525962,0.0008618794,0.0010116777,0.00084719795,0.0015759575],"category_scores_gemma":[0.03469519,0.00028555415,0.0008887021,0.0019589188,0.0005483279,0.0010905125,0.0008420624,0.0009733054,0.00054277136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012990403,0.0003734693,0.15114926,0.0002477189,0.00019600106,0.00033215352,0.0010089361,0.79586506,0.0016110532,0.00393906,0.0025738177,0.041404344],"study_design_scores_gemma":[0.000054835746,0.00034305372,0.060964257,0.00004989539,0.000022086617,0.000116246025,0.0008218032,0.93238926,0.0013558078,0.0012198088,0.0026132583,0.000049715985],"about_ca_topic_score_codex":0.102570154,"about_ca_topic_score_gemma":0.053629924,"teacher_disagreement_score":0.102570154,"about_ca_system_score_codex":0.0018275151,"about_ca_system_score_gemma":0.0013504885,"threshold_uncertainty_score":0.20394623},"labels":[],"label_agreement":null},{"id":"W2288460956","doi":"10.5539/mas.v10n2p56","title":"Modeling of the Chlorine Content Degradation in a Network of Drinking Water","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Water Systems and 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":"Chlorine; Degradation (telecommunications); Disinfectant; Environmental science; Contamination; Water quality; Water treatment; Environmental chemistry; Chemistry; Computer science; Environmental engineering; Ecology; Telecommunications","score_opus":0.022877338082311908,"score_gpt":0.1814550885334624,"score_spread":0.1585777504511505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288460956","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":[0.57135767,0.0007070085,0.4117206,0.0007249118,0.000090575566,0.0001548901,0.0010582885,0.0006108038,0.01357526],"genre_scores_gemma":[0.98398465,0.00019456787,0.008519815,0.000036302128,0.000011014098,0.00012215813,0.00024233166,0.00003320132,0.0068558953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996524,0.0000781732,0.000016118758,0.00012648891,0.000050248556,0.000076631084],"domain_scores_gemma":[0.9993186,0.00037037267,0.00011594816,0.000019338817,0.00013641008,0.000039369548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005600989,0.00091181975,0.0007758977,0.000709381,0.00073638326,0.0011782285,0.0011811976,0.0020293612,0.0019973267],"category_scores_gemma":[0.0012082666,0.00060343527,0.0012625369,0.00072940986,0.00087787176,0.0010480807,0.00066605676,0.0007174029,0.00019627021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015511276,0.000014624286,0.0006754497,0.000014007406,0.0000073865667,0.000028030432,0.000012526771,0.99746644,0.00055004953,0.00055450154,0.000051726896,0.00060978864],"study_design_scores_gemma":[0.0000013747109,0.000006144469,0.00011155813,8.064418e-7,0.0000023828588,0.0000021361457,0.0000049972523,0.99961084,0.00008958726,0.00013348601,0.000034995057,0.0000016681408],"about_ca_topic_score_codex":0.07041825,"about_ca_topic_score_gemma":0.022130324,"teacher_disagreement_score":0.07041825,"about_ca_system_score_codex":0.0023557441,"about_ca_system_score_gemma":0.0012657986,"threshold_uncertainty_score":0.14001673},"labels":[],"label_agreement":null},{"id":"W2291790540","doi":"10.14288/1.0076464","title":"Reliability analysis of water distribution networks using minimum cut set approach","year":2015,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Set (abstract data type); Component (thermodynamics); Computation; Engineering; Computer science; Power (physics)","score_opus":0.012855526379061248,"score_gpt":0.16542041350051379,"score_spread":0.15256488712145252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291790540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03107677,0.0005408407,0.9645493,0.00019573563,0.000020849417,0.000094232746,0.00020392766,0.00016572705,0.003152583],"genre_scores_gemma":[0.7372757,0.0009811178,0.2575908,0.0000579005,0.000058609276,0.0004450804,0.00065439957,0.0001229867,0.0028133513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871874,0.0006710913,0.000041971554,0.0000936927,0.00036101695,0.00011345925],"domain_scores_gemma":[0.9952106,0.0038730311,0.0002374246,0.00007620063,0.0005328987,0.00006989117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022142744,0.0012175646,0.0012584433,0.0026207902,0.00054651004,0.0013261568,0.0010876335,0.0009729452,0.0019002869],"category_scores_gemma":[0.007026518,0.00080795615,0.0013224883,0.0016043541,0.0007527952,0.0009047018,0.0007672928,0.0010173518,0.0001840413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016632166,0.000007545327,0.00024919253,0.00003124199,0.000015682484,0.000028637916,0.000015592797,0.9920906,0.00021033542,0.002805739,0.00012848168,0.004400322],"study_design_scores_gemma":[0.0000022382135,0.000010047157,0.00011240254,0.0000063838042,0.000004100999,0.0000059348877,0.000006492886,0.99575365,0.00010679177,0.0038950113,0.000094269744,0.000002687173],"about_ca_topic_score_codex":0.009818079,"about_ca_topic_score_gemma":0.0056338995,"teacher_disagreement_score":0.009818079,"about_ca_system_score_codex":0.0017650703,"about_ca_system_score_gemma":0.0012191469,"threshold_uncertainty_score":0.019521832},"labels":[],"label_agreement":null},{"id":"W2292611869","doi":"10.1109/cdc.2015.7403043","title":"Optimal pump scheduling and water flow in water distribution networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Computer science; Scheduling (production processes); Water flow; Distributed computing; Environmental science; Mathematical optimization; Environmental engineering; Mathematics","score_opus":0.009527668473698347,"score_gpt":0.18088752296374683,"score_spread":0.17135985449004848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292611869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08457872,0.000522901,0.90415627,0.00053735235,0.000055089822,0.000095771706,0.00028401316,0.0002357918,0.009534149],"genre_scores_gemma":[0.8597118,0.00072966225,0.13291918,0.000093018534,0.000047242564,0.00014655713,0.00024063612,0.000120094,0.005991864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994791,0.00024141037,0.000012713967,0.00007794766,0.00009794613,0.000090918926],"domain_scores_gemma":[0.99943966,0.00037939506,0.000077155535,0.00001551748,0.000048926202,0.000039295468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090936135,0.0007288234,0.0008093366,0.00054185325,0.00047051368,0.0010842672,0.00056651654,0.0008348822,0.0022840675],"category_scores_gemma":[0.0025295815,0.0006752148,0.0003937375,0.0010584323,0.0008354504,0.0016353871,0.00058810046,0.0007145682,0.00017445731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026518363,0.000010570351,0.000076099335,0.000015448446,0.0000027044448,0.000013521077,0.000008907156,0.9915662,0.00026953378,0.004946713,0.0002518974,0.0028118829],"study_design_scores_gemma":[0.00000806012,0.000008008127,0.000039122984,0.0000017899417,0.0000011381208,0.0000023518153,0.000007760455,0.99642485,0.0001405586,0.0030941744,0.00027028602,0.0000019329443],"about_ca_topic_score_codex":0.018994862,"about_ca_topic_score_gemma":0.013550119,"teacher_disagreement_score":0.018994862,"about_ca_system_score_codex":0.0017081439,"about_ca_system_score_gemma":0.002003525,"threshold_uncertainty_score":0.037768602},"labels":[],"label_agreement":null},{"id":"W2292983827","doi":"10.1007/s12652-016-0362-7","title":"Wireless sensor networks for leak detection in pipelines: a survey","year":2016,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Water Systems and 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":"Acadia University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Pipeline transport; Computer science; Focus (optics); Pipeline (software); Wireless; Wireless sensor network; Leak; Transient (computer programming); Leak detection; SIGNAL (programming language); Computer security; Telecommunications; Environmental science","score_opus":0.02925847341406986,"score_gpt":0.24167878676758556,"score_spread":0.2124203133535157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292983827","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":[0.016764047,0.7502557,0.21165173,0.0026080757,0.0007310014,0.0001468726,0.00027912817,0.00019100726,0.017372454],"genre_scores_gemma":[0.13164417,0.80581146,0.054273065,0.00074210856,0.001390781,0.00012443628,0.00039906704,0.000048143767,0.005566738],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993175,0.00014668674,0.00006330977,0.00018677293,0.00024750544,0.000038259208],"domain_scores_gemma":[0.99797577,0.0012805533,0.00017228759,0.00012988303,0.00039182065,0.00004972764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014186071,0.0010050342,0.0009541475,0.0013471723,0.00027510282,0.0011839151,0.001575974,0.00117498,0.0018648108],"category_scores_gemma":[0.0024477642,0.0005310453,0.0007167899,0.0028433048,0.0005285471,0.0024383012,0.0008718793,0.00095095235,0.000732908],"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":[0.00014389506,0.00022165955,0.0047453456,0.0052161985,0.0002515035,0.00013186394,0.00012714083,0.029376647,0.005547916,0.01821646,0.009051716,0.92696965],"study_design_scores_gemma":[0.000071938804,0.0012632742,0.012436489,0.0037043868,0.00065547857,0.002693263,0.001081383,0.2816277,0.02374836,0.045094144,0.62736577,0.00025782283],"about_ca_topic_score_codex":0.0014678835,"about_ca_topic_score_gemma":0.0015630256,"teacher_disagreement_score":0.0018648108,"about_ca_system_score_codex":0.00043356992,"about_ca_system_score_gemma":0.0006649785,"threshold_uncertainty_score":0.007502377},"labels":[],"label_agreement":null},{"id":"W2293506250","doi":"","title":"Using parallel computing to increase the speed of water distribution network optimization","year":2012,"lang":"en","type":"article","venue":"WDSA 2012: 14th Water Distribution Systems Analysis Conference, 24-27 September 2012 in Adelaide, South Australia","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Message Passing Interface; Sorting; Computer science; Genetic algorithm; Task (project management); Distributed computing; Window (computing); Sorting network; Parallel computing; Multi-core processor; Parallel processing; Interface (matter); Supercomputer; Optimization problem; Message passing; Computational science; Sorting algorithm; Operating system; Algorithm; Engineering; Machine learning","score_opus":0.04761391193698707,"score_gpt":0.2603509842229321,"score_spread":0.21273707228594504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293506250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05659545,0.0015952305,0.91608626,0.0012888544,0.00020985022,0.00011030608,0.0000912733,0.0017348509,0.022287872],"genre_scores_gemma":[0.55154157,0.0017317476,0.44244888,0.00014257042,0.0000919018,0.00014108678,0.00012823453,0.0003394036,0.0034346255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969566,0.00010847007,0.000012719632,0.000033529122,0.000119711796,0.0000298055],"domain_scores_gemma":[0.99937516,0.00037292647,0.000045127916,0.00008231295,0.0001113502,0.000013130242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076683227,0.00063271885,0.0005456226,0.0005437321,0.00044147274,0.0008711196,0.0003931153,0.00044269697,0.00229181],"category_scores_gemma":[0.0028790517,0.0003216306,0.0003784019,0.0011601539,0.0004485317,0.0012277956,0.0006399302,0.0009711004,0.0004846058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008597556,0.000058508696,0.0015021197,0.00016722937,0.000057436053,0.00007036958,0.00007864148,0.83783644,0.009424845,0.021603787,0.0024635997,0.12665102],"study_design_scores_gemma":[0.000019826304,0.000028217666,0.00028176454,0.000013424811,0.00001202728,0.000023297354,0.000017926406,0.976684,0.0034028613,0.014422219,0.0050871233,0.000007332921],"about_ca_topic_score_codex":0.0035029976,"about_ca_topic_score_gemma":0.0027431706,"teacher_disagreement_score":0.0035029976,"about_ca_system_score_codex":0.0005650947,"about_ca_system_score_gemma":0.0008156986,"threshold_uncertainty_score":0.0076668262},"labels":[],"label_agreement":null},{"id":"W2297878978","doi":"10.1007/s11269-016-1241-x","title":"Prediction of Timing of Watermain Failure Using Gene Expression Models","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Gene expression programming; Parametric statistics; Environmental science; Reliability engineering; Structural engineering; Engineering; Computer science; Statistics; Mathematics","score_opus":0.02261287429235681,"score_gpt":0.17884090729014646,"score_spread":0.15622803299778965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297878978","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":[0.8748978,0.0002508306,0.12028751,0.00023461062,0.000049804115,0.000048446134,0.0020471327,0.00079844357,0.0013853668],"genre_scores_gemma":[0.9916666,0.00008104528,0.0070007737,0.000013008055,0.000007966079,0.000020953183,0.000692772,0.00002567583,0.00049128116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988616,0.000020155483,0.0000056557947,0.000044204487,0.000020050195,0.00002380906],"domain_scores_gemma":[0.9991098,0.0005878845,0.00012586066,0.000029477795,0.00009422276,0.000052673284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040998816,0.0006092439,0.00061398983,0.0006933708,0.00018140824,0.00057493383,0.00037876496,0.00047879928,0.0008069436],"category_scores_gemma":[0.0016144324,0.0003437444,0.0005790289,0.0006000555,0.00024893045,0.00044090374,0.00020620963,0.0005308573,0.00020414319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017279116,0.00005451393,0.022369599,0.000033145003,0.000044398406,0.00007092424,0.000012345924,0.95810175,0.008490544,0.00063567644,0.00037750506,0.009636832],"study_design_scores_gemma":[0.0000040271525,0.000018015993,0.0037951227,0.0000014745489,0.000008441084,0.0000058650626,0.0000051219645,0.99405164,0.0015188275,0.00051768724,0.0000702946,0.000003505671],"about_ca_topic_score_codex":0.011012879,"about_ca_topic_score_gemma":0.011613252,"teacher_disagreement_score":0.011012879,"about_ca_system_score_codex":0.00090458634,"about_ca_system_score_gemma":0.0007570116,"threshold_uncertainty_score":0.021897554},"labels":[],"label_agreement":null},{"id":"W2297893592","doi":"10.1002/j.1551-8833.2009.tb09860.x","title":"Effect of regulations and treatment technologies on water distribution infrastructure","year":2009,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Research Council Canada; Water Research Foundation; U.S. Environmental Protection Agency","keywords":"General partnership; Water quality; Distribution (mathematics); Water infrastructure; Water treatment; Environmental planning; Business; Upgrade; Environmental science; Environmental resource management; Environmental economics; Environmental engineering; Water supply; Computer science; Economics; Finance","score_opus":0.001404355075122082,"score_gpt":0.17744030019679657,"score_spread":0.1760359451216745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297893592","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":[0.97120225,0.0004951683,0.0074497354,0.00052751624,0.00006803771,0.00016972021,0.00037539657,0.000093510636,0.019618616],"genre_scores_gemma":[0.99601513,0.0002025679,0.0018294019,0.000106627966,0.00000869535,0.00004923785,0.00013544652,0.0000120235345,0.0016409126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9933696,0.0031900322,0.00029259882,0.0005537189,0.0013627083,0.0012312318],"domain_scores_gemma":[0.9533524,0.037094813,0.004687186,0.0015918713,0.002758123,0.00051556906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043841554,0.00063300267,0.0004970268,0.0009436377,0.0008198184,0.0015734559,0.0010358641,0.001541316,0.0049822624],"category_scores_gemma":[0.021939397,0.00044913683,0.0012358803,0.00103312,0.0015426347,0.0017719808,0.0009273809,0.0010622178,0.0003916365],"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":[0.0025808022,0.0021040333,0.06996855,0.00058081397,0.00057211093,0.0011018834,0.0003315216,0.8191928,0.027578583,0.01760299,0.0023097624,0.056076236],"study_design_scores_gemma":[0.0010120356,0.011342501,0.36772925,0.00033402955,0.003001202,0.00087227084,0.004006064,0.43043405,0.13257313,0.022630097,0.02561255,0.00045278884],"about_ca_topic_score_codex":0.021540217,"about_ca_topic_score_gemma":0.018674575,"teacher_disagreement_score":0.021540217,"about_ca_system_score_codex":0.0037180136,"about_ca_system_score_gemma":0.0026490188,"threshold_uncertainty_score":0.042829692},"labels":[],"label_agreement":null},{"id":"W2300215658","doi":"10.14796/jwmm.r235-23","title":"A Simulation Study on Using Inverse Transient Analysis for Leak Detection in Water Distribution Networks","year":2009,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Toronto","funders":"University of Toronto","keywords":"Leak detection; Transient (computer programming); Leak; Environmental science; Transient analysis; Computer science; Distribution (mathematics); Engineering; Civil engineering; Environmental engineering; Transient response; Mathematics; Electrical engineering","score_opus":0.02611733913303207,"score_gpt":0.23832353157089795,"score_spread":0.21220619243786587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300215658","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":[0.9597428,0.00011844934,0.031869847,0.00013279733,0.000011383489,0.00008516417,0.0001897317,0.0000951788,0.007754674],"genre_scores_gemma":[0.99204046,0.00012171492,0.0071259765,0.000008478458,0.0000023817581,0.000038588903,0.000078592406,0.000006015935,0.0005778785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997279,0.00015721207,0.000013472211,0.000019233597,0.00004452583,0.000037623635],"domain_scores_gemma":[0.99672973,0.002680541,0.00012938457,0.000096131196,0.00032217748,0.00004204373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085342576,0.00036793295,0.00050266023,0.0004781206,0.00030254936,0.00043256575,0.00036445435,0.00060258986,0.0009848657],"category_scores_gemma":[0.0023655116,0.00018008568,0.0005103895,0.00055871607,0.00032928638,0.000591811,0.0002786461,0.00037974297,0.00006818428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009997575,0.00007860069,0.0030862645,0.000035577355,0.0000112997495,0.00006243734,0.000045795856,0.99069935,0.0013180593,0.0010689555,0.00010900435,0.0033847499],"study_design_scores_gemma":[0.00001559893,0.0001281985,0.00072929444,0.0000050141216,0.0000111998625,0.00001233765,0.00004321279,0.99756694,0.0011410799,0.00019440497,0.00014730952,0.000005383581],"about_ca_topic_score_codex":0.011694818,"about_ca_topic_score_gemma":0.0069111255,"teacher_disagreement_score":0.011694818,"about_ca_system_score_codex":0.00053477037,"about_ca_system_score_gemma":0.00040146764,"threshold_uncertainty_score":0.0232535},"labels":[],"label_agreement":null},{"id":"W2300790086","doi":"10.14796/jwmm.r220-21","title":"GIS Applications for Water Distribution Systems","year":2004,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geographic information system; GIS applications; GIS and public health; Distribution (mathematics); Computer science; AM/FM/GIS; Remote sensing; Geography; Mathematics","score_opus":0.011417705261526535,"score_gpt":0.1955840947652687,"score_spread":0.18416638950374217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300790086","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":[0.005390763,0.004292723,0.7611735,0.0021211868,0.00073326495,0.00052747445,0.02337642,0.087207764,0.11517688],"genre_scores_gemma":[0.09189615,0.012891341,0.7780634,0.0010756659,0.00059224654,0.0013497117,0.046414346,0.009578423,0.05813886],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992754,0.00018778484,0.00007391742,0.00008182865,0.00034320238,0.00003781891],"domain_scores_gemma":[0.9989229,0.0005135125,0.000043180873,0.00014056443,0.00034036636,0.000039446168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087360945,0.0011681035,0.0006537997,0.002516062,0.0005544057,0.0020257593,0.00125826,0.0009658681,0.056620337],"category_scores_gemma":[0.0039203893,0.0005949524,0.00078675826,0.0048955693,0.00027960812,0.0018144429,0.0016165922,0.0010802591,0.019598003],"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":[0.00011891549,0.00007316918,0.0016623909,0.0012626586,0.000101187194,0.00077240623,0.00069232367,0.037107006,0.004495159,0.07358985,0.3781584,0.50196654],"study_design_scores_gemma":[0.00005369919,0.000019268207,0.0005662623,0.00017138863,0.00002483095,0.000505868,0.00018105285,0.052926153,0.003139754,0.036843456,0.90553087,0.00003744038],"about_ca_topic_score_codex":0.0029765847,"about_ca_topic_score_gemma":0.0026786535,"teacher_disagreement_score":0.056620337,"about_ca_system_score_codex":0.0006165662,"about_ca_system_score_gemma":0.000566866,"threshold_uncertainty_score":0.18941385},"labels":[],"label_agreement":null},{"id":"W2301155771","doi":"10.14796/jwmm.r207-18","title":"An Installed Accuracy Assessment using Dye Dilution Testing for Seven Common Flow Metering Technologies","year":2001,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Metering mode; Dilution; Protocol (science); Flow measurement; Accuracy and precision; Indicator dilution; Computer science; Flow (mathematics); Metre; Environmental science; Engineering; Mechanical engineering; Statistics; Mathematics; Mechanics; Medicine","score_opus":0.04841161878859767,"score_gpt":0.2799283808440703,"score_spread":0.2315167620554726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301155771","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":[0.73955995,0.0018673063,0.2379118,0.0003069272,0.00020337595,0.0017310397,0.0018674906,0.0012101766,0.01534196],"genre_scores_gemma":[0.7478069,0.0009386923,0.24078906,0.0001160396,0.00003549368,0.00079036324,0.0013538765,0.00012160371,0.008047938],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9848504,0.0025928523,0.0013348954,0.0011387707,0.009711738,0.00037141918],"domain_scores_gemma":[0.97816294,0.0046471083,0.0019197315,0.001684796,0.013363488,0.00022194271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008191435,0.0008886009,0.0007475487,0.004188138,0.00095956365,0.0011491171,0.0012828716,0.0009953827,0.0018079395],"category_scores_gemma":[0.014913283,0.00042915303,0.00068310095,0.0022742297,0.0007597042,0.0013691116,0.0010103673,0.0006000661,0.0006187662],"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":[0.00085417915,0.00079228927,0.17763412,0.0010350018,0.00017360985,0.00031916486,0.0018089786,0.014648292,0.4286827,0.0031188098,0.0023746993,0.36855823],"study_design_scores_gemma":[0.0000834303,0.0052891364,0.17236288,0.00017024211,0.00036818915,0.0007141547,0.0008400591,0.03462688,0.76086915,0.0009306492,0.02343361,0.00031164286],"about_ca_topic_score_codex":0.0053566257,"about_ca_topic_score_gemma":0.01819885,"teacher_disagreement_score":0.008191435,"about_ca_system_score_codex":0.0021054398,"about_ca_system_score_gemma":0.0012555837,"threshold_uncertainty_score":0.043320954},"labels":[],"label_agreement":null},{"id":"W2302626862","doi":"10.1061/(asce)cf.1943-5509.0000886","title":"Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models","year":2016,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline transport; Artificial neural network; Robustness (evolution); Backpropagation; Engineering; Pipeline (software); Forensic engineering; Civil engineering; Reliability engineering; Computer science; Artificial intelligence; Environmental engineering","score_opus":0.027765675366037884,"score_gpt":0.22197079178797657,"score_spread":0.1942051164219387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2302626862","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":[0.9326264,0.00033807923,0.062521555,0.00021886441,0.000027725891,0.00004834564,0.00043363663,0.0002458132,0.0035396307],"genre_scores_gemma":[0.9943151,0.00008140849,0.0045521967,0.000009390164,0.0000034135421,0.00002046532,0.00018708485,0.000005442701,0.0008254815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998016,0.000055342694,0.000013580197,0.000040097937,0.000053651132,0.000035729783],"domain_scores_gemma":[0.99919647,0.00039545502,0.00010495702,0.000029564704,0.00024358711,0.000029977175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006790639,0.00082084833,0.00038838014,0.0007408261,0.00034141185,0.00063940714,0.0007886645,0.0006658676,0.00063963875],"category_scores_gemma":[0.0021148399,0.00019676264,0.00050480844,0.0005719823,0.00029775995,0.00054759905,0.00026251274,0.00031397873,0.00008568705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029636194,0.000024883026,0.0034022017,0.000010046061,0.000011880739,0.000018776838,0.000007891142,0.9916972,0.0002164977,0.00013200383,0.00009040671,0.004358495],"study_design_scores_gemma":[9.2935e-7,0.000009752671,0.0006911506,9.760798e-7,0.0000024403473,0.0000013982603,0.0000032209975,0.999108,0.00011857075,0.000041589476,0.000020191763,0.0000017761355],"about_ca_topic_score_codex":0.18363185,"about_ca_topic_score_gemma":0.12139958,"teacher_disagreement_score":0.18363185,"about_ca_system_score_codex":0.0030521862,"about_ca_system_score_gemma":0.0013264732,"threshold_uncertainty_score":0.36512595},"labels":[],"label_agreement":null},{"id":"W2303312828","doi":"10.14796/jwmm.r227-19","title":"Water Quality Modeling using Fault Tree Method","year":2007,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Fault tree analysis; Due diligence; Quality (philosophy); Tree (set theory); Fault (geology); Reliability engineering; Diligence; Computer science; Outbreak; Engineering; Business; Mathematics; Geology; Psychology; Virology; Biology; Seismology; Social psychology","score_opus":0.050436505633538305,"score_gpt":0.2950152630208158,"score_spread":0.24457875738727752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2303312828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011140181,0.00016071413,0.9863501,0.00011723656,0.000022061146,0.000024912113,0.00021767194,0.00027771082,0.0016893104],"genre_scores_gemma":[0.87807006,0.0005232548,0.1154434,0.000066487904,0.00005703937,0.00022986933,0.0005879348,0.00008814645,0.0049338257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995552,0.00015639405,0.000025654492,0.00007980022,0.00012847569,0.0000545276],"domain_scores_gemma":[0.99907243,0.0005592347,0.00011435209,0.00003768559,0.0001872755,0.000029084087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007323672,0.00065155164,0.00096497545,0.0010090475,0.00044692308,0.0008131918,0.0011320873,0.0011361175,0.0033433596],"category_scores_gemma":[0.0023241683,0.00035193027,0.00084168505,0.0011367286,0.00039671225,0.0011505772,0.00042358908,0.00069934555,0.00049737963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000083070945,0.000005238025,0.0002565496,0.000009482721,0.000008780058,0.000016399212,0.000008660911,0.99113125,0.000113573384,0.0027012152,0.00014891842,0.005591608],"study_design_scores_gemma":[9.413293e-7,0.0000020982566,0.000028055412,8.9208476e-7,0.000001224055,0.0000017396518,9.292701e-7,0.9984944,0.000023312474,0.0013546866,0.00009058169,0.0000011047644],"about_ca_topic_score_codex":0.022854486,"about_ca_topic_score_gemma":0.009990845,"teacher_disagreement_score":0.022854486,"about_ca_system_score_codex":0.0008287177,"about_ca_system_score_gemma":0.0008266502,"threshold_uncertainty_score":0.04544288},"labels":[],"label_agreement":null},{"id":"W2304678330","doi":"10.14796/jwmm.c399","title":"Assessment of Pressure Transients Due to Trapped Air Compression in Rapidly Filling Combined Sewer Overflow Tunnels","year":2016,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Compressed air; Compression (physics); Pipeline (software); Geotechnical engineering; Environmental science; Geology; Petroleum engineering; Mechanics; Materials science; Engineering; Composite material; Physics; Mechanical engineering","score_opus":0.011414178489104388,"score_gpt":0.21817238375315226,"score_spread":0.20675820526404787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2304678330","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":[0.99809927,0.000015800415,0.0015518271,0.000010012962,0.0000022211116,0.000010037534,0.00003825986,0.0000414165,0.00023113137],"genre_scores_gemma":[0.99941385,0.000012098413,0.00044884792,0.0000014858521,6.2224547e-7,0.000005499391,0.00002166731,0.000002245708,0.00009378787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983954,0.000035029047,0.000011025847,0.000023395092,0.00005190616,0.00003909133],"domain_scores_gemma":[0.99923694,0.00039481552,0.00012757732,0.000040637886,0.00011288786,0.000087177505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046142464,0.00027755636,0.00037963782,0.00053081225,0.00029008367,0.0005454713,0.00041298015,0.0006004393,0.0008052215],"category_scores_gemma":[0.0011303794,0.00019111177,0.0002727583,0.00040598013,0.00042211034,0.00047733894,0.0004815065,0.00042228884,0.00005223941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017840299,0.0006796098,0.072384775,0.00024733937,0.00006421558,0.0020978171,0.0005068352,0.70325273,0.19337545,0.0012926218,0.00040240787,0.02391219],"study_design_scores_gemma":[0.000030359157,0.00085716305,0.027461765,0.000012528517,0.000014664791,0.00011705453,0.0002248389,0.93710196,0.03379286,0.00019832468,0.00015471545,0.000033767883],"about_ca_topic_score_codex":0.002751962,"about_ca_topic_score_gemma":0.0019492909,"teacher_disagreement_score":0.002751962,"about_ca_system_score_codex":0.0003804818,"about_ca_system_score_gemma":0.00043625745,"threshold_uncertainty_score":0.005471885},"labels":[],"label_agreement":null},{"id":"W2305824475","doi":"10.14796/jwmm.r245-21","title":"An Improved Water Distribution System Chlorine Decay Model Using EPANET MSX","year":2012,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Chlorine; Residual; Water quality; Water model; Environmental science; Computer science; Chemistry; Algorithm; Computational chemistry","score_opus":0.01805544174443297,"score_gpt":0.21139825052067657,"score_spread":0.1933428087762436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2305824475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23965436,0.00032083562,0.6584317,0.0008623229,0.0003082805,0.0004307291,0.01041588,0.015537824,0.074038036],"genre_scores_gemma":[0.84487724,0.00022987745,0.119190305,0.00017392254,0.00004193204,0.0003896997,0.0035128843,0.00063913333,0.030945066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999871,0.0000245692,0.0000057001325,0.000026712074,0.000054120253,0.000017861472],"domain_scores_gemma":[0.9998091,0.000043836575,0.000015395804,0.000019667792,0.00009676858,0.000015330606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028251606,0.0005677135,0.0006886753,0.00032723657,0.00048390697,0.0007119129,0.0013012425,0.00075653085,0.0070569557],"category_scores_gemma":[0.00061865913,0.00037418836,0.000653585,0.0004338585,0.00021693173,0.0008232013,0.00048095395,0.0008484675,0.00095117034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037632515,0.00003064361,0.0006573795,0.000024137442,0.000011620036,0.000026440333,0.000010503827,0.98877865,0.0014014208,0.0016640038,0.0018453005,0.0055122394],"study_design_scores_gemma":[0.000013534382,0.0000097905995,0.00012518158,0.0000016100142,0.0000040601594,0.000004016188,0.0000022726203,0.99767894,0.0005076819,0.00024181792,0.0014073062,0.0000039148226],"about_ca_topic_score_codex":0.05144152,"about_ca_topic_score_gemma":0.03147698,"teacher_disagreement_score":0.05144152,"about_ca_system_score_codex":0.0011531031,"about_ca_system_score_gemma":0.0020664986,"threshold_uncertainty_score":0.10228419},"labels":[],"label_agreement":null},{"id":"W2307020659","doi":"","title":"PRESSURE RELATED STANDARDS AND PERFORMANCE OF WATER DISTRIBUTION SYSTEMS","year":2016,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Water Systems and 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":"Government of Canada; McMaster University","keywords":"Environmental science","score_opus":0.0032879447073450776,"score_gpt":0.1549734172069822,"score_spread":0.15168547249963713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2307020659","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":[0.8003971,0.0019891486,0.13516255,0.0019505613,0.00015528193,0.00019146911,0.00077573245,0.0010837879,0.05829441],"genre_scores_gemma":[0.9967976,0.00016970915,0.0019618592,0.000020612337,0.000026019832,0.000019310739,0.00014022364,0.00003535981,0.0008292885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99229795,0.0018764309,0.000723314,0.0005618351,0.0039646835,0.0005757607],"domain_scores_gemma":[0.9792002,0.009633659,0.0036868856,0.0014211918,0.0056554945,0.00040265638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004963999,0.00042392252,0.000428384,0.0016409642,0.00069097464,0.0036959562,0.0007965041,0.0010814206,0.0024884853],"category_scores_gemma":[0.02561642,0.00024912276,0.00029124005,0.001962569,0.0013902057,0.003944369,0.0013590823,0.00060487335,0.0006489974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065568584,0.0003311535,0.07314739,0.0007006366,0.00018895345,0.0005072448,0.0012757668,0.67346203,0.02106695,0.07467289,0.004701217,0.14929004],"study_design_scores_gemma":[0.0001299602,0.0030160642,0.15663782,0.00023419154,0.00014813809,0.0009798729,0.0025150485,0.6185896,0.056856934,0.13365196,0.026932232,0.0003082811],"about_ca_topic_score_codex":0.0021611955,"about_ca_topic_score_gemma":0.000754585,"teacher_disagreement_score":0.004963999,"about_ca_system_score_codex":0.0016830396,"about_ca_system_score_gemma":0.0009352922,"threshold_uncertainty_score":0.026252449},"labels":[],"label_agreement":null},{"id":"W2308290803","doi":"10.14796/jwmm.r225-14","title":"Transient Flow Modeling and Verification of a 20-MGD Raw Water Conveyance System","year":2006,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Transient (computer programming); Transient flow; Environmental science; Flow (mathematics); Mechanics; Steady state (chemistry); Meteorology; Computer science; Physics; Surge; Chemistry","score_opus":0.0077014047681164665,"score_gpt":0.16299416091961474,"score_spread":0.1552927561514983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2308290803","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":[0.9087522,0.0000672046,0.0780613,0.00016650851,0.000029391591,0.00017777948,0.0010319551,0.0029048368,0.008808734],"genre_scores_gemma":[0.99545634,0.00002090553,0.0028852231,0.0000053213425,0.0000016400545,0.000033521097,0.00016567337,0.000025784548,0.0014055013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965644,0.00007194927,0.000028587827,0.00006118462,0.0001269707,0.00005485997],"domain_scores_gemma":[0.9993844,0.00028850927,0.000062886145,0.00007092443,0.00017116315,0.000022015869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056889787,0.00042819112,0.0005808505,0.00055149506,0.0005605785,0.00054785516,0.00076508115,0.00057486125,0.0040580328],"category_scores_gemma":[0.0013143226,0.0002833096,0.00064257096,0.00030021908,0.0005121827,0.0006062256,0.00033641694,0.00045161555,0.0003365616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033930902,0.00009655433,0.0032117874,0.00014049457,0.000013842056,0.00028127935,0.0002266904,0.94888765,0.030957358,0.0023535818,0.0007307823,0.012760685],"study_design_scores_gemma":[0.000022737762,0.00016542431,0.0012411526,0.000004497732,0.000007538493,0.00001674334,0.000028180966,0.98810476,0.009838967,0.0001583586,0.00040311934,0.000008606454],"about_ca_topic_score_codex":0.024027994,"about_ca_topic_score_gemma":0.010215923,"teacher_disagreement_score":0.024027994,"about_ca_system_score_codex":0.0016168677,"about_ca_system_score_gemma":0.0014596678,"threshold_uncertainty_score":0.04777628},"labels":[],"label_agreement":null},{"id":"W2308412799","doi":"10.14796/jwmm.r245-03","title":"Discretized Air Phase Modeling in the Simulation of Pipeline Refilling Operations","year":2012,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Discretization; Pipeline (software); Computation; Phase (matter); Mechanics; Mains electricity; Pipeline transport; Computer science; Marine engineering; Petroleum engineering; Geology; Engineering; Mechanical engineering; Physics; Mathematics; Thermodynamics; Algorithm; Power (physics); Mathematical analysis","score_opus":0.035076530174536054,"score_gpt":0.2608245067510682,"score_spread":0.22574797657653217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2308412799","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":[0.17885178,0.0014824924,0.771928,0.0008189798,0.00050396204,0.00008802696,0.0004458648,0.00064080145,0.045240097],"genre_scores_gemma":[0.9084793,0.00096976175,0.07785172,0.00014997169,0.00008107289,0.00010177258,0.00019766061,0.00014239458,0.012026301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998234,0.00007212116,0.000011248085,0.000016413494,0.000052269337,0.000024555617],"domain_scores_gemma":[0.9997185,0.000165812,0.000024112518,0.000020560685,0.00004886491,0.000022126955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021410405,0.00031841092,0.0005510981,0.0002478751,0.00039174553,0.0009843563,0.0007726206,0.0010325984,0.0030959886],"category_scores_gemma":[0.0011015303,0.00039156835,0.0004963753,0.00048571918,0.0005425996,0.00095701346,0.0005668136,0.0009057403,0.00036414337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019913474,0.0000150719,0.00023633687,0.000020960551,0.0000065034087,0.000033664917,0.000031042247,0.9855334,0.0014793823,0.008894411,0.00023402563,0.0034951936],"study_design_scores_gemma":[0.000005435829,0.0000075847743,0.000042203734,0.0000036398703,0.0000015870737,0.0000036996803,0.0000075569837,0.99779665,0.0002112317,0.001256591,0.0006613571,0.0000025749255],"about_ca_topic_score_codex":0.014338621,"about_ca_topic_score_gemma":0.009906797,"teacher_disagreement_score":0.014338621,"about_ca_system_score_codex":0.0006024911,"about_ca_system_score_gemma":0.0007778743,"threshold_uncertainty_score":0.028510332},"labels":[],"label_agreement":null},{"id":"W2308711047","doi":"10.14796/jwmm.r227-11","title":"Optimal Design of Urban Drainage Systems using Genetic Algorithms","year":2007,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Coquitlam College","funders":"","keywords":"Drainage; Computer science; Genetic algorithm; Algorithm; Biology; Machine learning; Ecology","score_opus":0.02546053678339466,"score_gpt":0.2147579121141261,"score_spread":0.18929737533073143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2308711047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1008113,0.00082926254,0.8808505,0.00042585208,0.000092395196,0.00021678227,0.00020985166,0.0006947288,0.01586946],"genre_scores_gemma":[0.8743582,0.00034984696,0.11806317,0.0001267683,0.000023804612,0.00038811966,0.0002499647,0.000109482426,0.0063306885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995859,0.00014538213,0.000018587329,0.000076019525,0.00006749662,0.00010661418],"domain_scores_gemma":[0.9989955,0.0006400143,0.0001093949,0.000028446611,0.00017171494,0.000054911663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008527913,0.0012142499,0.0013201117,0.001367001,0.0005699375,0.0014146484,0.00095538865,0.0014975364,0.0038587896],"category_scores_gemma":[0.0023635365,0.0009400482,0.00088043173,0.0010290404,0.0009809774,0.00067570567,0.0010289238,0.00084837334,0.0003905385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073920714,0.0000071143727,0.00008083501,0.000006867909,0.000005803892,0.000007681857,0.0000057582197,0.99681807,0.00006677706,0.00052273844,0.000079654135,0.0023912485],"study_design_scores_gemma":[0.000010700772,0.0000116783785,0.000036384376,0.0000034048212,0.0000035142155,0.0000017639234,0.0000068852255,0.9988016,0.00005048075,0.0008935805,0.00017813678,0.0000018039652],"about_ca_topic_score_codex":0.01710364,"about_ca_topic_score_gemma":0.015004836,"teacher_disagreement_score":0.01710364,"about_ca_system_score_codex":0.0013812792,"about_ca_system_score_gemma":0.0019347131,"threshold_uncertainty_score":0.034008205},"labels":[],"label_agreement":null},{"id":"W2309564712","doi":"10.2166/aqua.2003.0020","title":"Predicting trihalomethane formation in chlorinated waters using multivariate regression and neural networks","year":2003,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":true,"ca_institutions":"Université Laval","funders":"U.S. Geological Survey","keywords":"Icon; Multivariate statistics; Trihalomethane; Computer science; Art; Library science; Chemistry; Machine learning","score_opus":0.029779815416798985,"score_gpt":0.27571542402566734,"score_spread":0.24593560860886837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2309564712","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":[0.78555214,0.0027092008,0.19947927,0.001358111,0.0001681529,0.00011789586,0.002556592,0.0016353716,0.006423236],"genre_scores_gemma":[0.95590734,0.001115926,0.037463434,0.00007357613,0.00006808341,0.000058532598,0.0014696391,0.000082920866,0.0037605797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966586,0.000107917615,0.00002519993,0.00009183373,0.000073952164,0.00003530748],"domain_scores_gemma":[0.9986463,0.00085389207,0.0001709976,0.00005008967,0.00023766761,0.00004116258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010739584,0.0010015626,0.00072617154,0.001323347,0.0002930212,0.001262974,0.00043282143,0.0007462722,0.002002728],"category_scores_gemma":[0.00447078,0.0004756525,0.0012574202,0.001038035,0.00021520167,0.0010286381,0.00043348374,0.0010504508,0.000709804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065604696,0.00037623307,0.059397835,0.00018426131,0.00045799042,0.000095926385,0.000061310035,0.7743416,0.008482526,0.00058932364,0.0025659215,0.15279111],"study_design_scores_gemma":[0.000005151271,0.000026807056,0.0053123278,0.000007858617,0.000021285228,0.000007976565,0.000010921976,0.99278647,0.0013196985,0.00029176663,0.00019954143,0.000010193673],"about_ca_topic_score_codex":0.038496442,"about_ca_topic_score_gemma":0.041098375,"teacher_disagreement_score":0.038496442,"about_ca_system_score_codex":0.0013358638,"about_ca_system_score_gemma":0.0007979799,"threshold_uncertainty_score":0.0765447},"labels":[],"label_agreement":null},{"id":"W2310237458","doi":"10.14288/1.0050201","title":"Two-layer exchange flow through the Burlington Ship Canal","year":2009,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"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":"Layer (electronics); Flow (mathematics); Geology; Mechanics","score_opus":0.010534756147117737,"score_gpt":0.16649664524035473,"score_spread":0.155961889093237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2310237458","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":[0.99868673,0.000024832474,0.00030365653,0.00002128073,0.0000021646808,0.000008373143,0.00009730071,0.000026716947,0.00082894677],"genre_scores_gemma":[0.99823415,0.000027523549,0.000482693,0.000008348924,0.0000016404316,0.000007818706,0.00014219354,0.0000040296977,0.0010915683],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998634,0.000008256207,0.0000051162656,0.000049181228,0.00003645311,0.00003752241],"domain_scores_gemma":[0.99981004,0.000023588098,0.000055495322,0.000014076945,0.000057999026,0.00003874113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087118926,0.00013154722,0.00017549138,0.000463169,0.0006894516,0.0007271351,0.00026832928,0.0002364395,0.0013050548],"category_scores_gemma":[0.00035384364,0.00014155044,0.00012092802,0.0006467902,0.00036674598,0.00027749498,0.0004895642,0.00018305703,0.000092991155],"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":[0.0008128108,0.0001576982,0.688163,0.00013785412,0.00006610005,0.0012276596,0.0051356703,0.01465423,0.22505607,0.00374818,0.002452208,0.05838848],"study_design_scores_gemma":[0.00006561668,0.00014532742,0.968566,0.000011594574,0.000020747375,0.00010012345,0.0009590961,0.018799324,0.005754513,0.00025796567,0.0052777003,0.000041986463],"about_ca_topic_score_codex":0.2585462,"about_ca_topic_score_gemma":0.30048677,"teacher_disagreement_score":0.2585462,"about_ca_system_score_codex":0.0024626849,"about_ca_system_score_gemma":0.0022125568,"threshold_uncertainty_score":0.51408255},"labels":[],"label_agreement":null},{"id":"W2310697366","doi":"10.14796/jwmm.r236-29","title":"Contaminant Source Identification for Priority Nodes in Water Distribution Systems","year":2010,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph; Conestoga College","funders":"","keywords":"Identification (biology); Water source; Environmental science; Distribution (mathematics); Stage (stratigraphy); Computer science; Water resource management; Geology; Mathematics; Ecology","score_opus":0.008417541506786799,"score_gpt":0.1995717918206945,"score_spread":0.1911542503139077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2310697366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11527503,0.00008049762,0.8810568,0.00008569613,0.000022229062,0.00031628145,0.00021019827,0.0012862423,0.0016670661],"genre_scores_gemma":[0.67938,0.000091711525,0.31440943,0.00004085532,0.000011366328,0.0001771534,0.00033370542,0.00009629836,0.0054594753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995152,0.00013929264,0.00001589103,0.00009844591,0.00015836228,0.00007290771],"domain_scores_gemma":[0.9987061,0.0006707361,0.00011976518,0.00006615134,0.0003587869,0.00007834538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082361524,0.00068242353,0.00054046727,0.0007521933,0.000660113,0.00070189545,0.000794328,0.00061031774,0.0036222653],"category_scores_gemma":[0.0028837218,0.00037193266,0.00033735397,0.00035095197,0.00028495744,0.0008009971,0.00068657106,0.0006386224,0.00069323665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015857072,0.0004156265,0.009899689,0.000571289,0.00005062674,0.000493288,0.00039125976,0.510613,0.20748515,0.009214325,0.0031963598,0.25608358],"study_design_scores_gemma":[0.000051148658,0.00040404755,0.0021841156,0.00001305872,0.000023224004,0.000064562584,0.000103637634,0.9214666,0.07082079,0.0032713795,0.0015594083,0.000038004833],"about_ca_topic_score_codex":0.006130873,"about_ca_topic_score_gemma":0.010410795,"teacher_disagreement_score":0.006130873,"about_ca_system_score_codex":0.00073927944,"about_ca_system_score_gemma":0.001824672,"threshold_uncertainty_score":0.012190402},"labels":[],"label_agreement":null},{"id":"W2310885566","doi":"10.14796/jwmm.r223-07","title":"A Review of Reliability Analysis for Water Quality in Water Distribution Systems","year":2005,"lang":"en","type":"review","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Water quality; Quality (philosophy); Environmental science; Distribution (mathematics); Computer science; Engineering; Mathematics; Thermodynamics; Physics","score_opus":0.05062733708069007,"score_gpt":0.3081057229457305,"score_spread":0.25747838586504046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2310885566","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":[0.00039658253,0.97725016,0.015119858,0.00063529436,0.00050791854,0.00002346995,0.00006527581,0.000050310708,0.0059511643],"genre_scores_gemma":[0.0049162856,0.9827178,0.008544666,0.00028858788,0.0006925682,0.000035527035,0.00009111946,0.0000251072,0.0026883625],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984133,0.00036995354,0.00020064805,0.00020652669,0.0007537765,0.000055698987],"domain_scores_gemma":[0.99679154,0.0018609137,0.0002391794,0.00011136641,0.00095997396,0.0000369251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018264166,0.0019715766,0.0026108848,0.0033507405,0.0005867388,0.0015482538,0.001885243,0.0019435523,0.004823066],"category_scores_gemma":[0.004054057,0.0007454008,0.0011776631,0.0072228005,0.0010372205,0.0026315136,0.00064985675,0.0014176179,0.003009353],"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":[0.000035344678,0.00010094731,0.0005349216,0.02390204,0.00017581497,0.0002903226,0.00017275247,0.017869918,0.0017850549,0.03699774,0.060534112,0.8576011],"study_design_scores_gemma":[0.000014948727,0.00017662073,0.002068196,0.009172209,0.00019767665,0.001353561,0.00014804634,0.007028865,0.0016349438,0.034834057,0.943262,0.000108763175],"about_ca_topic_score_codex":0.005857253,"about_ca_topic_score_gemma":0.0051482837,"teacher_disagreement_score":0.005857253,"about_ca_system_score_codex":0.001666327,"about_ca_system_score_gemma":0.002245907,"threshold_uncertainty_score":0.016134739},"labels":[],"label_agreement":null},{"id":"W2311754513","doi":"10.14796/jwmm.r223-13","title":"Stormwater Quality Descriptions using the Three Parameter Lognormal Distribution","year":2005,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Log-normal distribution; Stormwater; Environmental science; Cumulative distribution function; Stormwater management; Hydrology (agriculture); Probability distribution; Distribution (mathematics); Statistics; Surface runoff; Econometrics; Mathematics; Probability density function; Geology; Geotechnical engineering","score_opus":0.04968672775095206,"score_gpt":0.24527262845714987,"score_spread":0.1955859007061978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2311754513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01721656,0.0007208924,0.9694782,0.00030947765,0.000062101404,0.000112181406,0.002392223,0.0007791204,0.008929224],"genre_scores_gemma":[0.7253942,0.0049714996,0.24170925,0.00047047265,0.00018167906,0.0006244381,0.0073043564,0.00054729066,0.01879677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998719,0.00023251044,0.00016470862,0.00016894632,0.0006017608,0.00011320971],"domain_scores_gemma":[0.9976084,0.0008016925,0.00038752583,0.00024515908,0.00090577774,0.000051425835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018371239,0.0007629483,0.0004086346,0.0020696712,0.0004313358,0.0018030746,0.0014005069,0.0006228951,0.0034257113],"category_scores_gemma":[0.00600839,0.00024302029,0.0009990184,0.0026681216,0.0005674101,0.002392647,0.00075397623,0.0007856113,0.0012837976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010339999,0.000073557036,0.017677557,0.00018100698,0.00009861943,0.00047598686,0.0002635268,0.68104583,0.0020286983,0.1567902,0.014536931,0.1267247],"study_design_scores_gemma":[0.000016836473,0.000048827627,0.005574095,0.00006944002,0.000029637042,0.0004867267,0.0001756904,0.83539224,0.00091783697,0.131863,0.025373574,0.000052098974],"about_ca_topic_score_codex":0.01740105,"about_ca_topic_score_gemma":0.012173668,"teacher_disagreement_score":0.01740105,"about_ca_system_score_codex":0.0016968935,"about_ca_system_score_gemma":0.0013715489,"threshold_uncertainty_score":0.034599483},"labels":[],"label_agreement":null},{"id":"W2312320697","doi":"10.1139/cjce-2014-0037","title":"Assessing the impact of renewal scenarios on the global structural state of sewer pipe networks","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"State (computer science); Civil engineering; Computer science; Engineering; Environmental science; Transport engineering","score_opus":0.00640942147352293,"score_gpt":0.20152894560378934,"score_spread":0.1951195241302664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312320697","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":[0.9924489,0.00005994128,0.0050966903,0.00011842675,0.0000048847096,0.000025315081,0.00022083924,0.000038051687,0.0019869753],"genre_scores_gemma":[0.9987061,0.000040833165,0.0008920587,0.0000077124605,8.767064e-7,0.0000056731556,0.00009053922,0.000003951733,0.00025229662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994609,0.00020525837,0.000015436188,0.0000611042,0.00009574484,0.00016146769],"domain_scores_gemma":[0.9979418,0.0012600044,0.00029188485,0.00012676089,0.00025798992,0.000121497615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017570138,0.0006345178,0.00045548606,0.0006407269,0.00039221483,0.00093226956,0.00082998927,0.00089832017,0.0010489338],"category_scores_gemma":[0.0052489527,0.0002861619,0.00071967975,0.00053027447,0.00093691994,0.0010778178,0.00070133444,0.00052326894,0.00005628573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041062394,0.000009330084,0.004062959,0.0000072113944,0.000011173456,0.00004143054,0.000009149694,0.9940188,0.0003715035,0.00057224603,0.000036711375,0.0008183993],"study_design_scores_gemma":[0.000016721917,0.00020064648,0.007829655,0.0000066761068,0.000048730733,0.000027017279,0.00018816788,0.9891451,0.001198689,0.0010744769,0.0002460558,0.00001811937],"about_ca_topic_score_codex":0.17945278,"about_ca_topic_score_gemma":0.193679,"teacher_disagreement_score":0.17945278,"about_ca_system_score_codex":0.004723388,"about_ca_system_score_gemma":0.002625637,"threshold_uncertainty_score":0.35681647},"labels":[],"label_agreement":null},{"id":"W2312327844","doi":"10.1061/41138(386)137","title":"Dynamic Simulation for Optimal Facility Sizing As Part of the Eastern New Mexico Rural Water Supply Project","year":2010,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Water supply; Pipeline transport; Water resource management; Population; Environmental science; Water scarcity; Land reclamation; Water resources; Environmental engineering; Agriculture; Geography; Archaeology","score_opus":0.010949731592409329,"score_gpt":0.22976449815912753,"score_spread":0.2188147665667182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312327844","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":[0.8350574,0.00038998306,0.10510558,0.0010342741,0.00009394613,0.0004108565,0.0022452252,0.0006285247,0.055034157],"genre_scores_gemma":[0.97438693,0.00011019158,0.019574376,0.00006114793,0.000008101106,0.00024134571,0.00056489505,0.000046328874,0.0050066686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983,0.00005929086,0.000004128609,0.000025656604,0.000025118787,0.000055690107],"domain_scores_gemma":[0.99911624,0.00058424973,0.00006729957,0.000022537672,0.00014408058,0.0000655395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006948827,0.00063808064,0.0007927174,0.0006220227,0.0006780825,0.0008606079,0.00072811637,0.0010009806,0.0060867346],"category_scores_gemma":[0.0015222578,0.0005835162,0.0006055993,0.00069249974,0.00042994577,0.00055584824,0.00060455437,0.00075587013,0.00022962556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020216388,0.000019027653,0.00041873378,0.000006196976,0.0000053862755,0.000013866312,0.0000065242716,0.99800724,0.000065612134,0.00041320012,0.00018455808,0.0008393997],"study_design_scores_gemma":[0.000009359971,0.000010233698,0.00016044895,0.0000014392099,0.0000021411183,8.7336133e-7,0.000010750363,0.99951935,0.000036986938,0.000082635845,0.00016457752,0.0000011918914],"about_ca_topic_score_codex":0.113969505,"about_ca_topic_score_gemma":0.08299804,"teacher_disagreement_score":0.113969505,"about_ca_system_score_codex":0.0021942966,"about_ca_system_score_gemma":0.0019582915,"threshold_uncertainty_score":0.22661227},"labels":[],"label_agreement":null},{"id":"W2312511878","doi":"10.1061/41024(340)23","title":"Water Distribution System Design and Environmental Impact: Balancing Local Interests with Broader Regional Concerns","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Index (typography); Environmental economics; Greenhouse gas; Environmental impact assessment; Environmental science; Resource (disambiguation); Renewable energy; Environmental resource management; Distribution (mathematics); Resource consumption; Computer science; Natural resource economics; Environmental engineering; Economics; Engineering; Mathematics","score_opus":0.007622715629913615,"score_gpt":0.1826787787033979,"score_spread":0.17505606307348429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312511878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04461803,0.0007898205,0.9362939,0.00015050558,0.000023240274,0.0001420989,0.00009053622,0.0001558814,0.017736103],"genre_scores_gemma":[0.6979135,0.0012264126,0.2937315,0.000044216144,0.00006568105,0.00028158558,0.00011638453,0.00014073896,0.006479968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977223,0.0009985802,0.000056321915,0.00014735648,0.0009795884,0.00009572059],"domain_scores_gemma":[0.99828464,0.0011068928,0.00018806547,0.00009138626,0.0002836142,0.000045483644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002354636,0.0008153691,0.00093714945,0.0017806005,0.00043066978,0.0021597287,0.0006311411,0.00052344106,0.0034252927],"category_scores_gemma":[0.005901556,0.00033255765,0.00040908594,0.0026429913,0.0008807153,0.002358563,0.00093546376,0.00058728096,0.00020109082],"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":[0.00017168722,0.000103823884,0.0058124866,0.00054071745,0.00011402637,0.00011797235,0.00024658794,0.6254278,0.010403987,0.06906282,0.0012870557,0.28671104],"study_design_scores_gemma":[0.000031075895,0.00035313232,0.004866996,0.00007592203,0.00009122433,0.00014241654,0.00036489312,0.9369454,0.0083544785,0.039277896,0.009448829,0.00004781762],"about_ca_topic_score_codex":0.002174518,"about_ca_topic_score_gemma":0.0048983367,"teacher_disagreement_score":0.0034252927,"about_ca_system_score_codex":0.00172787,"about_ca_system_score_gemma":0.0010860267,"threshold_uncertainty_score":0.012536585},"labels":[],"label_agreement":null},{"id":"W2313704979","doi":"10.1061/40792(173)57","title":"Water Distribution System Reliability Under a Fire Flow Condition: A Probabilistic Approach","year":2005,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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); Probabilistic logic; Flow (mathematics); Monte Carlo method; Computer science; Probability distribution; Reliability engineering; Environmental science; Engineering; Mathematics; Statistics","score_opus":0.006889881178795965,"score_gpt":0.17510633214740895,"score_spread":0.16821645096861298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313704979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051000264,0.0005802447,0.9444355,0.0003585836,0.000013982382,0.000062225554,0.00011530187,0.00015592888,0.0032780434],"genre_scores_gemma":[0.9333122,0.00093823945,0.064244315,0.000043883552,0.000072929986,0.0001630119,0.00012343792,0.000047793135,0.0010542856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980014,0.0010193426,0.00007327461,0.0002187148,0.00052703184,0.00016026411],"domain_scores_gemma":[0.99303013,0.0054242215,0.00067645404,0.0002617703,0.0005374344,0.00006999176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046076975,0.00089199276,0.0008467831,0.002678195,0.00063174,0.0012027508,0.0013147909,0.001250765,0.0014198391],"category_scores_gemma":[0.013977079,0.0008784993,0.0013399759,0.0012873117,0.0017481111,0.002386125,0.001058977,0.00094298564,0.00015630321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000138797595,0.0000068771483,0.00056493055,0.00002127549,0.000014820609,0.000033259636,0.000028612487,0.9847216,0.0002674714,0.010206676,0.00007750033,0.00404317],"study_design_scores_gemma":[0.0000031064342,0.00001978878,0.0003913916,0.000007856165,0.000009162958,0.000025481515,0.000014003191,0.9904259,0.00024210081,0.008693465,0.0001590294,0.000008776723],"about_ca_topic_score_codex":0.0049482533,"about_ca_topic_score_gemma":0.0038425236,"teacher_disagreement_score":0.0049482533,"about_ca_system_score_codex":0.0018393032,"about_ca_system_score_gemma":0.0009484812,"threshold_uncertainty_score":0.024368107},"labels":[],"label_agreement":null},{"id":"W2315466686","doi":"10.1061/9780784479360.078","title":"The Need for Holistic System-Wide Transient Assessment","year":2015,"lang":"en","type":"article","venue":"Pipelines 2015","topic":"Water Systems and 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":"Canadian Sleep Society; Aecom (Canada)","funders":"","keywords":"Transient (computer programming); Benchmarking; Pipeline (software); Process (computing); Computer science; Risk analysis (engineering); Reliability engineering; Best practice; Systems engineering; Engineering; Business","score_opus":0.038720950468315196,"score_gpt":0.2769324707561136,"score_spread":0.2382115202877984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315466686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064705886,0.003584032,0.9005391,0.004916002,0.00016061407,0.00073009316,0.00082544883,0.002098878,0.022439973],"genre_scores_gemma":[0.5937113,0.0036133958,0.39828464,0.0006829916,0.000113600276,0.00042936372,0.00083487376,0.00025533437,0.0020744158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99617887,0.0013569468,0.00034239644,0.00040332813,0.00147902,0.00023943004],"domain_scores_gemma":[0.99269,0.0021948859,0.00072589866,0.0010272448,0.0029965204,0.00036542394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008070718,0.0011899592,0.0018640654,0.0030547713,0.0008086933,0.0048147747,0.0022832288,0.0016240381,0.0021489596],"category_scores_gemma":[0.012161265,0.0005967373,0.0008823731,0.0021209538,0.0011188611,0.008403205,0.003667876,0.001856359,0.0004941975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010268017,0.0002636526,0.017737223,0.0016522859,0.0004023866,0.00044983486,0.0009649779,0.53171825,0.016828964,0.03769501,0.0067107514,0.38547397],"study_design_scores_gemma":[0.00004743411,0.0011223537,0.023958571,0.0012242405,0.00041835534,0.00062137295,0.009430247,0.723084,0.00910481,0.16663614,0.06399882,0.0003537031],"about_ca_topic_score_codex":0.004996345,"about_ca_topic_score_gemma":0.006530319,"teacher_disagreement_score":0.008070718,"about_ca_system_score_codex":0.0020562587,"about_ca_system_score_gemma":0.004781932,"threshold_uncertainty_score":0.042682588},"labels":[],"label_agreement":null},{"id":"W2315735438","doi":"10.1139/l2012-066","title":"Design of a centralized regional water distribution system: a case study in the County of Paintearth No. 18, Alberta, Canada","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Alberta; Cochrane; WSP (Canada)","funders":"","keywords":"Water supply; Environmental science; Per capita; Environmental engineering; Water resource management; Water quality; Population; Pipeline transport; Range (aeronautics); Hydrology (agriculture); Engineering","score_opus":0.012325141826990267,"score_gpt":0.16921521853556862,"score_spread":0.15689007670857835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315735438","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":[0.9729171,0.0002013936,0.009394966,0.00031299278,0.00001890072,0.0007396795,0.00041815234,0.00022091101,0.015775992],"genre_scores_gemma":[0.9840735,0.00012225904,0.0075208414,0.00003850362,0.0000043906493,0.000107276894,0.00022833915,0.000025358946,0.007879561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990971,0.00016973328,0.000023137756,0.0001235121,0.00022054432,0.0003659848],"domain_scores_gemma":[0.9990495,0.0002277616,0.00004687771,0.000031207313,0.0004046555,0.00023995334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008623403,0.0007623477,0.000550306,0.0007539389,0.003271556,0.002042097,0.0017811692,0.0013033486,0.0042562885],"category_scores_gemma":[0.0012758408,0.00048767088,0.00040855614,0.0010686809,0.0012829615,0.00045224794,0.0007139185,0.0004995353,0.00033430854],"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":[0.00087192684,0.0013316502,0.03480975,0.00039381676,0.00008901969,0.005818964,0.0013590656,0.8913839,0.009813248,0.005784414,0.0048872735,0.043456852],"study_design_scores_gemma":[0.0007256057,0.0018001731,0.03866803,0.00007879219,0.00016324113,0.00044081997,0.0148220835,0.9264285,0.0045646434,0.0015508372,0.010624359,0.00013289457],"about_ca_topic_score_codex":0.90010995,"about_ca_topic_score_gemma":0.93551725,"teacher_disagreement_score":0.09989005,"about_ca_system_score_codex":0.02086284,"about_ca_system_score_gemma":0.02030706,"threshold_uncertainty_score":0.20095658},"labels":[],"label_agreement":null},{"id":"W2315954672","doi":"10.1061/41203(425)95","title":"How Much Is \"Your Un-Calibrated Model\" Costing Your Utility? Ten Lessons Learned from Calibrating the CRD Water Model","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"GeoAdvice Engineering (Canada); Capital Regional District","funders":"","keywords":"Calibration; Computer science; Process (computing); Field (mathematics); Residual; Water model; Simulation; Statistics; Mathematics","score_opus":0.14661219774695172,"score_gpt":0.2409548213438582,"score_spread":0.09434262359690648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315954672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1542386,0.004556009,0.4415596,0.28612682,0.0030455159,0.0003632057,0.0015934847,0.003122617,0.10539419],"genre_scores_gemma":[0.7226416,0.0053373445,0.24220668,0.007973102,0.00055860286,0.00015321243,0.0009313556,0.0014992269,0.01869882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949909,0.0017675039,0.00022771872,0.00045108757,0.0022454462,0.0003173584],"domain_scores_gemma":[0.98685664,0.0040108394,0.00057407876,0.0020504743,0.0056629665,0.0008449822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008296654,0.000756828,0.0006590799,0.00087514083,0.0011288647,0.005854644,0.0023474176,0.0013667777,0.0057192002],"category_scores_gemma":[0.030687714,0.00045728934,0.0005515085,0.0011502776,0.0019163559,0.009290431,0.0031158703,0.004275579,0.0015238668],"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":[0.0002735464,0.0003457458,0.024127671,0.0005810578,0.00013215041,0.000841674,0.0017344485,0.17371644,0.0033487927,0.15532191,0.14699408,0.49258256],"study_design_scores_gemma":[0.00011395934,0.00042830248,0.011371605,0.001450764,0.0001527244,0.0011223869,0.007046986,0.31936163,0.010256016,0.23499866,0.4133172,0.00037988005],"about_ca_topic_score_codex":0.022523887,"about_ca_topic_score_gemma":0.025370656,"teacher_disagreement_score":0.022523887,"about_ca_system_score_codex":0.0048620873,"about_ca_system_score_gemma":0.006274447,"threshold_uncertainty_score":0.04478556},"labels":[],"label_agreement":null},{"id":"W2315985130","doi":"10.1061/9780784413548.039","title":"Challenges and Opportunities in Hydraulic Modeling during Business Transformation","year":2014,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2014","topic":"Water Systems and 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":"American Water (Canada)","funders":"","keywords":"SCADA; Computer science; Asset management; Geographic information system; Engineering; Business; Geography; Finance","score_opus":0.014492842637681193,"score_gpt":0.1599649600454638,"score_spread":0.1454721174077826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315985130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06609138,0.003181569,0.80281895,0.063618064,0.0005143941,0.0001723933,0.00059069967,0.0010402935,0.061972182],"genre_scores_gemma":[0.7635138,0.005487933,0.21683776,0.0014055981,0.00044290014,0.000316131,0.0004008167,0.0003674346,0.011227645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99712676,0.0019171569,0.00011888808,0.00024718905,0.00043150186,0.00015857816],"domain_scores_gemma":[0.99477696,0.0037019423,0.00028074346,0.00035948132,0.0006367288,0.00024427962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004870608,0.0006351262,0.0008226301,0.00080636714,0.0016621548,0.0047570644,0.0019058844,0.0030184316,0.0026025118],"category_scores_gemma":[0.010238657,0.0006779401,0.00081399427,0.0017070196,0.0026326391,0.007553201,0.0025047078,0.003193216,0.0006017539],"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":[0.000034424294,0.000096380005,0.0033121784,0.0001240686,0.000020112398,0.00026391947,0.00092066755,0.6934906,0.00044577944,0.24223417,0.0071025956,0.051955067],"study_design_scores_gemma":[0.000010106143,0.000021699045,0.00038372807,0.00004748438,0.000004831642,0.000055347657,0.0012218612,0.8638644,0.00029029098,0.11593292,0.018137354,0.000029927898],"about_ca_topic_score_codex":0.019081077,"about_ca_topic_score_gemma":0.014679861,"teacher_disagreement_score":0.019081077,"about_ca_system_score_codex":0.0030110234,"about_ca_system_score_gemma":0.0038710344,"threshold_uncertainty_score":0.037940025},"labels":[],"label_agreement":null},{"id":"W231604502","doi":"10.21236/ada554944","title":"New Lock for Soo Locks and Dam, Model Investigations During 2006-2010, Sault Ste. Marie, Michigan, St. Mary's River: Hydraulic Model Investigation","year":2012,"lang":"en","type":"report","venue":"","topic":"Water Systems and 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":"Coastal and Hydraulics Laboratory; Engineer Research and Development Center; U.S. Army Corps of Engineers","keywords":"Lock (firearm); Geography; Geology; Hydrology (agriculture); Archaeology; Geotechnical engineering","score_opus":0.03662481304410705,"score_gpt":0.2187653017312948,"score_spread":0.18214048868718774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W231604502","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":[0.85026914,0.0004473854,0.011842399,0.0028336179,0.00014218429,0.00017595934,0.082310244,0.00083793374,0.051141042],"genre_scores_gemma":[0.8888775,0.0006610513,0.008481647,0.00017405266,0.00003746518,0.0002205429,0.047411844,0.0005298666,0.053605974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997298,0.00003987747,0.000010996181,0.000039393864,0.00014332516,0.00003657408],"domain_scores_gemma":[0.9996086,0.00008223824,0.000042974254,0.00003648688,0.00016617287,0.00006342165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062543544,0.0004445897,0.00042185848,0.0006362725,0.0010168096,0.0011947147,0.0009732781,0.001022394,0.004537622],"category_scores_gemma":[0.0014363608,0.0007481178,0.00049951696,0.0014056606,0.00027671375,0.0017972249,0.00045699647,0.0007813363,0.0009983558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001203215,0.0005673109,0.20852526,0.000287265,0.0002531915,0.0009229179,0.0008366512,0.49403402,0.0071332976,0.005482509,0.23875766,0.04199669],"study_design_scores_gemma":[0.00031987898,0.00028597,0.22415765,0.00012613268,0.00015851045,0.0002935411,0.0023850775,0.6726263,0.007794955,0.0018545955,0.089862734,0.00013464416],"about_ca_topic_score_codex":0.41867128,"about_ca_topic_score_gemma":0.76237744,"teacher_disagreement_score":0.41867128,"about_ca_system_score_codex":0.0031988018,"about_ca_system_score_gemma":0.0044978694,"threshold_uncertainty_score":0.8324687},"labels":[],"label_agreement":null},{"id":"W2316046757","doi":"10.1061/41036(342)36","title":"Application of Optimization Technology to Water Distribution System Master Planning","year":2009,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2009","topic":"Water Systems and 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":"Wilfrid Laurier University; St. Michael's Hospital","funders":"University of Exeter","keywords":"Variety (cybernetics); Computer science; Reliability (semiconductor); Plan (archaeology); Process (computing); Iterative and incremental development; Optimization problem; Mathematical optimization; Algorithm","score_opus":0.003407901377645831,"score_gpt":0.16658379712212437,"score_spread":0.16317589574447855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316046757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032100934,0.0005478438,0.9875218,0.0002482293,0.000032413092,0.000040441708,0.00002320076,0.00014442719,0.008231474],"genre_scores_gemma":[0.27010328,0.002557952,0.7199815,0.00014633805,0.00008607553,0.00035447985,0.0000962929,0.0002343114,0.00643978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991912,0.00042893377,0.000031333122,0.00007877634,0.00022180435,0.00004796393],"domain_scores_gemma":[0.99907684,0.0007177078,0.000062903746,0.000040484545,0.00008967468,0.0000123484315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001180196,0.00086952443,0.00079260516,0.0011262113,0.0006047886,0.0011268551,0.0004749772,0.000501861,0.0025530655],"category_scores_gemma":[0.0028849246,0.0006656341,0.0008276315,0.0014139654,0.0013289725,0.0008473017,0.0008092503,0.0012455205,0.0004221522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019220133,0.00001676151,0.00022445331,0.0001004253,0.000046238278,0.00004265373,0.000060411654,0.85887843,0.0010702902,0.0677941,0.0006888951,0.0710582],"study_design_scores_gemma":[0.000014666119,0.000033780572,0.00016557013,0.000040136467,0.000018366598,0.000024006566,0.000030613057,0.9156786,0.0018376657,0.0722317,0.009908289,0.000016462362],"about_ca_topic_score_codex":0.006801583,"about_ca_topic_score_gemma":0.004121345,"teacher_disagreement_score":0.006801583,"about_ca_system_score_codex":0.0014047588,"about_ca_system_score_gemma":0.0016394773,"threshold_uncertainty_score":0.013523996},"labels":[],"label_agreement":null},{"id":"W2316124702","doi":"10.1061/9780784479162.072","title":"Minimum Pressure Criterion in Water Distribution Systems: Challenges and Consequences","year":2015,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2015","topic":"Water Systems and 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":"McMaster University; University of Toronto","funders":"","keywords":"Leakage (economics); Risk analysis (engineering); Environmental science; Computer science; Water supply; Reliability engineering; Control theory (sociology); Engineering; Business; Control (management); Environmental engineering; Economics","score_opus":0.013482893513966171,"score_gpt":0.18462552847416333,"score_spread":0.17114263496019716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316124702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07974979,0.0070565343,0.86204976,0.015769571,0.00042043865,0.00016896502,0.00022063963,0.00043360319,0.034130793],"genre_scores_gemma":[0.9317245,0.0026599702,0.058929462,0.00057059247,0.00043535995,0.00017321519,0.00009964496,0.00011709077,0.005290146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99526453,0.0016411082,0.00024931287,0.00044307474,0.0020500994,0.00035195885],"domain_scores_gemma":[0.99569654,0.0024511993,0.00049943576,0.00018471398,0.0010075336,0.00016055745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032337967,0.0005839151,0.0009467238,0.0005255082,0.0009478886,0.0020358602,0.0013916522,0.0022576046,0.00173062],"category_scores_gemma":[0.009047022,0.00047758291,0.00045156322,0.0006061687,0.0017797914,0.0035046192,0.0016466429,0.0025468983,0.00037184157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001646632,0.00012188467,0.00282786,0.00087614584,0.00006571216,0.00072846666,0.0004168501,0.64926976,0.007636988,0.21642855,0.009066735,0.11239645],"study_design_scores_gemma":[0.00005451239,0.00039715457,0.0027449136,0.00020686339,0.00002581585,0.00041492956,0.00056214543,0.6464199,0.0055021555,0.32150143,0.022082733,0.00008749863],"about_ca_topic_score_codex":0.0028580774,"about_ca_topic_score_gemma":0.0012805016,"teacher_disagreement_score":0.0032337967,"about_ca_system_score_codex":0.0017720762,"about_ca_system_score_gemma":0.00189859,"threshold_uncertainty_score":0.017102122},"labels":[],"label_agreement":null},{"id":"W2316277882","doi":"10.1061/9780784412312.302","title":"Contaminant Intrusion in Water Distribution Systems: An Ingress Model","year":2012,"lang":"en","type":"article","venue":"World Environmental And Water Resources Congress 2012","topic":"Water Systems and 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, Okanagan Campus; University of British Columbia","funders":"","keywords":"Intrusion; Dimensionless quantity; Factorial experiment; Environmental science; Set (abstract data type); Computer science; Statistics; Geotechnical engineering; Mathematics; Soil science; Geology; Mechanics","score_opus":0.006468667433485093,"score_gpt":0.16968139284378253,"score_spread":0.16321272541029744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316277882","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":[0.5152304,0.0011503357,0.44076768,0.0014870515,0.00016191976,0.00031504768,0.0010494697,0.00069684174,0.039141223],"genre_scores_gemma":[0.9708554,0.00046929382,0.008777612,0.00009378612,0.0000317427,0.0002170892,0.00022654062,0.00005994331,0.019268556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996145,0.000102070444,0.000017152017,0.00008860619,0.000078745026,0.00009894969],"domain_scores_gemma":[0.9993955,0.0003003602,0.0000998875,0.000020318705,0.00012707697,0.000056895187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006736863,0.0011496115,0.0013168907,0.00075209857,0.00084211177,0.0015461198,0.0019794465,0.0024929282,0.0031996185],"category_scores_gemma":[0.0015654408,0.00071764836,0.0012365562,0.00085127965,0.00094668684,0.0012427985,0.0014024049,0.001466006,0.00047624414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042711905,0.000033045704,0.00041913855,0.000024663546,0.000010115443,0.000074587566,0.000033012977,0.9948468,0.0007432859,0.0024015543,0.00013572002,0.0012352613],"study_design_scores_gemma":[0.000007325076,0.000018586345,0.00006490094,0.0000018281717,0.00000648712,0.000004205242,0.000008154725,0.9993349,0.0001072059,0.00030100177,0.00014242814,0.0000029842558],"about_ca_topic_score_codex":0.026905634,"about_ca_topic_score_gemma":0.009160792,"teacher_disagreement_score":0.026905634,"about_ca_system_score_codex":0.0015106982,"about_ca_system_score_gemma":0.0013211394,"threshold_uncertainty_score":0.05349803},"labels":[],"label_agreement":null},{"id":"W2316334967","doi":"10.1061/40737(2004)477","title":"Identifying Dependencies in Input Data in Hydraulic Network Problems","year":2004,"lang":"en","type":"article","venue":"Critical Transitions in Water and Environmental Resources Management","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Dependency (UML); SCADA; Computer science; Solver; Preprocessor; Component (thermodynamics); Data mining; State (computer science); Dependency theory (database theory); Algorithm; Reliability engineering; Functional dependency; Artificial intelligence; Engineering; Programming language","score_opus":0.017954483047203974,"score_gpt":0.21262687157997462,"score_spread":0.19467238853277063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316334967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032635026,0.00013168354,0.9647442,0.00025910386,0.000019577357,0.000046850375,0.00018879802,0.0003469174,0.0016277775],"genre_scores_gemma":[0.6141595,0.00039454302,0.38144135,0.0001124321,0.000082850616,0.00022178321,0.0007846226,0.00029338058,0.0025094762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982875,0.00082862604,0.00011042954,0.0001967724,0.0004523118,0.00012430902],"domain_scores_gemma":[0.98882234,0.009502189,0.00050240604,0.0004129628,0.00065894355,0.000101153026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026854044,0.0009576488,0.00084285834,0.00079259276,0.00080652925,0.0013548331,0.0009024407,0.0010865171,0.0021719867],"category_scores_gemma":[0.018566646,0.00096812035,0.00047451854,0.0009996245,0.0011552343,0.002183558,0.0016555791,0.0023500312,0.0002779125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000142774,0.000044967885,0.0024581598,0.00017359985,0.000026732636,0.0003882366,0.0002552424,0.88079816,0.0016425792,0.048289374,0.0016662908,0.064113855],"study_design_scores_gemma":[0.000007589791,0.00001558263,0.00028146204,0.0000122634865,0.0000040965415,0.000024304918,0.000019616797,0.97515535,0.0014440715,0.0220931,0.0009358718,0.000006738305],"about_ca_topic_score_codex":0.004134985,"about_ca_topic_score_gemma":0.0042328555,"teacher_disagreement_score":0.004134985,"about_ca_system_score_codex":0.00079061935,"about_ca_system_score_gemma":0.0017586439,"threshold_uncertainty_score":0.014201939},"labels":[],"label_agreement":null},{"id":"W2316353479","doi":"10.1061/40792(173)50","title":"A Systematic Exploration of Uncertainty and Convergence of Inverse Transient Calibration for WDSs","year":2005,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Particle swarm optimization; Calibration; Mathematical optimization; Computer science; Convergence (economics); Evolutionary algorithm; Premature convergence; Global optimization; Process (computing); Genetic algorithm; Local optimum; Transient (computer programming); Evolutionary computation; Algorithm; Mathematics; Statistics","score_opus":0.017147378515654975,"score_gpt":0.2015458475055922,"score_spread":0.18439846898993723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316353479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06229671,0.0007293407,0.9313749,0.00038395458,0.000015610143,0.000040373958,0.000024105813,0.00018637667,0.004948667],"genre_scores_gemma":[0.77509034,0.0008393629,0.22297642,0.00005069059,0.000019768031,0.000069322436,0.000045055054,0.00012386248,0.0007852157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981231,0.000919574,0.00008943003,0.00013293266,0.00067838514,0.000056515408],"domain_scores_gemma":[0.991347,0.0066559305,0.0005377955,0.00056162174,0.0008566625,0.00004089134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061015254,0.00069990556,0.00070308073,0.00123116,0.00054942915,0.0008380202,0.00050399284,0.00064779754,0.0005580061],"category_scores_gemma":[0.021257177,0.00043841655,0.0005194784,0.0007139161,0.0013853275,0.0014858709,0.00076108787,0.0010758789,0.0000826266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008170112,0.000035195153,0.0045043975,0.00023962095,0.000043716027,0.000114231705,0.00037596814,0.8519849,0.008093854,0.037010673,0.0002522235,0.097263396],"study_design_scores_gemma":[0.0000063501548,0.00006407988,0.0012261415,0.00008044204,0.000009602449,0.000088757886,0.00006705735,0.9777179,0.00961876,0.009882126,0.0012157245,0.000023063609],"about_ca_topic_score_codex":0.0023360383,"about_ca_topic_score_gemma":0.0014181768,"teacher_disagreement_score":0.0061015254,"about_ca_system_score_codex":0.0006975324,"about_ca_system_score_gemma":0.00093871105,"threshold_uncertainty_score":0.032268286},"labels":[],"label_agreement":null},{"id":"W2316434383","doi":"10.1061/40976(316)673","title":"Life-Cycle Cost Based Rehabilitation Plan for Water Mains","year":2008,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2008","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Rehabilitation; Trench; Computer science; Engineering; Marine engineering; Environmental science; Materials science","score_opus":0.0071550485233767185,"score_gpt":0.16171938434895275,"score_spread":0.15456433582557602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316434383","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":[0.6703986,0.0008625207,0.20511216,0.0019666906,0.000088491324,0.008583065,0.013014078,0.0018040934,0.09817028],"genre_scores_gemma":[0.9425646,0.00025393866,0.039301246,0.00009403405,0.000007231067,0.0014224699,0.003794127,0.00005560531,0.012506889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871576,0.00032440884,0.0000476359,0.00012182735,0.00045102034,0.00033940218],"domain_scores_gemma":[0.9989152,0.00019704904,0.00018877618,0.00006326624,0.00049326685,0.00014250702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012997679,0.00081153953,0.00047885656,0.002296679,0.0007981658,0.0013615354,0.0011762657,0.0011148588,0.009507951],"category_scores_gemma":[0.002886114,0.0005655564,0.00072470354,0.0017405536,0.00049833657,0.00092096225,0.00054041814,0.0007099769,0.0010794717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015068997,0.00025421596,0.0052986247,0.00009351112,0.000023664992,0.0001272746,0.000060908766,0.9505413,0.0028680353,0.007742067,0.0039620353,0.028877633],"study_design_scores_gemma":[0.00008786118,0.000633456,0.015817242,0.00006559509,0.00005315164,0.00008504891,0.0004714766,0.9626705,0.0022024536,0.0061071245,0.011745881,0.000060132163],"about_ca_topic_score_codex":0.080394916,"about_ca_topic_score_gemma":0.10336979,"teacher_disagreement_score":0.080394916,"about_ca_system_score_codex":0.007432118,"about_ca_system_score_gemma":0.009062198,"threshold_uncertainty_score":0.15985394},"labels":[],"label_agreement":null},{"id":"W2316722995","doi":"10.1061/41069(360)35","title":"Comparison and Analysis of Water Main Performance Models","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Statistical model; Computer science; Breakage; Poisson distribution; Operations research; Engineering; Data mining; Statistics; Mathematics; Artificial intelligence","score_opus":0.012890341312988083,"score_gpt":0.20324434603468985,"score_spread":0.19035400472170177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316722995","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":[0.5610485,0.0019860954,0.3885479,0.0016659484,0.00014930929,0.0004068058,0.0071230535,0.0036823486,0.03539002],"genre_scores_gemma":[0.9761437,0.00068264024,0.016338825,0.00006148228,0.000025088048,0.00015521298,0.002850042,0.00018008798,0.0035628818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998784,0.00037689862,0.00006627711,0.00021185046,0.00037360282,0.00018725499],"domain_scores_gemma":[0.99070215,0.007106651,0.0004830951,0.00035684253,0.001238593,0.0001125919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030243665,0.0010955,0.0010228052,0.00211352,0.0004097854,0.0013976197,0.0015169899,0.000995074,0.0039286558],"category_scores_gemma":[0.010860362,0.0004203019,0.0014260135,0.0018933852,0.00037953522,0.001319773,0.0005742999,0.000825074,0.0006890935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005803388,0.000027640153,0.0020061156,0.000044196902,0.00003296884,0.000021518928,0.000024693367,0.984914,0.0001589903,0.0017703689,0.0007427443,0.010198735],"study_design_scores_gemma":[0.000005525523,0.00003387803,0.0011564848,0.000008064154,0.00001408745,0.000008494171,0.000023238665,0.997008,0.00020251454,0.001061487,0.00046976557,0.000008451378],"about_ca_topic_score_codex":0.05306978,"about_ca_topic_score_gemma":0.018479139,"teacher_disagreement_score":0.05306978,"about_ca_system_score_codex":0.0027976872,"about_ca_system_score_gemma":0.0019579364,"threshold_uncertainty_score":0.10552174},"labels":[],"label_agreement":null},{"id":"W2316836625","doi":"10.2166/ws.2013.033","title":"Effect of seasonal climatic variance on water main failure frequencies in moderate climate regions","year":2013,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and Optimization","field":"Engineering","cited_by":27,"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":"Environmental science; Climatology; Seasonality; Climate change; Physical geography; Geography; Geology; Statistics; Mathematics","score_opus":0.0036680892212858896,"score_gpt":0.18575787839438437,"score_spread":0.18208978917309848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316836625","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":[0.9990706,0.000059442944,0.00025759346,0.000011382695,0.0000045546353,0.000002965912,0.0002646893,0.000011914171,0.0003168835],"genre_scores_gemma":[0.9996593,0.000011947916,0.000033009488,0.0000024642402,0.0000032381324,0.0000017818588,0.00023323172,0.000004625441,0.000050394174],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994438,0.00014566498,0.000035287823,0.00018718743,0.00007005805,0.00011796174],"domain_scores_gemma":[0.9975594,0.00111006,0.00056426873,0.0002743394,0.00020328637,0.00028862225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008494941,0.00020174781,0.00037741513,0.0005169661,0.00027967265,0.0007456292,0.00021071846,0.0002744225,0.0013151604],"category_scores_gemma":[0.002099561,0.000098940975,0.0006509257,0.0006553018,0.00031009037,0.0002438247,0.00045791134,0.0002696079,0.00020982267],"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":[0.0004305597,0.000057326768,0.98635525,0.000023495602,0.00023257875,0.00025757134,0.0002904638,0.0038530934,0.0041049384,0.00014485655,0.00025160663,0.0039982195],"study_design_scores_gemma":[0.0000014596743,0.000029152563,0.998796,0.0000013373664,0.000011331615,0.000024348565,0.000100681464,0.00082586723,0.000099584344,0.000016538314,0.00009080542,0.0000027898693],"about_ca_topic_score_codex":0.0074541033,"about_ca_topic_score_gemma":0.0066458583,"teacher_disagreement_score":0.0074541033,"about_ca_system_score_codex":0.0002433101,"about_ca_system_score_gemma":0.00021484798,"threshold_uncertainty_score":0.01482141},"labels":[],"label_agreement":null},{"id":"W2317564448","doi":"10.1061/40644(2002)85","title":"Benign Urban Water Systems: Optimal Complexity, Asynchronous Learning, and User Performance Improvement","year":2002,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Computer science; Asynchronous learning; Asynchronous communication; The Internet; Control (management); Quality (philosophy); Human–computer interaction; Multimedia; Knowledge management; World Wide Web; Artificial intelligence; Teaching method; Cooperative learning; Synchronous learning; Mathematics education","score_opus":0.011123732599725506,"score_gpt":0.17120201814094135,"score_spread":0.16007828554121584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317564448","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":[0.9141605,0.00024367122,0.075904444,0.00030929258,0.000010532858,0.0001456395,0.00002589034,0.00024132027,0.008958763],"genre_scores_gemma":[0.9809816,0.000088658606,0.018196762,0.000021137861,0.000008199146,0.000048394184,0.000024665054,0.000011147181,0.000619403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992378,0.0003980388,0.00004392515,0.000074153424,0.0001989326,0.000047189747],"domain_scores_gemma":[0.99717903,0.0020894322,0.00030826754,0.00016679754,0.00012113897,0.00013532602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013175418,0.00033573608,0.00020892173,0.00038501833,0.00027296564,0.0009037133,0.00036237287,0.00032229206,0.0012511421],"category_scores_gemma":[0.0056852624,0.00010984137,0.0001614451,0.0003695875,0.00046093488,0.00094887894,0.00088002457,0.00026685034,0.00013512568],"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":[0.002549235,0.0030255609,0.09583164,0.0008514997,0.000064821164,0.00049513404,0.0033898416,0.067659646,0.03679246,0.024451364,0.0010985113,0.7637903],"study_design_scores_gemma":[0.00054487656,0.011579378,0.21282779,0.00013933476,0.00025915657,0.001133733,0.0034125072,0.58584785,0.10528764,0.066122875,0.012712958,0.00013182341],"about_ca_topic_score_codex":0.00065575825,"about_ca_topic_score_gemma":0.00069692044,"teacher_disagreement_score":0.0013175418,"about_ca_system_score_codex":0.0004474743,"about_ca_system_score_gemma":0.00041543576,"threshold_uncertainty_score":0.006967902},"labels":[],"label_agreement":null},{"id":"W2317826432","doi":"10.1061/41024(340)29","title":"Design of Drinking Water Distribution Networks with Consideration of Future Retrofit","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Robustness (evolution); Urbanization; Network planning and design; Computer science; Water conservation; Population; Risk analysis (engineering); Environmental economics; Water resources; Business; Economics","score_opus":0.0056166250301784,"score_gpt":0.16179217402274831,"score_spread":0.1561755489925699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317826432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04262939,0.0003087038,0.9471609,0.0003084963,0.000035591496,0.00016502952,0.00013131714,0.00019699433,0.009063502],"genre_scores_gemma":[0.8523689,0.0005729908,0.14097825,0.00008173403,0.00003255176,0.00034996992,0.00015454907,0.000079814636,0.0053812414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989473,0.0003647599,0.00003911336,0.00024178406,0.00025593466,0.00015118488],"domain_scores_gemma":[0.9991146,0.0003163325,0.0002327834,0.000043778142,0.00022379367,0.0000687581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013214194,0.0010602686,0.00070113526,0.00088536274,0.0005596141,0.0013400721,0.0015077378,0.0012336887,0.0022832637],"category_scores_gemma":[0.0028691033,0.00064980966,0.0006883033,0.0006174033,0.00074434915,0.00132541,0.0010760219,0.0006829014,0.0003027049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030790095,0.000014513478,0.00042287578,0.000035772257,0.000014058818,0.00005635983,0.000029959212,0.9815599,0.001776238,0.004683166,0.00016853979,0.011207787],"study_design_scores_gemma":[0.000013414802,0.000074935306,0.00022648148,0.000013500959,0.000017827557,0.000028843606,0.000033836535,0.99214864,0.0011674355,0.004433094,0.0018313413,0.000010571487],"about_ca_topic_score_codex":0.0038239008,"about_ca_topic_score_gemma":0.0035527572,"teacher_disagreement_score":0.0038239008,"about_ca_system_score_codex":0.0018771483,"about_ca_system_score_gemma":0.0013227698,"threshold_uncertainty_score":0.013619721},"labels":[],"label_agreement":null},{"id":"W2317938918","doi":"10.1061/41203(425)51","title":"An Unsteady Optimization Algorithm for Water Distribution Systems Including Hydraulics and Water Quality Criteria","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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, Okanagan Campus; University of British Columbia","funders":"","keywords":"Hydraulics; Turbidity; Mechanics; Flow (mathematics); Pipeline (software); Water flow; Particle swarm optimization; Turbulence; Momentum (technical analysis); Grid; Pipeline transport; Surge; Water quality; Transient (computer programming); Computer science; Environmental science; Engineering; Algorithm; Mathematics; Geotechnical engineering; Physics; Meteorology; Mechanical engineering; Geology; Thermodynamics; Geometry","score_opus":0.045227269577447604,"score_gpt":0.2652040285340366,"score_spread":0.21997675895658897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317938918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006902643,0.00010464221,0.9914,0.00009355027,0.000016898271,0.00003835412,0.000020285315,0.000117821386,0.0013058551],"genre_scores_gemma":[0.36683515,0.0002541324,0.6266488,0.00011576661,0.000036761212,0.00047266908,0.00017717434,0.00011986021,0.0053396835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969304,0.0001260787,0.000017914397,0.000054115677,0.000071445844,0.00003743689],"domain_scores_gemma":[0.9995152,0.00028804745,0.000054664226,0.000018321958,0.00010428618,0.000019473368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011391969,0.0007927567,0.0009978167,0.0006085505,0.0005254045,0.00092879933,0.00080267194,0.001111369,0.0018518849],"category_scores_gemma":[0.0016705204,0.0005391869,0.00059806084,0.0006792966,0.00060148066,0.00092647044,0.00069181935,0.00090209505,0.00031400178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015004433,0.000014783326,0.00016326747,0.000024972036,0.000012472564,0.000012760136,0.000022864611,0.9810321,0.0003018513,0.0039647915,0.0002365384,0.014198633],"study_design_scores_gemma":[0.0000021735616,0.0000057491875,0.000013362227,0.0000014667748,9.227123e-7,0.0000012838649,0.0000022360955,0.9992575,0.00005397966,0.00048370607,0.00017678126,8.1348037e-7],"about_ca_topic_score_codex":0.013330816,"about_ca_topic_score_gemma":0.009607551,"teacher_disagreement_score":0.013330816,"about_ca_system_score_codex":0.0010381483,"about_ca_system_score_gemma":0.0018989361,"threshold_uncertainty_score":0.026506424},"labels":[],"label_agreement":null},{"id":"W2318965785","doi":"10.1557/opl.2012.117","title":"Statistical Analysis Based on Information Obtained from Inspection in Line to Forecast Future Land Pipeline Damage","year":2012,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Water Systems and 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":"École de Technologie Supérieure","funders":"","keywords":"Pipeline (software); Pipeline transport; Fractal; Artificial neural network; Series (stratigraphy); Fractal analysis; Time series; Software; Data mining; Computer science; Operations research; Fractal dimension; Artificial intelligence; Engineering; Machine learning; Geology; Mathematics; Mechanical engineering","score_opus":0.005566058890630859,"score_gpt":0.1961654125529387,"score_spread":0.19059935366230785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2318965785","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":[0.84051484,0.00023679345,0.15384549,0.00018523984,0.000045368233,0.00006519519,0.0020384712,0.0008534908,0.0022150823],"genre_scores_gemma":[0.9883364,0.00007917442,0.009292173,0.000014636454,0.000021035066,0.000023786304,0.001526097,0.000019210122,0.0006874907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993105,0.00016275229,0.000061714454,0.00014399787,0.00025453776,0.00006646825],"domain_scores_gemma":[0.9908894,0.005832572,0.0010096212,0.00056086376,0.001536741,0.00017077228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019858389,0.00063847995,0.0005939331,0.0026732858,0.00020904766,0.00052894023,0.00046018304,0.0004532263,0.0013869057],"category_scores_gemma":[0.007488852,0.0002542526,0.0008048954,0.0014240838,0.00028116556,0.0008028402,0.000253132,0.00045429732,0.00042575065],"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":[0.0010787925,0.00044679004,0.22312449,0.00011089594,0.00033960314,0.00021535082,0.000053729997,0.65811294,0.010559214,0.001047403,0.001737002,0.10317374],"study_design_scores_gemma":[0.000009242022,0.00026858476,0.0637229,0.0000061293376,0.000057920686,0.000041783285,0.000018188048,0.93305546,0.002117134,0.00049500674,0.00018817882,0.0000194159],"about_ca_topic_score_codex":0.007411519,"about_ca_topic_score_gemma":0.008789086,"teacher_disagreement_score":0.007411519,"about_ca_system_score_codex":0.00055053603,"about_ca_system_score_gemma":0.0006299228,"threshold_uncertainty_score":0.014736712},"labels":[],"label_agreement":null},{"id":"W2319648436","doi":"10.1061/41069(360)6","title":"Assessing 380km of PCCP Using Acoustic Monitoring — A Comparison of Technologies","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Nova Chemicals (Canada); BP (Canada)","funders":"","keywords":"Pipeline (software); Pipeline transport; Hydrophone; Metre; Asset management; Marine engineering; Engineering; Telecommunications; Geology; Mechanical engineering","score_opus":0.04758445474121108,"score_gpt":0.30647210035314165,"score_spread":0.25888764561193056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319648436","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":[0.9532796,0.0006564881,0.026861688,0.0001375322,0.0000162301,0.00013467288,0.00039789054,0.00032714632,0.01818868],"genre_scores_gemma":[0.9803656,0.00040272524,0.016980456,0.000014088768,0.0000054687216,0.000034158104,0.00021339758,0.000023205446,0.0019609872],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987489,0.00028248012,0.000050321396,0.00024604917,0.000606849,0.00006539231],"domain_scores_gemma":[0.9990257,0.00032488542,0.00015566366,0.00007285651,0.00037367668,0.000047220965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012639903,0.00071348046,0.00035209808,0.0018280203,0.00030720254,0.00093131687,0.00058421376,0.0005789994,0.00084943586],"category_scores_gemma":[0.0019669544,0.00025803078,0.0002674785,0.001363252,0.00027444604,0.001698314,0.0006350299,0.00027479138,0.00022012835],"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":[0.00104462,0.00045049496,0.24108292,0.00059048366,0.0001519276,0.0005653022,0.0011902818,0.10715152,0.07820831,0.0023411615,0.0014528902,0.56577015],"study_design_scores_gemma":[0.00008356435,0.0030278198,0.54599506,0.00019190303,0.00030204005,0.0007676199,0.002891073,0.33122927,0.09775213,0.0016711756,0.015921889,0.00016639532],"about_ca_topic_score_codex":0.009371287,"about_ca_topic_score_gemma":0.019647548,"teacher_disagreement_score":0.009371287,"about_ca_system_score_codex":0.0009356478,"about_ca_system_score_gemma":0.00047057652,"threshold_uncertainty_score":0.018633485},"labels":[],"label_agreement":null},{"id":"W2320020346","doi":"10.1061/41069(360)49","title":"Leak Detection Survey for a Large Diameter Water Transmission Main: City of Calgary","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Leak; Pipeline transport; Pipeline (software); Leak detection; Marine engineering; Port (circuit theory); Environmental science; Engineering; Electrical engineering; Mechanical engineering; Environmental engineering","score_opus":0.013420691730520184,"score_gpt":0.20150762201756497,"score_spread":0.1880869302870448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320020346","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":[0.9932747,0.00010668044,0.0021826804,0.00007721227,0.000011767768,0.00024388768,0.0012937117,0.0001706756,0.0026387717],"genre_scores_gemma":[0.97941935,0.0003149599,0.0069293138,0.0001622184,0.000014880327,0.00013352711,0.002543495,0.00004128748,0.010441123],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995647,0.00002931473,0.000017418199,0.00012204086,0.00017582832,0.00009066503],"domain_scores_gemma":[0.99941933,0.000040896794,0.00006852406,0.00003924407,0.00035445511,0.00007758405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027803864,0.000541294,0.00040430223,0.0012564169,0.0012699715,0.00069813285,0.0007254529,0.0005108962,0.0020251318],"category_scores_gemma":[0.0006027722,0.00037474863,0.00022728617,0.001974398,0.0004980858,0.0003261325,0.0009617167,0.0004861567,0.0006772668],"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":[0.0007599051,0.00083862874,0.60881966,0.00051424024,0.00011734666,0.0061374353,0.010255173,0.007937498,0.15991619,0.00033625014,0.007959416,0.19640818],"study_design_scores_gemma":[0.00003432667,0.00077695417,0.9789286,0.00003370379,0.000030461526,0.00041815336,0.0036527505,0.00272281,0.008526445,0.00003606638,0.0048015537,0.000038094015],"about_ca_topic_score_codex":0.3729337,"about_ca_topic_score_gemma":0.64066535,"teacher_disagreement_score":0.6270663,"about_ca_system_score_codex":0.002857868,"about_ca_system_score_gemma":0.0029223561,"threshold_uncertainty_score":0.74152595},"labels":[],"label_agreement":null},{"id":"W2320260603","doi":"10.1061/9780784413548.048","title":"Development of a Methodology to Predict the Failure of Large-Diameter Cast Iron Water Mains","year":2014,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2014","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Mains electricity; Monte Carlo method; Corrosion; Cast iron; Sensitivity (control systems); Cumulative distribution function; Structural engineering; Materials science; Environmental science; Probability density function; Engineering; Metallurgy; Mathematics; Statistics","score_opus":0.00863825015812735,"score_gpt":0.18562734486729984,"score_spread":0.17698909470917248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320260603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11066918,0.000093284434,0.8858702,0.00010674235,0.00002055517,0.00020804342,0.0003135324,0.00077244744,0.0019459268],"genre_scores_gemma":[0.8235003,0.000099240846,0.17436609,0.000028227107,0.000015526293,0.00024458236,0.00031853147,0.00004682797,0.0013807489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997931,0.000042837855,0.000013141484,0.000044057328,0.000078462894,0.000028506814],"domain_scores_gemma":[0.9990233,0.00051558035,0.00015013227,0.000046524594,0.00022142344,0.000042966993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008786441,0.0005705223,0.0004504197,0.0008730016,0.0003800568,0.00056717,0.0010445262,0.0008497568,0.0011198471],"category_scores_gemma":[0.0020571062,0.0004790362,0.0007906119,0.0003972536,0.00036136244,0.00042123726,0.00049588346,0.00061996805,0.00015972326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057003385,0.000012016467,0.0012248792,0.000008200952,0.00000823909,0.000024382742,0.000007782631,0.9936806,0.0006835242,0.00067030167,0.00004854487,0.0036257773],"study_design_scores_gemma":[0.0000015027309,0.000009341348,0.00021849849,0.0000014917065,0.0000018900055,0.0000055018586,0.000002614122,0.9990711,0.00031280887,0.00028493255,0.0000883731,0.0000017941717],"about_ca_topic_score_codex":0.023188127,"about_ca_topic_score_gemma":0.013209191,"teacher_disagreement_score":0.023188127,"about_ca_system_score_codex":0.0009497047,"about_ca_system_score_gemma":0.0021328584,"threshold_uncertainty_score":0.04610628},"labels":[],"label_agreement":null},{"id":"W2321353676","doi":"10.1061/41024(340)83","title":"A Methodology for Water Quality Regulatory Compliance Using Bayesian Belief Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"SCADA; Bayesian network; Risk analysis (engineering); Computer science; Water quality; Quality (philosophy); Water utility; Bayesian probability; Water supply; Business; Engineering; Artificial intelligence; Environmental engineering","score_opus":0.13773003840999495,"score_gpt":0.3306656937005284,"score_spread":0.19293565529053344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321353676","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":[0.00035279957,0.00004463428,0.9988262,0.000075757736,0.000009822239,0.000060077855,0.000023092005,0.0000808752,0.00052667526],"genre_scores_gemma":[0.046497323,0.00019733168,0.95138973,0.000091514565,0.000049093185,0.0006519471,0.00015744678,0.000067947236,0.0008976977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9885132,0.0062548947,0.00066992844,0.0016481817,0.0025263985,0.0003874838],"domain_scores_gemma":[0.98313254,0.012835603,0.0010824522,0.0006807962,0.0020449907,0.00022356545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014138516,0.0017266654,0.0017417993,0.0041667074,0.0014169551,0.0029876856,0.003944095,0.0026955353,0.006097614],"category_scores_gemma":[0.032394014,0.0012826486,0.0023331481,0.003480881,0.00265454,0.0038921838,0.0036923708,0.0030155387,0.00084746967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004884923,0.00007713103,0.0008106874,0.00028923075,0.00015545452,0.00012856476,0.00028651668,0.6682766,0.0005138813,0.18827248,0.0018434038,0.13929723],"study_design_scores_gemma":[0.000027048616,0.00003380503,0.00011419627,0.000074154734,0.000031542917,0.000041933366,0.000047353376,0.88983417,0.0003838476,0.10638078,0.0030052497,0.000025844303],"about_ca_topic_score_codex":0.011928783,"about_ca_topic_score_gemma":0.00829999,"teacher_disagreement_score":0.014138516,"about_ca_system_score_codex":0.0031251025,"about_ca_system_score_gemma":0.005693579,"threshold_uncertainty_score":0.07477254},"labels":[],"label_agreement":null},{"id":"W2321521001","doi":"10.1061/41020(339)114","title":"Risk Assessment for Water Mains Using Fuzzy Approach","year":2009,"lang":"en","type":"article","venue":"Construction Research Congress 2009","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Mains electricity; Dam failure; Risk analysis (engineering); Pipeline transport; Risk assessment; Event (particle physics); Fuzzy logic; Forensic engineering; Reliability engineering; Failure rate; Risk management; Engineering; Failure mode and effects analysis; Computer science; Business; Environmental engineering; Artificial intelligence; Flood myth; Computer security","score_opus":0.042440212603097514,"score_gpt":0.32653451187499766,"score_spread":0.28409429927190016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321521001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11514895,0.0002972042,0.87963617,0.0001968213,0.000015703017,0.0000854667,0.00012997772,0.00015768998,0.004332027],"genre_scores_gemma":[0.934893,0.0001791402,0.063478865,0.00001984531,0.000013487714,0.000058143774,0.00008082517,0.000009319814,0.0012674191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992336,0.00023012623,0.000038469545,0.00009869291,0.00032147675,0.000077611665],"domain_scores_gemma":[0.9990804,0.00047323437,0.00013488696,0.000034701814,0.00023333397,0.000043370364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012988106,0.00063975336,0.00061873166,0.0017054832,0.0005023997,0.0012232105,0.00067239866,0.0007638613,0.0014259123],"category_scores_gemma":[0.0028463115,0.0003248085,0.0009616246,0.00061530975,0.00044717157,0.0010302902,0.000655388,0.000453964,0.000099688375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060073464,0.00003138274,0.0021527626,0.00006244115,0.000062633786,0.00012376513,0.00012654811,0.9568802,0.0022350652,0.013476296,0.0004066445,0.02438224],"study_design_scores_gemma":[0.0000046474447,0.000039671693,0.0006322565,0.000012842632,0.000014580406,0.000028827359,0.000064948465,0.9890681,0.00046537473,0.009272322,0.00038203414,0.000014435408],"about_ca_topic_score_codex":0.010511425,"about_ca_topic_score_gemma":0.0071903043,"teacher_disagreement_score":0.010511425,"about_ca_system_score_codex":0.0015212897,"about_ca_system_score_gemma":0.0011707742,"threshold_uncertainty_score":0.020900488},"labels":[],"label_agreement":null},{"id":"W2322120881","doi":"10.1115/ipc2012-90556","title":"Optimization of Valve Placement in Liquids Pipeline Systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Dynamic Systems Analysis (Canada)","funders":"","keywords":"Pipeline (software); Pipeline transport; Computer science; Minification; Containment (computer programming); Globe valve; Timeline; Volume (thermodynamics); Environmental science; Engineering; Mechanical engineering","score_opus":0.009976143420225842,"score_gpt":0.19968974996778005,"score_spread":0.1897136065475542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322120881","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":[0.65084803,0.00043142968,0.34023595,0.00016448849,0.000034149944,0.00019268489,0.00013648246,0.0006859062,0.0072708083],"genre_scores_gemma":[0.9772494,0.000081664446,0.02181162,0.000009765592,0.0000031186407,0.00003681337,0.000028802895,0.000031204603,0.0007475995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995888,0.0001289906,0.000023533406,0.00007721902,0.00009394923,0.00008765955],"domain_scores_gemma":[0.9991235,0.0004842675,0.00019112288,0.000045855973,0.00009827426,0.00005690021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006798102,0.00077400426,0.0007807457,0.00065109757,0.0005564139,0.0012592636,0.0006357039,0.00080887054,0.0014923484],"category_scores_gemma":[0.002091976,0.00057344744,0.0004130605,0.00045449592,0.00068452547,0.00091080857,0.0005666418,0.00047619053,0.00022866447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048797618,0.000031378877,0.00048696643,0.00003338949,0.000007134026,0.000058415524,0.000033593165,0.98735553,0.00482499,0.0005347421,0.000069279966,0.006515769],"study_design_scores_gemma":[0.000027703409,0.00023121704,0.00035846193,0.000007921212,0.000017570153,0.000027949249,0.00005815765,0.990889,0.006914927,0.0009198165,0.0005341485,0.000013162966],"about_ca_topic_score_codex":0.0029397167,"about_ca_topic_score_gemma":0.0029257163,"teacher_disagreement_score":0.0029397167,"about_ca_system_score_codex":0.001096154,"about_ca_system_score_gemma":0.0010052354,"threshold_uncertainty_score":0.007953227},"labels":[],"label_agreement":null},{"id":"W2322362886","doi":"10.1061/40737(2004)478","title":"Implications of Over- and Under-Determined Networks in State Estimation","year":2004,"lang":"en","type":"article","venue":"Critical Transitions in Water and Environmental Resources Management","topic":"Water Systems and 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 Calgary","funders":"","keywords":"SCADA; Solver; Computer science; Section (typography); State (computer science); Estimation; Mathematical optimization; Mathematics; Engineering; Algorithm; Systems engineering","score_opus":0.005647096615153031,"score_gpt":0.1970829302184603,"score_spread":0.19143583360330726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322362886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28007093,0.00071874046,0.7108329,0.0032817863,0.00006101595,0.000087559245,0.0002312906,0.00026875624,0.0044469903],"genre_scores_gemma":[0.96272457,0.0002082537,0.036320567,0.00016237833,0.000024275647,0.000034523517,0.00007993628,0.000029623227,0.0004159094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99334866,0.003837306,0.00029677353,0.0009989177,0.001091747,0.00042657938],"domain_scores_gemma":[0.95462805,0.037565984,0.0032673252,0.0023195266,0.0019110192,0.00030809446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008876102,0.0006139933,0.00083983236,0.0007521782,0.0009844557,0.0016125215,0.00077864603,0.0010773317,0.000854613],"category_scores_gemma":[0.05982128,0.0006991266,0.0005208205,0.0009555594,0.0036064961,0.0046002064,0.0018410129,0.0021532746,0.00007285253],"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":[0.00007053981,0.000016948263,0.006757285,0.000030041729,0.000030978037,0.00013253692,0.00017764448,0.9416669,0.00023700108,0.039961837,0.00019288827,0.010725401],"study_design_scores_gemma":[0.000013801894,0.000030309127,0.0019607984,0.00002545114,0.0000142969875,0.00005726856,0.00010384337,0.9421591,0.0010607025,0.053954355,0.00060102943,0.000019026134],"about_ca_topic_score_codex":0.032693952,"about_ca_topic_score_gemma":0.022411918,"teacher_disagreement_score":0.032693952,"about_ca_system_score_codex":0.002309393,"about_ca_system_score_gemma":0.0024309142,"threshold_uncertainty_score":0.06500733},"labels":[],"label_agreement":null},{"id":"W2322441848","doi":"10.1002/cjce.22489","title":"Pump and pipeline performance when pumping slurries with different particle gradings","year":2016,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Slurry; Settling; Pipeline (software); Weighting; Particle (ecology); Homogeneous; Mechanics; Focus (optics); Environmental science; Engineering; Geology; Mechanical engineering; Physics; Thermodynamics; Environmental engineering; Acoustics","score_opus":0.005365467940398703,"score_gpt":0.1400364190732737,"score_spread":0.13467095113287497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322441848","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":[0.99474454,0.0001302387,0.0037076883,0.000053211577,0.000012627809,0.000022107273,0.0002587193,0.00012026459,0.00095067377],"genre_scores_gemma":[0.99853647,0.000083340885,0.0004974504,0.000008603434,0.0000015674775,0.000010356704,0.00019098782,0.000016556218,0.000654523],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956137,0.000069723406,0.000042059444,0.00009154932,0.00013327386,0.00010204867],"domain_scores_gemma":[0.9990583,0.00047765847,0.00011189709,0.00007588801,0.00023289287,0.00004339005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012253346,0.00038099033,0.00075003156,0.00045215397,0.0004550098,0.0011490553,0.00045795436,0.00080268,0.0021068829],"category_scores_gemma":[0.0029563732,0.00029602315,0.0004892859,0.00060543977,0.00042968272,0.0012380991,0.0003845061,0.0005871973,0.0005342728],"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":[0.010975301,0.0006599857,0.03484343,0.00081448955,0.00017669993,0.00053659023,0.00045457337,0.21796171,0.67362934,0.0010644938,0.0012670137,0.057616394],"study_design_scores_gemma":[0.00026220048,0.009628079,0.07963979,0.00003693353,0.00029331614,0.00015463943,0.0005768449,0.3186405,0.5880826,0.00049595546,0.0020742982,0.00011481595],"about_ca_topic_score_codex":0.006300856,"about_ca_topic_score_gemma":0.003346164,"teacher_disagreement_score":0.006300856,"about_ca_system_score_codex":0.00060126255,"about_ca_system_score_gemma":0.0004897756,"threshold_uncertainty_score":0.01252836},"labels":[],"label_agreement":null},{"id":"W2322471970","doi":"10.1061/9780784479360.108","title":"Water Mains Degradation Analysis Using Log-Linear Models","year":2015,"lang":"en","type":"article","venue":"Pipelines 2015","topic":"Water Systems and 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 Waterloo","funders":"","keywords":"Weibull distribution; Degradation (telecommunications); Mains electricity; Exponential function; Reliability engineering; Computer science; Electricity; Environmental science; Engineering; Statistics; Mathematics","score_opus":0.06005257974603472,"score_gpt":0.26015921716441803,"score_spread":0.2001066374183833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322471970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14019148,0.00043411678,0.849276,0.0002574918,0.000026089416,0.000094636285,0.00078272575,0.0012385885,0.007698918],"genre_scores_gemma":[0.97472805,0.00036386034,0.016443014,0.00004175645,0.000015508776,0.00010680898,0.0003927258,0.000086563894,0.00782173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995834,0.0001370767,0.000019851417,0.00007005862,0.00011490461,0.00007471782],"domain_scores_gemma":[0.9989318,0.000635264,0.00016070124,0.0000592433,0.00018815653,0.000024698307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008710004,0.00069496455,0.0004418106,0.0009026242,0.0002610793,0.0007380993,0.0010496306,0.0006416487,0.0028746293],"category_scores_gemma":[0.0023203946,0.0002937742,0.00085238833,0.0006433007,0.00038613626,0.0009375652,0.00054850616,0.00067201944,0.0006259669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003033934,0.000033465705,0.0011712117,0.000036896065,0.000015454849,0.000044650074,0.000029225626,0.9870907,0.0009836573,0.0020153925,0.00031255477,0.008236565],"study_design_scores_gemma":[0.0000011315384,0.000013830178,0.00028017786,0.0000016655758,0.000003280247,0.000008755094,0.000008125831,0.9985708,0.00016432226,0.0007963179,0.00014851992,0.0000031358657],"about_ca_topic_score_codex":0.026754517,"about_ca_topic_score_gemma":0.018670673,"teacher_disagreement_score":0.026754517,"about_ca_system_score_codex":0.0010961441,"about_ca_system_score_gemma":0.000985098,"threshold_uncertainty_score":0.053197622},"labels":[],"label_agreement":null},{"id":"W2322635954","doi":"10.1061/40737(2004)447","title":"A New Versatile Technique for Hydraulic Models of Water Distribution Networks","year":2004,"lang":"en","type":"article","venue":"Critical Transitions in Water and Environmental Resources Management","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Computer science; Solver; Variety (cybernetics); Context (archaeology); Mathematical optimization; State (computer science); Distribution (mathematics); Algorithm; Mathematics; Artificial intelligence","score_opus":0.006357340771902331,"score_gpt":0.1819714226986412,"score_spread":0.17561408192673886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322635954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003219447,0.000030381843,0.99838233,0.0000446905,0.000014169579,0.000008086344,0.000028269153,0.00013256013,0.0010374029],"genre_scores_gemma":[0.104895145,0.0005668448,0.88586974,0.00021884205,0.000102931364,0.0002628798,0.00029643343,0.00035955952,0.0074276226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995022,0.00015706178,0.000024237594,0.000077247314,0.00020817627,0.00003120972],"domain_scores_gemma":[0.99963593,0.0001837968,0.00003740378,0.00006946763,0.000059023074,0.000014391988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006609417,0.00073542073,0.0006878285,0.0006322936,0.0005669489,0.00096977135,0.0012295792,0.00097326917,0.0040514027],"category_scores_gemma":[0.0023262259,0.0004888722,0.000995266,0.00086575706,0.0008871602,0.0023230186,0.0014887771,0.0018209213,0.0010603751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013581853,0.000020205718,0.00017014005,0.000096146636,0.000024054776,0.000062275045,0.00011640903,0.48840365,0.0035442633,0.4329297,0.004718821,0.0699006],"study_design_scores_gemma":[0.0000073654674,0.000013037866,0.00002814925,0.000014095011,0.0000062879235,0.000047498674,0.000011708755,0.91872996,0.0008040632,0.06352185,0.016805826,0.000010193293],"about_ca_topic_score_codex":0.0027100402,"about_ca_topic_score_gemma":0.002919845,"teacher_disagreement_score":0.0040514027,"about_ca_system_score_codex":0.00045633898,"about_ca_system_score_gemma":0.0012349143,"threshold_uncertainty_score":0.013553321},"labels":[],"label_agreement":null},{"id":"W2322877879","doi":"10.1061/41187(420)65","title":"Handling Transmission Mains in Water Loss Control Programs","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Pure North; Research Canada","funders":"","keywords":"Mains electricity; Leakage (economics); Transmission (telecommunications); Transmission loss; Electric power transmission; Electricity; Computer science; Leak; Engineering; Telecommunications; Environmental engineering; Electrical engineering","score_opus":0.012574941703274974,"score_gpt":0.16029044410968377,"score_spread":0.1477155024064088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322877879","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":[0.61818045,0.00041491227,0.3580897,0.0008714652,0.000052503725,0.0012963599,0.00073918264,0.002441315,0.017914176],"genre_scores_gemma":[0.96863294,0.00018093734,0.026392076,0.00006208911,0.000013866536,0.00018087745,0.0003065734,0.00010620164,0.004124531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984627,0.00054504507,0.000043609194,0.00016640335,0.00039149102,0.00039081174],"domain_scores_gemma":[0.99715155,0.0013426458,0.00062645413,0.0002488668,0.00043587727,0.00019468094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018418861,0.00066031306,0.00066728744,0.0009488663,0.00084802136,0.0014831482,0.0008718548,0.00082066574,0.006029943],"category_scores_gemma":[0.0045737014,0.00043211915,0.00045879086,0.0011262923,0.00063847116,0.0019760318,0.0012568891,0.0010179223,0.00056934543],"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":[0.0007413489,0.00078570214,0.027007425,0.00046750228,0.000083976716,0.0006448848,0.00055992726,0.70799714,0.012467987,0.007269521,0.0057041054,0.2362705],"study_design_scores_gemma":[0.00016599194,0.0018405438,0.030444382,0.00013526023,0.0001310662,0.00038543428,0.0025944454,0.91073096,0.022073979,0.012888295,0.018522067,0.000087644614],"about_ca_topic_score_codex":0.011813951,"about_ca_topic_score_gemma":0.013813951,"teacher_disagreement_score":0.011813951,"about_ca_system_score_codex":0.0015001721,"about_ca_system_score_gemma":0.0021459605,"threshold_uncertainty_score":0.02349037},"labels":[],"label_agreement":null},{"id":"W2323902692","doi":"10.1061/40569(2001)406","title":"Solute Dispersion and Transport in Pipes under Transient Hydraulic Conditions","year":2001,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Transient (computer programming); Dispersion (optics); Environmental science; Mechanics; Transient analysis; Hydraulic machinery; Petroleum engineering; Geotechnical engineering; Materials science; Computer science; Engineering; Transient response; Mechanical engineering; Physics; Electrical engineering; Optics","score_opus":0.008285103294834539,"score_gpt":0.18795434445179013,"score_spread":0.1796692411569556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323902692","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":[0.8903176,0.00056369964,0.10436718,0.0003791129,0.000031694668,0.000031707576,0.00014458502,0.00023119895,0.0039332393],"genre_scores_gemma":[0.99581677,0.00023913334,0.002540598,0.000011411045,0.0000056411413,0.000013523751,0.000031063584,0.000011183434,0.0013305701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999211,0.000016643335,0.000003525671,0.00001717535,0.000021729908,0.00001987125],"domain_scores_gemma":[0.9998062,0.0001033154,0.000039895578,0.000014846652,0.000025246145,0.00001043521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032770456,0.00016289057,0.00024135955,0.0003131904,0.00033027612,0.00056425645,0.00021006828,0.00075121975,0.0005633147],"category_scores_gemma":[0.0009964544,0.00018259593,0.00019670355,0.00028776706,0.0009708646,0.0010098192,0.00044092647,0.00025812254,0.00011185577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048222358,0.0000976083,0.012010149,0.000200973,0.000022798482,0.00087329664,0.000365884,0.7317863,0.19144686,0.031383134,0.0006688974,0.030661788],"study_design_scores_gemma":[0.000023435992,0.000107319334,0.0029041038,0.000009607385,0.00000835143,0.00010724721,0.0000865507,0.9631318,0.026105264,0.0067671747,0.00073253753,0.000016687405],"about_ca_topic_score_codex":0.0031608064,"about_ca_topic_score_gemma":0.0011440313,"teacher_disagreement_score":0.0031608064,"about_ca_system_score_codex":0.0006954902,"about_ca_system_score_gemma":0.00043603877,"threshold_uncertainty_score":0.006284833},"labels":[],"label_agreement":null},{"id":"W2324384116","doi":"10.1061/41036(342)48","title":"Comparison of Pressures Simulated Using Transient Analysis with Field Data from a Full-Scale Distribution System","year":2009,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2009","topic":"Water Systems and 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; University of Toronto; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Transient (computer programming); Pressure measurement; Environmental science; Field (mathematics); Mechanics; Meteorology; Mathematics; Computer science; Physics","score_opus":0.01139055067907769,"score_gpt":0.21018037899008918,"score_spread":0.1987898283110115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324384116","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":[0.9907203,0.000012327752,0.00797787,0.00002922007,0.0000040600617,0.000021443158,0.00024590813,0.0003157923,0.0006731399],"genre_scores_gemma":[0.99884737,0.0000068289023,0.00090404524,0.0000028421011,8.4286006e-7,0.000014913887,0.0001351534,0.000009462501,0.00007863021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999734,0.000056648256,0.000021979466,0.00007622899,0.000071934264,0.000039166345],"domain_scores_gemma":[0.99843544,0.0009939217,0.00012650952,0.0001368993,0.00025812813,0.00004899825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046675085,0.00038742792,0.00034891765,0.0004492861,0.00029876575,0.0004936205,0.0006057676,0.0005194561,0.0006328639],"category_scores_gemma":[0.0018578449,0.00026495708,0.00028304636,0.00046464932,0.00036534126,0.00068891264,0.0002712989,0.00051871024,0.00012065839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000510739,0.0004067449,0.037342563,0.000105318075,0.000044933182,0.00020528861,0.00020796676,0.92465574,0.01990379,0.00042587589,0.0005270207,0.015664112],"study_design_scores_gemma":[0.000025326832,0.00016029965,0.012592039,0.0000029056596,0.000008357329,0.000023807677,0.00005067103,0.98082167,0.0061067776,0.00008348355,0.000111587906,0.000012957522],"about_ca_topic_score_codex":0.013424332,"about_ca_topic_score_gemma":0.0065054097,"teacher_disagreement_score":0.013424332,"about_ca_system_score_codex":0.00093572447,"about_ca_system_score_gemma":0.00059025205,"threshold_uncertainty_score":0.02669239},"labels":[],"label_agreement":null},{"id":"W2324387613","doi":"10.1061/9780784479360.074","title":"The Link between Transient Surges and Minimum Pressure Criterion in Water Distribution Systems","year":2015,"lang":"en","type":"article","venue":"Pipelines 2015","topic":"Water Systems and 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":"McMaster University; University of Toronto","funders":"","keywords":"Transient (computer programming); Surge; Control theory (sociology); Computer science; Environmental science; Engineering; Control (management)","score_opus":0.018895326939242212,"score_gpt":0.22381300591758693,"score_spread":0.20491767897834473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324387613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30244744,0.0015391947,0.6738499,0.0014072873,0.000078442295,0.00008063879,0.00024870248,0.0005592538,0.019789148],"genre_scores_gemma":[0.9960056,0.00017543834,0.0032827174,0.000017909888,0.000019905181,0.000015791107,0.000027090284,0.000012091768,0.00044341647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904555,0.00032665778,0.00006695501,0.00013136324,0.00027877817,0.00015068987],"domain_scores_gemma":[0.9949038,0.0034672753,0.0009650223,0.00011512788,0.00041711988,0.00013164627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009386047,0.00036804698,0.0005823644,0.00057913765,0.00051100674,0.0012433117,0.00057790853,0.0008875466,0.0021825857],"category_scores_gemma":[0.006732721,0.00024662993,0.0004328625,0.0006763174,0.0010941295,0.0014088565,0.0007021008,0.0011078005,0.000134851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017177062,0.000077554774,0.004617834,0.00040126118,0.000062055595,0.0005984288,0.00032955778,0.8891683,0.006595973,0.075565025,0.0015038953,0.020908248],"study_design_scores_gemma":[0.000014494891,0.00024816595,0.004356165,0.000027689706,0.000022208982,0.0002047669,0.00014018717,0.94298893,0.0015268845,0.049457613,0.0009837552,0.000029059021],"about_ca_topic_score_codex":0.002805795,"about_ca_topic_score_gemma":0.0008879932,"teacher_disagreement_score":0.002805795,"about_ca_system_score_codex":0.0007233573,"about_ca_system_score_gemma":0.000599108,"threshold_uncertainty_score":0.00730145},"labels":[],"label_agreement":null},{"id":"W2324432978","doi":"10.1061/9780784412947.086","title":"Pipeline Optimization Accounting for Transient Conditions: Exploring the Connections between System Configuration, Operation, and Surge Protection","year":2013,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2013","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Transient (computer programming); Particle swarm optimization; Surge; Water hammer; Pipeline (software); Pipeline transport; Sorting; Optimization problem; Multi-objective optimization; Pareto principle; Mathematical optimization; Engineering; Computer science; Reliability engineering; Mathematics; Algorithm; Environmental engineering","score_opus":0.012878686885834514,"score_gpt":0.1687656236936434,"score_spread":0.15588693680780888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324432978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20311017,0.00021469918,0.7922363,0.00028743478,0.000012632683,0.00005687899,0.000093509865,0.00022341647,0.0037648932],"genre_scores_gemma":[0.97425824,0.00013807834,0.024458978,0.000014505877,0.0000074084105,0.00004370481,0.000059848593,0.000028066712,0.0009911575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985814,0.000053139393,0.000004529772,0.000024596924,0.000026318678,0.000033192064],"domain_scores_gemma":[0.99960667,0.00027102968,0.000054574368,0.00001625378,0.00003595259,0.000015546575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062381744,0.0006592407,0.00062310556,0.00042966226,0.00027503673,0.00071796513,0.00043224523,0.0006931607,0.0010578093],"category_scores_gemma":[0.0015834783,0.0004647747,0.00055029395,0.00043904933,0.00060237333,0.00093632564,0.0003534307,0.0004577726,0.00007960132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016587532,0.0000118955595,0.00046215617,0.000014235424,0.000007271707,0.000025222269,0.000009931769,0.9937262,0.0007414913,0.0010134086,0.00007675874,0.0038948227],"study_design_scores_gemma":[0.000003008377,0.00002713816,0.00025403767,0.0000017726469,0.000004186672,0.000007052747,0.000010190397,0.99809796,0.0003343328,0.0012002776,0.0000578678,0.0000022939887],"about_ca_topic_score_codex":0.0053349324,"about_ca_topic_score_gemma":0.0033144115,"teacher_disagreement_score":0.0053349324,"about_ca_system_score_codex":0.0006273354,"about_ca_system_score_gemma":0.0014773309,"threshold_uncertainty_score":0.010607779},"labels":[],"label_agreement":null},{"id":"W2324701914","doi":"10.1061/40941(247)148","title":"Use of Bayesian Statistics to Study Chlorine Decay within a Water Distribution System","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":true,"ca_institutions":"University of Guelph","funders":"Canada Research Chairs","keywords":"Chlorine; Monte Carlo method; Markov chain Monte Carlo; Bayesian probability; Markov process; Component (thermodynamics); Residual; Statistical physics; Chemistry; Biological system; Computer science; Mathematics; Statistics; Algorithm; Thermodynamics; Physics","score_opus":0.019640954121616542,"score_gpt":0.19641166690340867,"score_spread":0.17677071278179213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324701914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113101214,0.00027446062,0.8834796,0.00026934515,0.000017549411,0.00011436671,0.00019246404,0.00029439712,0.002256529],"genre_scores_gemma":[0.8285914,0.0003427034,0.16927984,0.000081213715,0.000055729815,0.00022666134,0.00034109643,0.00008098169,0.0010003577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611926,0.0025571845,0.00016161671,0.00036054084,0.00063010963,0.00017133473],"domain_scores_gemma":[0.9513997,0.04269836,0.0029414562,0.0009315663,0.0016376534,0.00039138092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0116866855,0.0006727217,0.0010942395,0.0034333775,0.00079291343,0.0017405736,0.001160434,0.0011345099,0.00085231935],"category_scores_gemma":[0.0461519,0.000804925,0.0008766806,0.0016456814,0.0013255052,0.002682178,0.0011382272,0.0011923232,0.00010320403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009936022,0.0001021742,0.020054415,0.00006390644,0.00020329788,0.0001514466,0.00021422567,0.89559454,0.0010560271,0.047103904,0.00043098294,0.034925688],"study_design_scores_gemma":[0.0000091895345,0.00003363316,0.0020577244,0.000007991105,0.000011068786,0.000019363806,0.0000352488,0.9848245,0.00022186922,0.0124985725,0.00026419837,0.000016625996],"about_ca_topic_score_codex":0.024269992,"about_ca_topic_score_gemma":0.0144954845,"teacher_disagreement_score":0.024269992,"about_ca_system_score_codex":0.0020906923,"about_ca_system_score_gemma":0.0025858348,"threshold_uncertainty_score":0.061805844},"labels":[],"label_agreement":null},{"id":"W2325015371","doi":"10.1080/00221686.2015.1131204","title":"Local wave speed and bulk flow viscosity in Francis turbines at part load operation","year":2016,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":49,"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":"BC Hydro; European Commission; Energy Foundation","keywords":"Draft tube; Francis turbine; Mechanics; Turbine; Flow (mathematics); Cavitation; Viscosity; Marine engineering; Dimensionless quantity; Physics; Mechanical engineering; Engineering","score_opus":0.035815790618545666,"score_gpt":0.270659871682901,"score_spread":0.23484408106435534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325015371","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":[0.99102646,0.00008477036,0.008202415,0.000018300314,0.0000034595591,0.0000066346074,0.0000261399,0.00004110168,0.00059071666],"genre_scores_gemma":[0.999151,0.000045400287,0.00055722205,0.0000013059997,0.0000011343456,0.0000042075403,0.000021818392,0.000009159524,0.00020876447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998498,0.00003361575,0.000008503203,0.000029558963,0.000051632935,0.00002693585],"domain_scores_gemma":[0.99949324,0.00027080203,0.00012357296,0.00003247522,0.000046683315,0.000033235145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032688797,0.00031217176,0.00045698995,0.000635693,0.00025003916,0.0006255655,0.0003954941,0.000783768,0.0004940399],"category_scores_gemma":[0.0014120499,0.00034594577,0.0002630958,0.00034943293,0.0008441729,0.0007862876,0.00027792627,0.00040226505,0.00012963306],"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":[0.0011412696,0.00033689485,0.048015483,0.00022305404,0.000058331352,0.0019371703,0.0010359077,0.5916593,0.31962752,0.0043225987,0.00055571034,0.031086812],"study_design_scores_gemma":[0.000030340376,0.0007936734,0.03843996,0.0000214476,0.000032575408,0.00019877174,0.00031542426,0.8979234,0.060915627,0.0008552765,0.00038548603,0.00008796334],"about_ca_topic_score_codex":0.0041823853,"about_ca_topic_score_gemma":0.0038835835,"teacher_disagreement_score":0.0041823853,"about_ca_system_score_codex":0.0003475843,"about_ca_system_score_gemma":0.00017666898,"threshold_uncertainty_score":0.0083161},"labels":[],"label_agreement":null},{"id":"W2325623958","doi":"10.1061/40685(2003)124","title":"Operating Modes and Connectivity Matrices for Water Distribution Systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Calgary","funders":"","keywords":"SCADA; Computer science; Mode (computer interface); Flow (mathematics); Flow network; Matrix (chemical analysis); Layer (electronics); Transport layer; Distributed computing; Mathematical optimization; Real-time computing; Topology (electrical circuits); Engineering; Mathematics","score_opus":0.008469763997124699,"score_gpt":0.18527735921000565,"score_spread":0.17680759521288095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325623958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029095603,0.00040543795,0.9548643,0.000353707,0.000047141082,0.00009384372,0.0008576142,0.00031987525,0.013962434],"genre_scores_gemma":[0.6637916,0.0014893641,0.32513592,0.00008694383,0.00011063429,0.0004565079,0.0014583454,0.00016713997,0.0073034856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995995,0.000121787445,0.000023208897,0.000073265444,0.00014319009,0.00003895472],"domain_scores_gemma":[0.99866426,0.0007940596,0.00022157322,0.00008360001,0.00019450899,0.000041987845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054447894,0.00081317476,0.00031497382,0.0014087289,0.00041677468,0.0011584216,0.00073130615,0.00047414552,0.007049926],"category_scores_gemma":[0.0037584493,0.0002620019,0.0005185889,0.0012756506,0.00051730796,0.001663689,0.00054071774,0.0008953425,0.00055200444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029780602,0.000016424843,0.0008426519,0.00007835256,0.000014712382,0.000104277526,0.00006372935,0.6879008,0.0012797465,0.27577713,0.0023779515,0.031514514],"study_design_scores_gemma":[0.0000056921963,0.000015773508,0.00027996057,0.00001844796,0.0000065371732,0.00008746124,0.00002415056,0.8517018,0.00034780274,0.14242549,0.00507413,0.000012729802],"about_ca_topic_score_codex":0.004533764,"about_ca_topic_score_gemma":0.0035869651,"teacher_disagreement_score":0.007049926,"about_ca_system_score_codex":0.00085069373,"about_ca_system_score_gemma":0.0006381859,"threshold_uncertainty_score":0.023584306},"labels":[],"label_agreement":null},{"id":"W2325852132","doi":"10.1061/40927(243)512","title":"Temporal and Spatial Scaling of Instantaneous Residential Water Demand for Network Analysis","year":2007,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2007","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Node (physics); Scaling; Variance (accounting); Computer science; Range (aeronautics); Space time; Network analysis; Network model; Statistics; Simulation; Real-time computing; Mathematics; Mathematical optimization; Data mining; Engineering","score_opus":0.003819340143033124,"score_gpt":0.17147417794637762,"score_spread":0.1676548378033445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325852132","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":[0.50218934,0.000111534406,0.4935868,0.00031985162,0.000027269518,0.000021392741,0.0002947374,0.00030535765,0.0031436856],"genre_scores_gemma":[0.9760976,0.000068675196,0.023068327,0.0000090738395,0.00000899926,0.00001668941,0.00008997961,0.000027269238,0.0006134046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998652,0.00005987527,0.0000059445665,0.000028792294,0.000026511047,0.000013746904],"domain_scores_gemma":[0.99947244,0.00032721466,0.00007017714,0.000042181466,0.00006817511,0.000019886462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048527654,0.00021911088,0.00023335981,0.00025726852,0.00016332153,0.00035722714,0.00035464845,0.0002891774,0.0007392281],"category_scores_gemma":[0.0024298956,0.0001946026,0.00030492072,0.00041289552,0.00027090113,0.0006776972,0.00027786798,0.00028619493,0.00008023324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008029757,0.0000051449283,0.0011064697,0.0000053105905,0.0000036320573,0.000013335233,0.000009111644,0.99402195,0.00058560935,0.0023056462,0.00008294914,0.0018528032],"study_design_scores_gemma":[2.575657e-7,0.0000013416403,0.00014786574,2.2445279e-7,4.2976137e-7,0.0000020961452,0.0000017577685,0.9993636,0.00005445583,0.00039331825,0.000034090655,5.996755e-7],"about_ca_topic_score_codex":0.011203796,"about_ca_topic_score_gemma":0.011614538,"teacher_disagreement_score":0.011203796,"about_ca_system_score_codex":0.0008615917,"about_ca_system_score_gemma":0.00039520333,"threshold_uncertainty_score":0.022277176},"labels":[],"label_agreement":null},{"id":"W2326530988","doi":"10.1061/41203(425)148","title":"A Two Stage Optimization Approach for Calibrating Water Distribution Systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Waterloo","funders":"","keywords":"Calibration; SCADA; Range (aeronautics); Computer science; Flow (mathematics); Stage (stratigraphy); Test data; Simulation; Mathematical optimization; Algorithm; Mathematics; Statistics; Engineering","score_opus":0.024031783396665323,"score_gpt":0.18950229126657503,"score_spread":0.16547050786990972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2326530988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01456812,0.0000931446,0.98085874,0.00012038333,0.000020721778,0.0002543724,0.000080088634,0.00031915703,0.003685249],"genre_scores_gemma":[0.36214387,0.0001148527,0.63193977,0.00014260836,0.000029177443,0.0009643785,0.00026023385,0.00012969156,0.004275461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815124,0.0007991428,0.00008409107,0.00042155507,0.00037398675,0.00016997331],"domain_scores_gemma":[0.99844164,0.0009349554,0.0001569478,0.00009243414,0.00032683322,0.00004712041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032083825,0.0018672574,0.0016078822,0.0013788181,0.0007864858,0.0019538584,0.0017527897,0.002145009,0.004286449],"category_scores_gemma":[0.0043896036,0.0015584233,0.0012980266,0.0013053461,0.0007140995,0.0013963984,0.0017867908,0.0018561558,0.00043925946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003062991,0.000045883084,0.00027324367,0.00004706152,0.000026712189,0.000025341607,0.000032782,0.98217475,0.0009371559,0.0019799182,0.00021975226,0.014206776],"study_design_scores_gemma":[0.000011256286,0.00005232675,0.0001149674,0.0000046957603,0.0000063289444,0.000004382828,0.000009235797,0.9982305,0.00042757732,0.00086369127,0.00026787198,0.0000071201403],"about_ca_topic_score_codex":0.0083260955,"about_ca_topic_score_gemma":0.0068194806,"teacher_disagreement_score":0.0083260955,"about_ca_system_score_codex":0.0016622047,"about_ca_system_score_gemma":0.0026058448,"threshold_uncertainty_score":0.016967773},"labels":[],"label_agreement":null},{"id":"W2326613168","doi":"10.1061/40792(173)373","title":"Management Alternatives for River-Alluvial Groundwater Supply Systems","year":2005,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Water supply; Reliability (semiconductor); Groundwater; Environmental science; Water resource management; Water quality; Risk analysis (engineering); Computer science; Environmental engineering; Business; Engineering","score_opus":0.007463601663501542,"score_gpt":0.19295293618926318,"score_spread":0.18548933452576163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2326613168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2625585,0.0024186953,0.69418335,0.0011891023,0.00010518202,0.0009539573,0.00076561596,0.00031184562,0.037513744],"genre_scores_gemma":[0.9102969,0.0008448681,0.08173094,0.00008818508,0.0000344248,0.0005352321,0.0002714287,0.000061506056,0.0061365366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980781,0.0010082509,0.00006652831,0.00020452664,0.000364717,0.0002778781],"domain_scores_gemma":[0.9984861,0.0009972835,0.00020433706,0.000024116534,0.0001980631,0.00009004907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030127517,0.0016813371,0.0015117157,0.0030746625,0.0010185557,0.0041622557,0.0013074777,0.0020969294,0.00543967],"category_scores_gemma":[0.0036974926,0.00095000473,0.0015107407,0.0021147784,0.0012306337,0.0018103523,0.0019236102,0.0012150204,0.00023401107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026088652,0.000033712382,0.00041068424,0.00004120204,0.000030985,0.0000595719,0.000022551185,0.98493344,0.00029352057,0.00844087,0.00023225568,0.0054751085],"study_design_scores_gemma":[0.000020456757,0.00013836884,0.00037136118,0.000028035192,0.00004763565,0.000020830768,0.00013356717,0.9810512,0.00035443186,0.016729334,0.0010843204,0.000020417425],"about_ca_topic_score_codex":0.011777077,"about_ca_topic_score_gemma":0.018708449,"teacher_disagreement_score":0.011777077,"about_ca_system_score_codex":0.0056559327,"about_ca_system_score_gemma":0.0028738913,"threshold_uncertainty_score":0.041036844},"labels":[],"label_agreement":null},{"id":"W2326868143","doi":"10.1061/40644(2002)323","title":"Real-Time Control of Urban Wastewater Systems - Where Do We Stand Today?","year":2002,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":18,"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":"Wastewater; Control (management); Section (typography); Sewage treatment; Field (mathematics); Current (fluid); Control system; Computer science; Engineering; Environmental science; Risk analysis (engineering); Environmental engineering; Business","score_opus":0.00598919324619407,"score_gpt":0.16134398298253255,"score_spread":0.15535478973633848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2326868143","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":[0.024331363,0.7396648,0.19965532,0.018553872,0.0013393749,0.000043286404,0.00008455609,0.00059950934,0.015727993],"genre_scores_gemma":[0.38028473,0.5602424,0.038339246,0.0027517786,0.0016766223,0.00006503128,0.00015478185,0.00014580415,0.016339608],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993142,0.00014408206,0.000030514815,0.0000919367,0.0003484552,0.00007081604],"domain_scores_gemma":[0.99922585,0.0002744559,0.00014859822,0.000035333225,0.00027094345,0.000044786688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010349344,0.0005393295,0.0009271185,0.0004478938,0.00025731954,0.0021638894,0.0009354366,0.0011334459,0.0019852084],"category_scores_gemma":[0.0016935356,0.00015229695,0.00023257484,0.001043816,0.0014543046,0.0025551987,0.0004633552,0.0008670329,0.00054254784],"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":[0.00016019447,0.00007170075,0.00094997976,0.002632023,0.00004816479,0.00011300504,0.00026858167,0.0524349,0.0072912304,0.04804862,0.010483627,0.87749803],"study_design_scores_gemma":[0.00007353248,0.0008291771,0.004940331,0.0016902327,0.00015114456,0.00053091126,0.0020000567,0.17131086,0.015509576,0.10579362,0.69687784,0.0002926392],"about_ca_topic_score_codex":0.00865085,"about_ca_topic_score_gemma":0.008884391,"teacher_disagreement_score":0.00865085,"about_ca_system_score_codex":0.001239568,"about_ca_system_score_gemma":0.0010673266,"threshold_uncertainty_score":0.017200947},"labels":[],"label_agreement":null},{"id":"W2326944796","doi":"10.1061/40994(321)51","title":"An Integrated Management Approach for Critical Water Mains","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Hamilton Health Sciences","funders":"","keywords":"Mains electricity; Asset management; Failure mode, effects, and criticality analysis; Computer science; Rationalization (economics); Risk analysis (engineering); Reliability engineering; Criticality; Operations research; Engineering; Business; Failure mode and effects analysis","score_opus":0.015992088362378608,"score_gpt":0.2110009140270026,"score_spread":0.195008825664624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2326944796","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":[0.00752499,0.0014907973,0.89653844,0.00715129,0.00030390517,0.00093542674,0.00021999812,0.0014487545,0.08438644],"genre_scores_gemma":[0.22147672,0.0034737824,0.72531164,0.0011778846,0.00042988206,0.0008703464,0.0008126909,0.00032723107,0.04611981],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955193,0.0007420494,0.00033317372,0.00070269697,0.0022066177,0.0004962286],"domain_scores_gemma":[0.99707246,0.00032853548,0.00031870912,0.0002534702,0.0016494385,0.00037743044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00519458,0.0011601681,0.0006178139,0.0041343253,0.0018256996,0.0075398264,0.0036109677,0.0019454659,0.008924077],"category_scores_gemma":[0.004179407,0.000605012,0.0008948415,0.002904174,0.002137612,0.0075868387,0.0059888763,0.0022297325,0.0016380068],"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":[0.000051130584,0.00028485624,0.0029104187,0.00054168614,0.00007326536,0.0004772683,0.0021462198,0.08016564,0.00634387,0.4891826,0.031474173,0.3863488],"study_design_scores_gemma":[0.000045286888,0.00033405662,0.002527742,0.0007974728,0.00009028104,0.00047193846,0.0037875657,0.18487626,0.004102263,0.35090396,0.45190647,0.00015662416],"about_ca_topic_score_codex":0.010110554,"about_ca_topic_score_gemma":0.012552595,"teacher_disagreement_score":0.010110554,"about_ca_system_score_codex":0.006307487,"about_ca_system_score_gemma":0.012271228,"threshold_uncertainty_score":0.045764208},"labels":[],"label_agreement":null},{"id":"W2327427422","doi":"10.1115/icnmm2012-73216","title":"Simulation of Constant Pressure and Flow Rate Through Mini-Channels Inserted Into Distribution Systems (WDS)","year":2012,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Constant (computer programming); Volumetric flow rate; Flow (mathematics); Mechanics; Steady state (chemistry); Water flow; Environmental science; Control theory (sociology); Computer science; Simulation; Environmental engineering; Physics; Chemistry","score_opus":0.013139947132136585,"score_gpt":0.21319417326298085,"score_spread":0.20005422613084425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327427422","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":[0.9563557,0.00013018584,0.02928805,0.00026464384,0.000043930784,0.000095573,0.0009095012,0.000457871,0.012454518],"genre_scores_gemma":[0.99393034,0.000059885006,0.0036095567,0.00001902616,0.0000022853078,0.000054440232,0.00015881368,0.000025670073,0.0021400831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982005,0.00003775928,0.0000078344065,0.000029526953,0.00004035979,0.000064390406],"domain_scores_gemma":[0.99893004,0.0007427492,0.00008889191,0.000038836522,0.00014336301,0.000056095196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037517867,0.0004437469,0.00059747713,0.0004642644,0.00044144856,0.0006425394,0.00064081454,0.0014699348,0.004028378],"category_scores_gemma":[0.0012105866,0.00038846215,0.000819934,0.00045917105,0.0005246826,0.00060573156,0.00031435254,0.0006784623,0.00023189042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005350038,0.000034415058,0.0011576323,0.000024228546,0.0000047691233,0.000036061378,0.000023591487,0.9962167,0.0010202122,0.00041972313,0.0001281079,0.0008810562],"study_design_scores_gemma":[0.0000093565695,0.00003394484,0.00031077716,0.0000021841893,0.000002826276,0.0000042400084,0.0000148345935,0.9987741,0.0006680959,0.00007367532,0.000103181774,0.0000027091426],"about_ca_topic_score_codex":0.0252351,"about_ca_topic_score_gemma":0.012511007,"teacher_disagreement_score":0.0252351,"about_ca_system_score_codex":0.0010528894,"about_ca_system_score_gemma":0.0010047575,"threshold_uncertainty_score":0.050176382},"labels":[],"label_agreement":null},{"id":"W2327696940","doi":"10.14796/jwmm.r207-23","title":"Modeling Siphon Weirs within EXTRAN","year":2001,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"Siphon (mollusc); Geology; Environmental science; Hydrology (agriculture); Geotechnical engineering; Biology; Anatomy","score_opus":0.015947144319195795,"score_gpt":0.1917507044681957,"score_spread":0.1758035601489999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327696940","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":[0.55710584,0.00021112525,0.41176513,0.0001460584,0.000115542585,0.0001767664,0.0008766309,0.0029111162,0.026691819],"genre_scores_gemma":[0.9286076,0.00018687714,0.06057807,0.000029822675,0.00001781089,0.0001816534,0.000485664,0.00027250894,0.009640053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998293,0.000031975604,0.000009935381,0.000035591507,0.00006280414,0.000030433303],"domain_scores_gemma":[0.9997669,0.00008149095,0.000039336308,0.000037155176,0.000048161484,0.00002694541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022026502,0.00041881506,0.00058745086,0.00029457186,0.00040973772,0.00053700426,0.0011117804,0.00077173,0.0027952217],"category_scores_gemma":[0.00072678184,0.00042445952,0.00050092686,0.00025907476,0.00036648876,0.0007370706,0.00077214045,0.00048820232,0.00031013082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015121354,0.000016863893,0.0010275267,0.00001889209,0.000006609301,0.000051626554,0.00002898836,0.99157554,0.0022735698,0.0016350925,0.00016899515,0.003181187],"study_design_scores_gemma":[0.000007627745,0.00001218679,0.0002796956,0.0000028240836,0.0000033870292,0.000012598871,0.0000060679804,0.99738175,0.00095008034,0.0004031306,0.00093573437,0.0000048867455],"about_ca_topic_score_codex":0.010568374,"about_ca_topic_score_gemma":0.010271074,"teacher_disagreement_score":0.010568374,"about_ca_system_score_codex":0.0008398656,"about_ca_system_score_gemma":0.0010440472,"threshold_uncertainty_score":0.021013737},"labels":[],"label_agreement":null},{"id":"W2328546530","doi":"10.1061/40644(2002)325","title":"Csoft - A New Software for the Design and Real Time Operation of Sewer Networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Quest University Canada","funders":"","keywords":"Computer science; Software; Software engineering; Operating system","score_opus":0.015308912412758911,"score_gpt":0.17537556972509283,"score_spread":0.1600666573123339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328546530","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":[0.002548166,0.00021371669,0.8989435,0.00012834251,0.00019654004,0.00014512132,0.001765777,0.08588847,0.010170259],"genre_scores_gemma":[0.0463264,0.00092852965,0.8656406,0.00044839783,0.00013592107,0.0010701201,0.010085902,0.04817787,0.027186206],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989304,0.00011838224,0.000104053346,0.000116526,0.0006363514,0.000094217525],"domain_scores_gemma":[0.99759954,0.0010504336,0.00015602006,0.00035897276,0.00071361224,0.000121342375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010663286,0.0018227206,0.0012944661,0.0012220425,0.00061838416,0.0017945553,0.0034344508,0.0012992165,0.039868023],"category_scores_gemma":[0.003229212,0.0017396549,0.0020235684,0.001231061,0.00060255366,0.0020519325,0.0011639551,0.0033576728,0.0108020445],"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":[0.0003954249,0.0003370272,0.0021851414,0.0016103053,0.00044640506,0.0004760171,0.0004924384,0.24267697,0.042124264,0.040681224,0.26619196,0.40238273],"study_design_scores_gemma":[0.0004055477,0.0001199602,0.0010482775,0.00018889747,0.00014271025,0.00053083757,0.000049874827,0.59939253,0.027005685,0.018779993,0.35215497,0.00018072968],"about_ca_topic_score_codex":0.0063075228,"about_ca_topic_score_gemma":0.008380005,"teacher_disagreement_score":0.039868023,"about_ca_system_score_codex":0.0008627081,"about_ca_system_score_gemma":0.0023235194,"threshold_uncertainty_score":0.13337183},"labels":[],"label_agreement":null},{"id":"W2328659470","doi":"10.1115/ipc2014-33652","title":"Laboratory and Numerical Simulations of Pipeline Leakage Behaviour: Volatile Organic Compound (VOC) Migration Through Porous Media and Subsequent Atmospheric Dispersion","year":2014,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Alberta Innovates","funders":"","keywords":"Pipeline transport; Leakage (economics); Environmental science; Volatile organic compound; Petroleum engineering; Diluent; Porous medium; Atmosphere (unit); Porosity; Geotechnical engineering; Geology; Environmental engineering; Meteorology; Chemistry","score_opus":0.0066302606806633154,"score_gpt":0.19299815275233362,"score_spread":0.18636789207167032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328659470","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":[0.9853485,0.00025229773,0.006167346,0.00023988364,0.000042132277,0.00008356452,0.001034726,0.000113077775,0.006718383],"genre_scores_gemma":[0.99398524,0.00017391559,0.00415255,0.000027970083,0.000006413486,0.00009896472,0.00028051037,0.000011652874,0.0012628512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998056,0.000025843372,0.000013650082,0.000038823287,0.00004028399,0.00007570001],"domain_scores_gemma":[0.9982817,0.0011277209,0.00018701791,0.000065190026,0.00024905594,0.000089258996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004271501,0.00055775646,0.0006309418,0.0006003802,0.00061930105,0.00086884695,0.00083607755,0.002028194,0.0020337957],"category_scores_gemma":[0.0016925635,0.00031521835,0.0008397867,0.0006250345,0.0007954563,0.00059267064,0.0006244938,0.000974222,0.0001919577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016788093,0.00022005492,0.005763933,0.00010810694,0.00001897894,0.00018907772,0.00007031038,0.9827877,0.007615695,0.0010434312,0.00022458867,0.001790303],"study_design_scores_gemma":[0.000028258759,0.00011222366,0.001113773,0.000008188232,0.000008640474,0.000013554744,0.000065609536,0.99536335,0.0029126722,0.00017520238,0.00018701295,0.000011363626],"about_ca_topic_score_codex":0.024880853,"about_ca_topic_score_gemma":0.010572169,"teacher_disagreement_score":0.024880853,"about_ca_system_score_codex":0.0010376627,"about_ca_system_score_gemma":0.0009517841,"threshold_uncertainty_score":0.049472094},"labels":[],"label_agreement":null},{"id":"W2329219963","doi":"10.1061/9780784413548.049","title":"Trunk Water Main Failure Consequence Modeling of Hydraulic Failure and Fire Flow","year":2014,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2014","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Trunk; Index (typography); Environmental science; Range (aeronautics); Computer science; Engineering; Ecology","score_opus":0.0035154591464046166,"score_gpt":0.1452087320328131,"score_spread":0.14169327288640848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329219963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4458207,0.00044306504,0.5315689,0.00054463354,0.00006814772,0.00012581782,0.0006626679,0.00024699283,0.020519203],"genre_scores_gemma":[0.9880576,0.00017508416,0.0068377554,0.000022555158,0.000019928542,0.000051977717,0.0001420842,0.000029816938,0.0046633044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999705,0.00009025303,0.000014918211,0.000050587932,0.000063954685,0.00007523805],"domain_scores_gemma":[0.9993438,0.00031067047,0.00013187964,0.000034966797,0.00011623896,0.00006233095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071166496,0.0008104723,0.000597314,0.0007452385,0.00034964154,0.0009012162,0.0010585447,0.0012542713,0.00220182],"category_scores_gemma":[0.0016918941,0.00036900977,0.0009929319,0.0005538403,0.0007274304,0.0011698435,0.0007330264,0.00083394675,0.00017015984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013783305,0.000012220942,0.0010480917,0.000008163277,0.0000065196386,0.00005212655,0.0000103756765,0.99447775,0.0002707734,0.0028327315,0.000114149814,0.0011533303],"study_design_scores_gemma":[0.0000017685201,0.000008179823,0.0003460678,0.0000013190291,0.0000026085736,0.000007482792,0.000005703319,0.9984102,0.00006349761,0.0010711203,0.00008000667,0.0000019794004],"about_ca_topic_score_codex":0.020338316,"about_ca_topic_score_gemma":0.014299995,"teacher_disagreement_score":0.020338316,"about_ca_system_score_codex":0.0011506046,"about_ca_system_score_gemma":0.0009887415,"threshold_uncertainty_score":0.040439904},"labels":[],"label_agreement":null},{"id":"W2329618392","doi":"10.1061/41036(342)47","title":"Comparative Evaluation of Two Algorithms for Locating Contaminant Ingress Points","year":2009,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2009","topic":"Water Systems and 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 Guelph","funders":"","keywords":"Node (physics); Event (particle physics); Algorithm; Computer science; Identification (biology); Metric (unit); False alarm; Hazard; Data mining; Stage (stratigraphy); ALARM; Real-time computing; Engineering; Artificial intelligence; Geology; Structural engineering","score_opus":0.021041646099538402,"score_gpt":0.24484216380825294,"score_spread":0.22380051770871454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329618392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25439164,0.0059096282,0.7242715,0.0006945347,0.00042717424,0.000502489,0.0009179756,0.007979018,0.004906026],"genre_scores_gemma":[0.3932575,0.0011654337,0.60155964,0.00020629496,0.00006533604,0.00023676094,0.0017485165,0.00015892345,0.0016016067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963403,0.00084224687,0.0005489458,0.000953126,0.001044386,0.00027108603],"domain_scores_gemma":[0.98705184,0.00909195,0.0007294281,0.0006540544,0.0021865258,0.00028620072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037894554,0.0018521133,0.0022703833,0.0051738955,0.00085356244,0.0022957996,0.0024787146,0.0022244917,0.0011201564],"category_scores_gemma":[0.013215135,0.00046789434,0.0011839815,0.0036447,0.0005312311,0.0023933004,0.0011159148,0.00080290984,0.0005092844],"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":[0.0016716551,0.0007793081,0.01959871,0.00078574946,0.0004592983,0.00015442983,0.0002145502,0.22186172,0.0055201673,0.002806835,0.0026672257,0.7434803],"study_design_scores_gemma":[0.0001069155,0.00035283074,0.00353112,0.000034744626,0.00009933832,0.00017818052,0.00023270812,0.9871279,0.0054421076,0.0012550658,0.0016142058,0.000024875233],"about_ca_topic_score_codex":0.010567626,"about_ca_topic_score_gemma":0.006912746,"teacher_disagreement_score":0.010567626,"about_ca_system_score_codex":0.0017906528,"about_ca_system_score_gemma":0.002613909,"threshold_uncertainty_score":0.021012247},"labels":[],"label_agreement":null},{"id":"W2329784961","doi":"10.2166/hydro.2010.127","title":"Life-cycle assessment of common water main materials in water distribution networks","year":2010,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"Queen's University; University of Toronto","funders":"","keywords":"Truck; Life-cycle assessment; Electricity; Environmental science; Greenhouse gas; Waste management; Renewable energy; Environmental impact assessment; Natural gas; Environmental engineering; Engineering; Production (economics); Automotive engineering; Ecology","score_opus":0.0034109547509647135,"score_gpt":0.2019922015094616,"score_spread":0.1985812467584969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329784961","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":[0.96386003,0.0003143032,0.028675044,0.00012338329,0.000008561843,0.00012211433,0.0010857466,0.000047439105,0.0057633794],"genre_scores_gemma":[0.99233365,0.00015187284,0.0050817574,0.00001160137,0.0000011621917,0.000064915424,0.0006494245,0.000015350512,0.0016903463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967587,0.00014020271,0.0000085394295,0.000037785354,0.00008477238,0.000052830143],"domain_scores_gemma":[0.9992829,0.00039833493,0.00008715383,0.00004028375,0.00015215663,0.000039242568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012174358,0.0005875018,0.00037009435,0.0011023406,0.0003409497,0.0006921616,0.000536249,0.0005504253,0.001595603],"category_scores_gemma":[0.00209662,0.000343653,0.00070493686,0.0010407988,0.0004084732,0.0010482196,0.0005030869,0.00029551247,0.000097318894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003593573,0.000020982805,0.0025921206,0.000020975442,0.0000194254,0.000036676138,0.000010016004,0.9919829,0.000574998,0.0007237871,0.00006610084,0.0039161774],"study_design_scores_gemma":[0.00000901426,0.000107189866,0.0032819137,0.000008514596,0.000020704987,0.000016491122,0.000044390275,0.99219465,0.0019792973,0.0018097901,0.00052008,0.000007897554],"about_ca_topic_score_codex":0.023405472,"about_ca_topic_score_gemma":0.024088047,"teacher_disagreement_score":0.023405472,"about_ca_system_score_codex":0.0032136282,"about_ca_system_score_gemma":0.0008449721,"threshold_uncertainty_score":0.046538472},"labels":[],"label_agreement":null},{"id":"W2329872555","doi":"10.1615/ihtc13.p12.290","title":"PHASE DISTRIBUTION AND PRESSURE DROP OF AIR-WATER FLOWS IN A HORIZONTAL IMPACTING TEE JUNCTION","year":2006,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Manitoba","funders":"","keywords":"Mechanics; Pressure drop; Drop (telecommunication); Air water; Phase (matter); Two-phase flow; Materials science; Environmental science; Electrical engineering; Physics; Flow (mathematics); Engineering","score_opus":0.003481894192751083,"score_gpt":0.18446102222517555,"score_spread":0.18097912803242447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329872555","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":[0.99748755,0.000029174464,0.0010978191,0.000030112416,0.0000066244224,0.000007294756,0.000091267066,0.000040540825,0.001209609],"genre_scores_gemma":[0.99927884,0.000016703125,0.0002378465,0.0000033829217,0.0000030376705,0.0000033986212,0.000064397114,0.0000054636157,0.00038693086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999119,0.0000106665175,0.0000028823763,0.000019177973,0.000022628596,0.000032785887],"domain_scores_gemma":[0.9997415,0.00011890804,0.00002694177,0.000009368134,0.000065226865,0.00003804533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017001889,0.00018537416,0.00039452652,0.0005756812,0.00053352345,0.0005107925,0.00031578867,0.00044171378,0.0014241318],"category_scores_gemma":[0.0004879301,0.00019156605,0.00022880905,0.000399507,0.00052233157,0.0004460638,0.0002895711,0.00052423397,0.00013077167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0052216537,0.0011556566,0.06449307,0.00018454982,0.00012619235,0.003192724,0.0005725948,0.47188112,0.41813472,0.007709303,0.0013844228,0.025943931],"study_design_scores_gemma":[0.000102348844,0.0005547762,0.054192502,0.000008056819,0.000032995362,0.00013344007,0.00037892666,0.87391055,0.069353916,0.0007587537,0.0005289519,0.000044721128],"about_ca_topic_score_codex":0.006491857,"about_ca_topic_score_gemma":0.0025792145,"teacher_disagreement_score":0.006491857,"about_ca_system_score_codex":0.00046980672,"about_ca_system_score_gemma":0.00037088958,"threshold_uncertainty_score":0.012908161},"labels":[],"label_agreement":null},{"id":"W2330051788","doi":"10.1061/41202(423)1","title":"Sewer System Performance Analysis Based on Monitoring and Modeling Technology in Plain Areas of the South of China","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Major Science and Technology Program for Water Pollution Control and Treatment; York University","keywords":"Sewerage; Environmental science; Sanitary sewer; Inflow; Drainage; Combined sewer; Hydrology (agriculture); Storm Water Management Model; Wastewater; Geographic information system; Current (fluid); Stormwater; Groundwater recharge; Environmental engineering; Engineering; Geology; Geotechnical engineering; Surface runoff; Groundwater; Aquifer; Remote sensing","score_opus":0.010629876907749301,"score_gpt":0.1634107071265643,"score_spread":0.152780830218815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330051788","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":[0.99692637,0.000017099597,0.0022065535,0.000012075255,9.811454e-7,0.000011494214,0.000096400254,0.0000735724,0.0006554871],"genre_scores_gemma":[0.99903166,0.000022503442,0.0006003583,0.0000014352601,4.8126066e-7,0.000009346049,0.00009751474,0.000004020621,0.00023265398],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998305,0.000020019617,0.000014288012,0.00005097267,0.000054341064,0.0000298197],"domain_scores_gemma":[0.9997974,0.000035486828,0.0000469732,0.000027512522,0.000076788885,0.000015906087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026259662,0.00044580706,0.00025818803,0.000815465,0.0003058763,0.0004348574,0.00041002178,0.00023560844,0.0005251379],"category_scores_gemma":[0.00043241176,0.00020230739,0.00035142535,0.0010025358,0.00021554602,0.0005658937,0.0003231791,0.00012074079,0.00008666246],"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":[0.00024671218,0.00022742544,0.48319387,0.00014420357,0.00009364821,0.00055125,0.0007158781,0.41787452,0.04053027,0.0010192175,0.00088898075,0.054514036],"study_design_scores_gemma":[0.000014880588,0.00010690193,0.26740834,0.000006271726,0.000042882366,0.0000495795,0.00025236243,0.7238406,0.0075335293,0.00025943608,0.0004624161,0.0000227717],"about_ca_topic_score_codex":0.062207494,"about_ca_topic_score_gemma":0.044056963,"teacher_disagreement_score":0.062207494,"about_ca_system_score_codex":0.0010056515,"about_ca_system_score_gemma":0.0007616905,"threshold_uncertainty_score":0.123690784},"labels":[],"label_agreement":null},{"id":"W2330183689","doi":"10.2166/wqrjc.2013.058","title":"Decision support system to select sustainable point-of-use/point-of-entry treatment systems (D4SPOUTS)","year":2013,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Water Systems and 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; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Decision support system; Usability; Point (geometry); Sustainability; Computer science; Process (computing); Risk analysis (engineering); Operations research; Systems engineering; Engineering; Management science; Data mining; Business; Human–computer interaction; Mathematics","score_opus":0.052307128650033854,"score_gpt":0.3263004228195849,"score_spread":0.27399329416955104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330183689","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":[0.10036805,0.0003589821,0.8011297,0.0014766264,0.00029121898,0.0027607384,0.0066336184,0.07251844,0.014462718],"genre_scores_gemma":[0.4422044,0.0003558333,0.54242045,0.000597844,0.00009187351,0.0015602432,0.0040452327,0.00043231316,0.008291773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916685,0.0002589736,0.00015548313,0.00017347926,0.00018907529,0.00005628476],"domain_scores_gemma":[0.9961798,0.002610064,0.00024888947,0.00019958669,0.0005502182,0.00021141116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023652767,0.0011046212,0.0009305652,0.0018091669,0.0006043484,0.002151942,0.0011709906,0.0012168161,0.014532417],"category_scores_gemma":[0.006168322,0.00039199897,0.0005947137,0.0008682701,0.00027687472,0.0012645146,0.0010534507,0.00079587474,0.003019459],"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":[0.0035082372,0.0016034706,0.012249114,0.0015649154,0.00025589872,0.0016526774,0.0009968973,0.09901059,0.03246799,0.015263916,0.050351415,0.7810748],"study_design_scores_gemma":[0.0010760342,0.00080001366,0.003725488,0.0002624541,0.0001839498,0.00042239533,0.0003799068,0.8859455,0.03932278,0.011525977,0.056184106,0.00017133681],"about_ca_topic_score_codex":0.002069103,"about_ca_topic_score_gemma":0.0015796218,"teacher_disagreement_score":0.014532417,"about_ca_system_score_codex":0.00071339577,"about_ca_system_score_gemma":0.0011368667,"threshold_uncertainty_score":0.048615754},"labels":[],"label_agreement":null},{"id":"W2330470504","doi":"10.1061/40737(2004)444","title":"Water Quality Implication of Using SCADA Data Directly Within the Unified Hydraulic Solver","year":2004,"lang":"en","type":"article","venue":"Critical Transitions in Water and Environmental Resources Management","topic":"Water Systems and 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 Calgary","funders":"","keywords":"SCADA; Solver; Computer science; Water quality; Hydraulic machinery; Hydraulic engineering; Supervisor; Environmental science; Petroleum engineering; Control theory (sociology); Engineering; Control (management); Mechanical engineering","score_opus":0.026723841909356394,"score_gpt":0.2464423740548432,"score_spread":0.2197185321454868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330470504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07566916,0.00030185966,0.8969635,0.0058941995,0.00028892898,0.000076989374,0.0003722656,0.0011423365,0.019290764],"genre_scores_gemma":[0.87272805,0.00020077938,0.12407533,0.00052585494,0.00007260002,0.00006087616,0.00021081249,0.00017807892,0.0019475494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966794,0.0018929727,0.00015471563,0.00031446674,0.00081564096,0.00014276634],"domain_scores_gemma":[0.98841256,0.006171062,0.0007948269,0.0016455122,0.0028104545,0.0001656135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054244474,0.0005884489,0.00062943064,0.00036440903,0.00050511555,0.0016915849,0.0011038118,0.0007731911,0.002921399],"category_scores_gemma":[0.023167187,0.00040205306,0.00036882618,0.00062048674,0.0011254562,0.0028340141,0.001483437,0.0018844625,0.00023532916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002985818,0.00013415056,0.0071251756,0.00015412462,0.00014619694,0.00018097229,0.00013511404,0.71147436,0.0037679635,0.18754046,0.005264744,0.08377819],"study_design_scores_gemma":[0.000057469224,0.000077454046,0.0006738492,0.000023787177,0.000021630232,0.000017478482,0.000036346046,0.9615864,0.0021597387,0.03249303,0.0028396084,0.000013182057],"about_ca_topic_score_codex":0.017756665,"about_ca_topic_score_gemma":0.011969041,"teacher_disagreement_score":0.017756665,"about_ca_system_score_codex":0.00094379816,"about_ca_system_score_gemma":0.0022054394,"threshold_uncertainty_score":0.035306633},"labels":[],"label_agreement":null},{"id":"W2331095248","doi":"10.1061/41024(340)15","title":"Impact of Climate Change on Hydraulic Performance in Water Distribution Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Climate change; Environmental science; Latitude; Node (physics); Mean radiant temperature; Coefficient of variation; Distribution (mathematics); Engineering; Mathematics; Statistics; Geology","score_opus":0.008844450090288997,"score_gpt":0.20481995724502905,"score_spread":0.19597550715474005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331095248","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":[0.9912975,0.00019204395,0.004770081,0.0002365161,0.000014916264,0.000006345019,0.00018274109,0.000049951774,0.0032499342],"genre_scores_gemma":[0.99951136,0.000056037825,0.00020923308,0.0000063770835,0.0000039602787,0.0000018177078,0.000046948207,0.00000498333,0.0001593072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993913,0.00024156626,0.00002023354,0.00007408169,0.00013545845,0.0001373407],"domain_scores_gemma":[0.9972013,0.0019044491,0.00029681966,0.00016233599,0.0003411766,0.00009387101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010788948,0.00026104145,0.0002591029,0.00045958182,0.00036415327,0.00075970276,0.00023298652,0.0004056252,0.0010206929],"category_scores_gemma":[0.0039407024,0.00012703735,0.00026486666,0.0008923894,0.0005149929,0.00089729833,0.0005286398,0.00033787455,0.00012948987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013935089,0.0000389766,0.048310798,0.000042902608,0.000041018146,0.00025687492,0.0001055313,0.92874295,0.0070455186,0.0016618997,0.00038479056,0.013229322],"study_design_scores_gemma":[0.000015649468,0.0003965458,0.15237372,0.000018224924,0.000052941974,0.00021811789,0.00050195464,0.8244305,0.01602998,0.003929542,0.0019703286,0.00006249885],"about_ca_topic_score_codex":0.0060145212,"about_ca_topic_score_gemma":0.0065705366,"teacher_disagreement_score":0.0060145212,"about_ca_system_score_codex":0.0010061671,"about_ca_system_score_gemma":0.0003200556,"threshold_uncertainty_score":0.011959016},"labels":[],"label_agreement":null},{"id":"W2331212241","doi":"10.1061/40994(321)59","title":"Integration of Geographic Information System and Probabilistic Analysis for Optimized Pipe Infrastructure Decisions","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Queen's University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Geographic information system; Scheduling (production processes); Computer science; Pipeline transport; Pipeline (software); Information system; Service (business); Operations research; Unit (ring theory); Engineering; Operations management; Business; Environmental engineering; Geography","score_opus":0.008817036883485486,"score_gpt":0.1849550728455162,"score_spread":0.17613803596203073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331212241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03347243,0.00018650184,0.9569389,0.00037052634,0.000028753451,0.0001630724,0.00043720275,0.0011781969,0.007224345],"genre_scores_gemma":[0.60741746,0.00032601616,0.3893763,0.00006881765,0.000040514995,0.00021821642,0.0006777409,0.00018563269,0.0016891478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985251,0.0006288174,0.0001062614,0.00013843366,0.0005102562,0.00009106503],"domain_scores_gemma":[0.9972249,0.0016501675,0.00027153044,0.0001795292,0.00062626903,0.000047573907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002706126,0.0007373098,0.0009977377,0.0028615666,0.00058416475,0.0020367505,0.00078216655,0.000735104,0.0036477125],"category_scores_gemma":[0.007708205,0.0008502703,0.0007705322,0.002828152,0.0004789805,0.0021125637,0.0010364056,0.0006066212,0.00043119292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041116276,0.000033213975,0.0012840402,0.00003350015,0.00003596864,0.0000387844,0.000026508284,0.95142007,0.0003103106,0.010044523,0.0006487981,0.036083117],"study_design_scores_gemma":[0.000005155549,0.0000120282575,0.00038426663,0.0000041613525,0.000012016483,0.000008150549,0.000010287823,0.99507314,0.00018341682,0.003712689,0.00058623427,0.00000844681],"about_ca_topic_score_codex":0.029014384,"about_ca_topic_score_gemma":0.034350544,"teacher_disagreement_score":0.029014384,"about_ca_system_score_codex":0.0020777478,"about_ca_system_score_gemma":0.0028495057,"threshold_uncertainty_score":0.057690978},"labels":[],"label_agreement":null},{"id":"W2331258592","doi":"10.1061/41173(414)17","title":"Multi-Objective Design Optimization of Branched Pipeline Systems: Analytical Probabilistic Assessment of Fire Flow Failure","year":2011,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2011","topic":"Water Systems and 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":"Queen's University; University of Toronto","funders":"","keywords":"Sorting; Probabilistic logic; Mathematical optimization; Flow (mathematics); Multi-objective optimization; Pareto principle; Computer science; Probability distribution; Pipeline (software); Engineering; Mathematics; Algorithm; Artificial intelligence; Statistics","score_opus":0.01558202438254242,"score_gpt":0.19034326014190764,"score_spread":0.17476123575936522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331258592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06355335,0.00040037298,0.93152714,0.0001900826,0.000019497098,0.0000790732,0.00006360367,0.00009377771,0.0040730676],"genre_scores_gemma":[0.892354,0.00046952156,0.10380068,0.000045910525,0.000018643666,0.00023651529,0.00008174935,0.00005796034,0.002934952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992993,0.00031452754,0.000020150186,0.000065992586,0.00021414802,0.000085869455],"domain_scores_gemma":[0.9986803,0.0008855303,0.00018310644,0.000039140516,0.00016128235,0.000050659703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021053725,0.001134076,0.001042902,0.0010966861,0.00049568096,0.0010717512,0.0008641795,0.0012448231,0.001311142],"category_scores_gemma":[0.0033112015,0.0007808912,0.00089922105,0.0008923928,0.0008234446,0.00096334855,0.0009251539,0.0007402456,0.00014251961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042855672,0.0000036076913,0.00005130315,0.000007985691,0.0000044316494,0.0000073141186,0.0000035675891,0.99796754,0.000109593246,0.0005346436,0.000014106213,0.0012916357],"study_design_scores_gemma":[0.0000021120572,0.000012173063,0.000038149035,0.0000021594972,0.0000022260379,0.0000031259851,0.0000020504917,0.99902034,0.00009443471,0.0007594856,0.0000623046,0.0000014046798],"about_ca_topic_score_codex":0.0039243256,"about_ca_topic_score_gemma":0.0036489242,"teacher_disagreement_score":0.0039243256,"about_ca_system_score_codex":0.0015002784,"about_ca_system_score_gemma":0.0015684584,"threshold_uncertainty_score":0.011134386},"labels":[],"label_agreement":null},{"id":"W2331313822","doi":"10.1061/41203(425)138","title":"Hydraulic Calibration for a Small Water Distribution Network","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Calibration; Sensitivity (control systems); Computer science; Monte Carlo method; Genetic algorithm; Simulation; Mathematical optimization; Control theory (sociology); Engineering; Electronic engineering; Mathematics; Statistics; Artificial intelligence","score_opus":0.021524741182607835,"score_gpt":0.1636919400254217,"score_spread":0.14216719884281387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331313822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24535672,0.00011496333,0.70649654,0.000656738,0.00008977936,0.00059636595,0.0032748228,0.0059847683,0.037429284],"genre_scores_gemma":[0.90954036,0.00010623242,0.08190815,0.000070254326,0.000010296788,0.00028816995,0.001357151,0.00025254043,0.0064668353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993734,0.00017650764,0.000029733952,0.00014969525,0.00022739891,0.000043205953],"domain_scores_gemma":[0.9989287,0.00045707423,0.00007926403,0.00018311114,0.0003208972,0.000030866577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009934296,0.00047909486,0.00033211283,0.00068120554,0.00060318626,0.0007191549,0.00086161704,0.0006367405,0.010567666],"category_scores_gemma":[0.004025014,0.00029453027,0.000355846,0.0013403518,0.00042185167,0.0009982274,0.0006005564,0.0008428845,0.00091098086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009133514,0.00006825144,0.0025477193,0.000058391972,0.00001280467,0.00008797086,0.000059286456,0.95229113,0.008361875,0.0051607974,0.002225024,0.029035363],"study_design_scores_gemma":[0.00003121623,0.0000685158,0.0018145057,0.000010950031,0.0000088893485,0.00003213308,0.000046995152,0.9829715,0.0069808112,0.0029776813,0.00502949,0.000027309068],"about_ca_topic_score_codex":0.015875535,"about_ca_topic_score_gemma":0.0151712345,"teacher_disagreement_score":0.015875535,"about_ca_system_score_codex":0.0016019165,"about_ca_system_score_gemma":0.0015292051,"threshold_uncertainty_score":0.03535235},"labels":[],"label_agreement":null},{"id":"W2332303347","doi":"10.1061/40569(2001)402","title":"Reconstruction of Hydraulic Management of a Water Distribution System Using Genetic Algorithms","year":2001,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Agency for Toxic Substances and Disease Registry; University of Waterloo","keywords":"Solver; Genetic algorithm; Computer science; Water supply; Frame (networking); Optimization problem; Mathematical optimization; Distribution (mathematics); Operations research; Environmental science; Algorithm; Engineering; Mathematics; Environmental engineering; Telecommunications","score_opus":0.009775322648816637,"score_gpt":0.18253648670670014,"score_spread":0.1727611640578835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332303347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36815476,0.00027799312,0.6201136,0.0006992063,0.000044919267,0.0002807655,0.0005199986,0.0011606185,0.00874816],"genre_scores_gemma":[0.8267731,0.00012759474,0.17043675,0.000068643014,0.000012785206,0.00025185026,0.00044956375,0.00006804118,0.0018115994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999772,0.00008766768,0.000014833266,0.000052276024,0.000031414485,0.00004178729],"domain_scores_gemma":[0.998757,0.00081401336,0.00014050397,0.000041390074,0.00019933927,0.000047789526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009669487,0.0006948433,0.00070120214,0.0014237915,0.0007238522,0.0011603246,0.0009646087,0.0013413318,0.0019024515],"category_scores_gemma":[0.0032169016,0.0007718395,0.00068660517,0.0007441456,0.0007358221,0.0007388308,0.0005754399,0.00067671714,0.00017575697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005513828,0.0000058985065,0.000521477,0.000003892496,0.0000029223515,0.000009828318,0.000007641845,0.99712807,0.000051197607,0.00027195498,0.00004159824,0.0019500189],"study_design_scores_gemma":[0.0000046093737,0.0000046623823,0.00009951857,0.00000199833,0.0000023563289,0.0000020384334,0.000014001047,0.9993754,0.000056914014,0.00035059062,0.00008615711,0.0000018533034],"about_ca_topic_score_codex":0.056522485,"about_ca_topic_score_gemma":0.038343076,"teacher_disagreement_score":0.056522485,"about_ca_system_score_codex":0.0024086162,"about_ca_system_score_gemma":0.0031662348,"threshold_uncertainty_score":0.112387},"labels":[],"label_agreement":null},{"id":"W2332662134","doi":"10.1061/9780784412947.068","title":"Forecasting Water Main Failure Using Artificial Neural Network and Generalized Linear Models","year":2013,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2013","topic":"Water Systems and 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":true,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial neural network; Computer science; Calibration; Prioritization; Generalized linear model; Predictive modelling; Reliability engineering; Operations research; Machine learning; Engineering; Statistics; Management science; Mathematics","score_opus":0.015391772191093648,"score_gpt":0.1681562043800065,"score_spread":0.15276443218891286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332662134","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":[0.7130697,0.0009847321,0.27763608,0.00097204815,0.00009348448,0.00007892704,0.0011118965,0.00081371085,0.0052394327],"genre_scores_gemma":[0.98835224,0.0002034716,0.009063783,0.000029747227,0.00001745134,0.00003637574,0.00040107587,0.000013726718,0.0018820695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972624,0.00010312827,0.00001707362,0.000058635265,0.00005118812,0.000043742573],"domain_scores_gemma":[0.9990577,0.0006604044,0.00009936177,0.000021288755,0.00013257342,0.000028661283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006886425,0.0007216178,0.0005350178,0.0006322676,0.00027464234,0.00067259127,0.00073567627,0.00072606636,0.0010726991],"category_scores_gemma":[0.0026798768,0.00035030383,0.00039435131,0.0006685389,0.00034821316,0.0005716842,0.00041459978,0.00071825564,0.00018021504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016867596,0.000010702838,0.0012267501,0.00000936191,0.000009827603,0.000014104928,0.0000068299146,0.99439865,0.00008077297,0.00018442591,0.0001549288,0.0038867146],"study_design_scores_gemma":[8.180112e-7,0.0000027097735,0.00019524171,6.131717e-7,7.48008e-7,5.2958814e-7,0.0000018948216,0.99964917,0.000014888299,0.000113081755,0.000019301244,0.0000010803587],"about_ca_topic_score_codex":0.13768761,"about_ca_topic_score_gemma":0.107239,"teacher_disagreement_score":0.13768761,"about_ca_system_score_codex":0.0013910275,"about_ca_system_score_gemma":0.00090426364,"threshold_uncertainty_score":0.27377236},"labels":[],"label_agreement":null},{"id":"W2332807014","doi":"10.1061/41024(340)57","title":"Spatial Analysis Tool for Development of Leakage Control Zones from the Analogy of Distributed Computing","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":34,"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":"Graph partition; Computer science; Zoning; Partition (number theory); Distributed computing; Python (programming language); Graph; Mathematical optimization; Theoretical computer science; Engineering; Mathematics; Operating system","score_opus":0.007467952023044728,"score_gpt":0.19766805257101305,"score_spread":0.19020010054796832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332807014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015524295,0.000047946418,0.98106855,0.00009041637,0.000030610532,0.000073313015,0.0006593007,0.012468301,0.0040091653],"genre_scores_gemma":[0.03713013,0.00016374065,0.95414966,0.00007238269,0.000016424261,0.00037400023,0.0015741884,0.0025046254,0.0040148175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995952,0.0000701774,0.000035495763,0.000067091714,0.00019380168,0.00003827929],"domain_scores_gemma":[0.9990067,0.0004743371,0.000078530706,0.00013620387,0.000267464,0.000036843052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007371489,0.000976454,0.0005235271,0.0015832778,0.0006238608,0.0016053711,0.0015385528,0.0006720812,0.022668164],"category_scores_gemma":[0.0025575554,0.00053663173,0.0010668006,0.0010522262,0.0005310947,0.0014526423,0.001325589,0.0013051578,0.003274825],"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":[0.00019336308,0.0001322445,0.0030170053,0.001560277,0.00009777908,0.0005770124,0.0011121845,0.27175516,0.022676049,0.21211204,0.068118855,0.41864794],"study_design_scores_gemma":[0.00006602435,0.000053257976,0.00085039355,0.00019245993,0.00003743523,0.00031066957,0.00020849373,0.7216547,0.021766124,0.05468753,0.20011504,0.00005791307],"about_ca_topic_score_codex":0.0042233574,"about_ca_topic_score_gemma":0.004578206,"teacher_disagreement_score":0.022668164,"about_ca_system_score_codex":0.00070647727,"about_ca_system_score_gemma":0.0018588639,"threshold_uncertainty_score":0.075832546},"labels":[],"label_agreement":null},{"id":"W2333125058","doi":"10.1061/41203(425)55","title":"Intrusion Modelling and the Effect of Ground Water Conditions","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Polytechnique Montréal; University of Toronto","funders":"","keywords":"Body orifice; Mechanics; Inflow; Hydrostatic equilibrium; Porous medium; Leak; Work (physics); Computational fluid dynamics; Geotechnical engineering; Aquifer; Orifice plate; Flow (mathematics); Geology; Intrusion; Groundwater; Petroleum engineering; Environmental science; Materials science; Porosity; Thermodynamics; Engineering; Physics; Environmental engineering; Mechanical engineering","score_opus":0.009208255596567343,"score_gpt":0.16531134020375793,"score_spread":0.15610308460719058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333125058","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":[0.8678521,0.0005202729,0.11387383,0.0003277778,0.00008322715,0.00009014701,0.00028709436,0.0005518462,0.01641371],"genre_scores_gemma":[0.9957982,0.00015125629,0.0026579897,0.000016010768,0.0000055368687,0.0000129636655,0.000050282655,0.000021139898,0.0012866929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997173,0.000072882816,0.000018601015,0.000051453662,0.000079705,0.000059975617],"domain_scores_gemma":[0.9994542,0.00030305292,0.000099053446,0.00004325666,0.000067848276,0.000032652246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023375417,0.00050622993,0.00056221,0.00041704593,0.00028222587,0.001294079,0.000650385,0.0012797224,0.001750285],"category_scores_gemma":[0.0017764363,0.00026350224,0.00070000393,0.0004492951,0.0008531822,0.0012690381,0.0009775362,0.00049517566,0.00016967054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000834106,0.000043557586,0.0030378415,0.000065389984,0.000017992126,0.00024086978,0.0001034498,0.9769925,0.010280395,0.0031084407,0.00014336705,0.005882801],"study_design_scores_gemma":[0.000008576229,0.00006269291,0.0011332997,0.000008361367,0.000011016434,0.000043052307,0.000041991116,0.99409914,0.0031543719,0.0009789034,0.0004494236,0.00000915623],"about_ca_topic_score_codex":0.009787152,"about_ca_topic_score_gemma":0.0029503733,"teacher_disagreement_score":0.009787152,"about_ca_system_score_codex":0.00066657015,"about_ca_system_score_gemma":0.00050383096,"threshold_uncertainty_score":0.01946032},"labels":[],"label_agreement":null},{"id":"W2333353852","doi":"10.1061/9780784413692.134","title":"Tell Me the Available Fire Flow for Every Pipe in the System: Integration of Model Calibration, Infrastructure Planning, and Operation","year":2014,"lang":"en","type":"article","venue":"Pipelines 2014","topic":"Water Systems and 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":"Custom Security Industries (Canada); Aecom (Canada); Canadian Water and Wastewater Association","funders":"","keywords":"Leverage (statistics); Computer science; Fire protection; Redundancy (engineering); Water flow; Calibration; Environmental science; Engineering; Reliability engineering; Civil engineering; Environmental engineering","score_opus":0.012196832753697644,"score_gpt":0.20682314061800136,"score_spread":0.1946263078643037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333353852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07748997,0.00060257013,0.8015757,0.022698792,0.00083366525,0.00034629303,0.005000318,0.021101309,0.070351355],"genre_scores_gemma":[0.6978367,0.0014443295,0.23649095,0.0013292292,0.00016542406,0.00026600613,0.0017846794,0.0016174412,0.059065193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975294,0.000065190536,0.000010814499,0.000041050436,0.00010083494,0.000029125667],"domain_scores_gemma":[0.99926454,0.00028038962,0.000072231756,0.00006999561,0.0002756075,0.00003725104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079603103,0.00088916917,0.00037387278,0.000441558,0.00042605045,0.0014450441,0.0005703157,0.0008623637,0.025590133],"category_scores_gemma":[0.0037570838,0.00040167934,0.0004834655,0.00043080753,0.0002791827,0.0022052452,0.00050297804,0.0010456543,0.006382984],"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":[0.0008423164,0.0002436307,0.02424867,0.0006617372,0.00009726736,0.00071646966,0.0018530918,0.2668647,0.020227369,0.01959055,0.23768368,0.42697054],"study_design_scores_gemma":[0.00012020264,0.0005135608,0.016136361,0.00025943574,0.00013269144,0.00071501563,0.0033726504,0.7600894,0.025583098,0.03188183,0.16091697,0.00027875364],"about_ca_topic_score_codex":0.011519014,"about_ca_topic_score_gemma":0.011851065,"teacher_disagreement_score":0.025590133,"about_ca_system_score_codex":0.0008830795,"about_ca_system_score_gemma":0.0007100503,"threshold_uncertainty_score":0.08560747},"labels":[],"label_agreement":null},{"id":"W2333364969","doi":"10.1061/41036(342)67","title":"Drinking Water Infrastructure Assessment: The National Research Council of Canada Perspective","year":2009,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2009","topic":"Water Systems and 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":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Asset management; Reliability (semiconductor); Quality (philosophy); Asset (computer security); Risk analysis (engineering); Investment (military); Business; Computer science; Perspective (graphical); Environmental economics; Computer security; Economics; Finance","score_opus":0.01367934179629828,"score_gpt":0.2101517982999493,"score_spread":0.196472456503651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333364969","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":[0.008280956,0.057970278,0.015363938,0.33745566,0.0035951543,0.00071564113,0.01311797,0.0004540466,0.5630463],"genre_scores_gemma":[0.30584377,0.19378921,0.124227,0.07162762,0.0019694038,0.00079205877,0.010294115,0.00063013675,0.2908267],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.984204,0.0013871314,0.0007911313,0.00086588174,0.01077575,0.001976187],"domain_scores_gemma":[0.96829724,0.0013028607,0.0005356693,0.00049711094,0.027264282,0.0021027885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008698904,0.0012947811,0.0012205115,0.011695655,0.005297516,0.010891309,0.0044644913,0.004024322,0.009305616],"category_scores_gemma":[0.014673159,0.0009389311,0.001233177,0.011790631,0.005961659,0.0042708027,0.003658652,0.0054438733,0.0012475426],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066645494,0.00017125237,0.010224285,0.0019573253,0.000146331,0.00060271757,0.0015048035,0.011567761,0.0012592634,0.36292055,0.43666947,0.17290963],"study_design_scores_gemma":[0.000025728847,0.00004723489,0.014565685,0.0024480522,0.00011524536,0.00010552357,0.003638382,0.0042190617,0.00058241124,0.030023094,0.94407076,0.00015877733],"about_ca_topic_score_codex":0.99368155,"about_ca_topic_score_gemma":0.99524873,"teacher_disagreement_score":0.8664124,"about_ca_system_score_codex":0.13358757,"about_ca_system_score_gemma":0.36503953,"threshold_uncertainty_score":0.9692502},"labels":[],"label_agreement":null},{"id":"W2333903492","doi":"10.1061/40976(316)494","title":"A New Algorithm for Water Distribution System Optimization: Discrete Dynamically Dimensioned Search","year":2008,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2008","topic":"Water Systems and 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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Genetic algorithm; Ant colony optimization algorithms; Meta-optimization; Algorithm; Mathematical optimization; Optimization problem; Optimization algorithm; Global optimization; Estimation of distribution algorithm; Mathematics; Machine learning; Geology","score_opus":0.004966172199650371,"score_gpt":0.1621437553842236,"score_spread":0.15717758318457323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333903492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023349645,0.00020213389,0.99524695,0.00010117008,0.00006109147,0.00004696158,0.000043536536,0.0002679111,0.0016953945],"genre_scores_gemma":[0.110037416,0.00038326634,0.8853862,0.00018477261,0.00005942455,0.0004724095,0.0002523397,0.00013597823,0.0030881178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921453,0.00023082473,0.000043808122,0.00012190086,0.00034560502,0.00004329985],"domain_scores_gemma":[0.9993144,0.00038201868,0.000055260367,0.00007519704,0.00014176109,0.000031281626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012049628,0.0008563363,0.0012276091,0.0010908694,0.00044203093,0.0010200721,0.0014135251,0.001119832,0.0026812905],"category_scores_gemma":[0.0026471687,0.00049232616,0.0008729812,0.0014042385,0.00079859904,0.0012257531,0.0015399415,0.0017913219,0.00059426663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007992444,0.00005780088,0.00066449435,0.00016600829,0.000099450306,0.000050862644,0.00008219252,0.7244023,0.0026748122,0.027015168,0.0039869053,0.24072003],"study_design_scores_gemma":[0.0000437543,0.00003997789,0.00008586617,0.000008941915,0.000009666433,0.000035086625,0.0000074833865,0.99069655,0.0005295331,0.004016497,0.0045176763,0.000008994618],"about_ca_topic_score_codex":0.0034155063,"about_ca_topic_score_gemma":0.003691392,"teacher_disagreement_score":0.0034155063,"about_ca_system_score_codex":0.0009490037,"about_ca_system_score_gemma":0.001256603,"threshold_uncertainty_score":0.008969784},"labels":[],"label_agreement":null},{"id":"W2334155832","doi":"10.1115/imece2012-86463","title":"A New Energy Optimization Strategy for Pumping Operation in Water Distribution Systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"McMaster University","funders":"","keywords":"Particle swarm optimization; Multi-objective optimization; Schedule; Computer science; Reliability (semiconductor); Pareto principle; Optimization problem; Mathematical optimization; Reliability engineering; Work (physics); Operations research; Engineering","score_opus":0.01429830288317938,"score_gpt":0.20050338411765015,"score_spread":0.18620508123447077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334155832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02892098,0.00022395788,0.9614955,0.00019719536,0.000033936663,0.00010913108,0.00005085158,0.000116448835,0.008851977],"genre_scores_gemma":[0.8090095,0.00036153852,0.18395425,0.00009923615,0.000028238479,0.00030973394,0.00008651,0.0000505177,0.006100533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999838,0.00005035014,0.000009904324,0.00002732779,0.000057559933,0.00001683976],"domain_scores_gemma":[0.9999161,0.000032685002,0.000011841573,0.0000056642016,0.00002683098,0.000006900906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044381013,0.00061578373,0.00040141557,0.00052016636,0.00032038594,0.00069889106,0.00050149136,0.00052016065,0.0016048799],"category_scores_gemma":[0.0005832951,0.00024996357,0.0004188416,0.00047764802,0.00031200604,0.00077017647,0.0005188044,0.00041521701,0.00017293329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030483869,0.000035235233,0.00029895446,0.000058971957,0.000018686202,0.000060581006,0.00003367435,0.9469262,0.0050117113,0.011730471,0.00058306026,0.035212077],"study_design_scores_gemma":[0.0000055439796,0.000038399485,0.00009760969,0.0000043362415,0.00000553944,0.0000114522145,0.000007048497,0.9967529,0.00076852925,0.0014460551,0.00085872086,0.000003902184],"about_ca_topic_score_codex":0.002205755,"about_ca_topic_score_gemma":0.0023831553,"teacher_disagreement_score":0.002205755,"about_ca_system_score_codex":0.00053564983,"about_ca_system_score_gemma":0.0006404067,"threshold_uncertainty_score":0.0053688884},"labels":[],"label_agreement":null},{"id":"W2334288794","doi":"10.1061/9780784479360.102","title":"Drinking Water Pipelines Defect Coding System","year":2015,"lang":"en","type":"article","venue":"Pipelines 2015","topic":"Water Systems and 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":"Thornhill Medical (Canada); University of Waterloo","funders":"Water Research Foundation","keywords":"Pipeline transport; Mains electricity; Produced water; Environmental science; Engineering; Forensic engineering; Civil engineering; Computer science; Environmental engineering","score_opus":0.022618192092028668,"score_gpt":0.22019290249967444,"score_spread":0.19757471040764576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334288794","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":[0.14288953,0.00037036798,0.6545626,0.0014678825,0.00048674626,0.0035000269,0.06071406,0.06215626,0.07385251],"genre_scores_gemma":[0.55268544,0.0006015392,0.2719461,0.0006133152,0.00011817603,0.0024481905,0.11127279,0.0024880152,0.05782636],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99712974,0.00027888673,0.00046224333,0.00043886548,0.001408952,0.00028137953],"domain_scores_gemma":[0.98813707,0.0009442848,0.0013778706,0.0017762842,0.007531906,0.00023251212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019257951,0.00094657816,0.0004888992,0.006201489,0.001005881,0.0016013477,0.0015977427,0.00087789726,0.013078519],"category_scores_gemma":[0.0070873825,0.00035339512,0.0005861053,0.0026015306,0.0007783051,0.0021166117,0.0017926405,0.0009099165,0.006112935],"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":[0.0011578035,0.0003518208,0.055587918,0.0010166239,0.000053269185,0.0010597879,0.0015333364,0.03185498,0.035147082,0.05347754,0.15432143,0.66443837],"study_design_scores_gemma":[0.00021982838,0.00073880923,0.056936916,0.00057738106,0.0001206556,0.0021362437,0.0015498627,0.25052214,0.10911573,0.03281297,0.54476875,0.0005006256],"about_ca_topic_score_codex":0.02341258,"about_ca_topic_score_gemma":0.0119255185,"teacher_disagreement_score":0.02341258,"about_ca_system_score_codex":0.0028414666,"about_ca_system_score_gemma":0.0037614717,"threshold_uncertainty_score":0.0465526},"labels":[],"label_agreement":null},{"id":"W2334290708","doi":"10.2166/ws.2010.571","title":"Planning renewal of water mains while considering deterioration, economies of scale and adjacent infrastructure","year":2010,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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":"National Research Council Canada","funders":"American Water Works Association Research Foundation","keywords":"Mains electricity; Breakage; Schedule; Economies of scale; Scale (ratio); Environmental science; Electricity; Water supply; Civil engineering; Engineering; Computer science; Economics; Environmental engineering; Geography; Microeconomics","score_opus":0.0041285030369490015,"score_gpt":0.17943727059123493,"score_spread":0.17530876755428593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334290708","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":[0.5082506,0.0002546783,0.4823542,0.00038769344,0.000025801872,0.0003162902,0.00033371418,0.0001984302,0.007878592],"genre_scores_gemma":[0.98038495,0.00014228089,0.01616295,0.000011898964,0.00000947023,0.00010098842,0.000096235715,0.000024075362,0.0030671787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951625,0.00014990839,0.000015880687,0.000113062946,0.00006900677,0.00013591899],"domain_scores_gemma":[0.99935716,0.00029344176,0.00012657512,0.000035136778,0.000054999004,0.00013275172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007280456,0.0005748078,0.00084257167,0.0007536242,0.0005591396,0.00090251246,0.001228542,0.0011314733,0.0023178305],"category_scores_gemma":[0.0017943017,0.00080467307,0.0007054755,0.00084086135,0.0008061915,0.001388805,0.00088963023,0.0007451373,0.0002329894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026685406,0.000024988967,0.00086227315,0.00001715733,0.000010258386,0.00008237097,0.000025967964,0.9907924,0.00097169436,0.0022337227,0.00011581844,0.0048366305],"study_design_scores_gemma":[0.000008762839,0.000078089746,0.00091766,0.000003855004,0.000010874269,0.000026380298,0.00009978186,0.9946465,0.0004902436,0.0033214628,0.00038869068,0.000007817107],"about_ca_topic_score_codex":0.014059192,"about_ca_topic_score_gemma":0.023589745,"teacher_disagreement_score":0.014059192,"about_ca_system_score_codex":0.0017210828,"about_ca_system_score_gemma":0.001827561,"threshold_uncertainty_score":0.027954698},"labels":[],"label_agreement":null},{"id":"W2334291935","doi":"10.4043/27026-ms","title":"Pipeline Leak and Impact Detection System - PipeLIDS - Monitoring Product Dedicated to Onshore Pipelines","year":2016,"lang":"en","type":"article","venue":"Offshore Technology Conference","topic":"Water Systems and 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":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Pipeline transport; Noise (video); Pipeline (software); Beacon; Global Positioning System; Leak; Engineering; Leak detection; Real-time computing; Computer science; Marine engineering; Telecommunications; Mechanical engineering","score_opus":0.011355501318024827,"score_gpt":0.22345260436776362,"score_spread":0.2120971030497388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334291935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43878198,0.0012023428,0.44395763,0.00054493727,0.00046412082,0.0010641728,0.007821806,0.085857615,0.020305289],"genre_scores_gemma":[0.889856,0.00022642079,0.078172006,0.00021282095,0.00008253038,0.00035358316,0.00491065,0.00040627102,0.025779754],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947506,0.000058814174,0.000042577652,0.00017413421,0.00021162677,0.000037846316],"domain_scores_gemma":[0.9994367,0.000086261876,0.00008721689,0.000090082045,0.00023832529,0.000061514926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037834418,0.0008207983,0.0006276446,0.0012981954,0.00031099867,0.00041045493,0.0007658781,0.00042099218,0.0077233273],"category_scores_gemma":[0.0007573048,0.00037246454,0.00022885686,0.0004060779,0.00020981785,0.0006294544,0.0006832598,0.0003052261,0.002506121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023284273,0.00037551828,0.05256413,0.001195758,0.00011180362,0.0009849793,0.0009896494,0.012473917,0.3834707,0.0014870014,0.04345967,0.5005584],"study_design_scores_gemma":[0.00043184153,0.0029564733,0.13375479,0.0002546823,0.00034299793,0.002461007,0.0004941696,0.2444097,0.45986378,0.0011239027,0.15363759,0.0002690756],"about_ca_topic_score_codex":0.002041587,"about_ca_topic_score_gemma":0.0020067338,"teacher_disagreement_score":0.0077233273,"about_ca_system_score_codex":0.0005985695,"about_ca_system_score_gemma":0.0006900688,"threshold_uncertainty_score":0.025837123},"labels":[],"label_agreement":null},{"id":"W2334750332","doi":"10.1115/imece2002-33497","title":"The Onset of Gas Entrainment From a Stratified Two-Phase Region Through a Single Side-Oriented Branch","year":2002,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Froude number; Mechanics; Sink (geography); Entrainment (biomusicology); Critical point (mathematics); Physics; Mathematics; Geometry; Meteorology; Flow (mathematics)","score_opus":0.022602578506266503,"score_gpt":0.2172784436623227,"score_spread":0.1946758651560562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334750332","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":[0.98567826,0.000066229266,0.013491665,0.000013849259,0.0000029073065,0.000009933732,0.000031027274,0.00006577544,0.0006402226],"genre_scores_gemma":[0.9986926,0.00003280267,0.0010992335,0.000001445866,7.9929373e-7,0.000004501062,0.000022745897,0.0000038510784,0.00014202786],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99997044,0.0000019290853,9.465406e-7,0.000006455797,0.000011024577,0.0000093080935],"domain_scores_gemma":[0.999912,0.000038666603,0.000012721487,0.000008279632,0.000017737539,0.000010550276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089636334,0.000218111,0.00026318763,0.0001655735,0.00019802403,0.00017082847,0.00027690377,0.00018611766,0.00056727],"category_scores_gemma":[0.00031262,0.0001305906,0.00021785806,0.00009125212,0.00028233565,0.0003088912,0.00018801726,0.00025858096,0.0000717256],"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":[0.00043739995,0.00007757759,0.011850812,0.00017336044,0.00001777713,0.0005716543,0.00022647284,0.16738172,0.80213803,0.0028562034,0.0001700372,0.014098944],"study_design_scores_gemma":[0.00001374799,0.0002844091,0.008017558,0.0000067112856,0.000008171612,0.00011326527,0.000058882364,0.76720965,0.2233543,0.00059649703,0.0003232609,0.000013583701],"about_ca_topic_score_codex":0.0013161051,"about_ca_topic_score_gemma":0.0009939764,"teacher_disagreement_score":0.0013161051,"about_ca_system_score_codex":0.00034640435,"about_ca_system_score_gemma":0.00030225617,"threshold_uncertainty_score":0.002616942},"labels":[],"label_agreement":null},{"id":"W2336160287","doi":"10.14288/1.0073366","title":"Water distribution system failures : an integrated framework for prognostic and diagnostic analyses","year":2012,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Distribution (mathematics); Computer science; Environmental science; Mathematics","score_opus":0.011562059267895554,"score_gpt":0.1894474974900707,"score_spread":0.17788543822217515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336160287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019845534,0.0008293989,0.9918469,0.0007571266,0.000051911666,0.000098378274,0.0001414533,0.0004039495,0.0038862396],"genre_scores_gemma":[0.3080341,0.003867726,0.6816352,0.00029840562,0.0004257429,0.0006099812,0.00071612734,0.00015845498,0.0042543444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979668,0.000755991,0.00014028481,0.00028758013,0.00070772134,0.00014157504],"domain_scores_gemma":[0.9981731,0.0007927904,0.00020002268,0.00015329894,0.000576386,0.00010441884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036421763,0.0021018882,0.0014271167,0.0023483639,0.00081496243,0.0043448135,0.0031142726,0.002125837,0.0021011382],"category_scores_gemma":[0.0042101466,0.00069171,0.0014897166,0.0015882228,0.0017759614,0.0041120183,0.0026811424,0.002615817,0.0006481119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028887918,0.00006194215,0.000884443,0.00019806389,0.00007637063,0.00020254863,0.00021534227,0.76097065,0.00079964014,0.19880062,0.0023348427,0.035426665],"study_design_scores_gemma":[0.0000099128265,0.000028380293,0.00018356727,0.000088202956,0.000030065119,0.000037776947,0.00006674336,0.9283223,0.0002420656,0.064484924,0.0064835055,0.000022585607],"about_ca_topic_score_codex":0.015121024,"about_ca_topic_score_gemma":0.01007294,"teacher_disagreement_score":0.015121024,"about_ca_system_score_codex":0.0026933725,"about_ca_system_score_gemma":0.0033888218,"threshold_uncertainty_score":0.030066013},"labels":[],"label_agreement":null},{"id":"W2337480729","doi":"","title":"Developing Hydrographic Services and the Work of the UK Hydrographic Office","year":2015,"lang":"en","type":"article","venue":"The International Hydrographic Review","topic":"Water Systems and 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":"Canadian Hydrographic Service","funders":"","keywords":"Hydrography; Hydrographic survey; Work (physics); Oceanography; Business; Geography; Engineering; Geology","score_opus":0.01786394068347023,"score_gpt":0.2166236681815351,"score_spread":0.1987597274980649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337480729","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017924106,0.47475526,0.008810375,0.27877766,0.013040177,0.00008748379,0.002481812,0.0002494375,0.2038737],"genre_scores_gemma":[0.2880469,0.54539555,0.0073092314,0.014368839,0.002652922,0.000112186346,0.0017705266,0.00032184363,0.14002198],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997303,0.0008387003,0.00023806066,0.00021217497,0.00094144937,0.00046655998],"domain_scores_gemma":[0.99422777,0.0017724901,0.0006345655,0.00039223602,0.0022536854,0.0007192414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035697918,0.00066011964,0.00049702794,0.0015607665,0.0011354381,0.003818582,0.0006380089,0.0019861015,0.013761412],"category_scores_gemma":[0.011104421,0.00034266338,0.00034493487,0.005222134,0.0025113355,0.003600603,0.002213797,0.0023052448,0.0020425732],"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":[0.00014812262,0.0000301145,0.002080039,0.0017117015,0.00003539632,0.00021424702,0.0008754822,0.0028739786,0.00047958124,0.19132419,0.3324973,0.46772993],"study_design_scores_gemma":[0.000019176585,0.00003380386,0.005141152,0.0008701509,0.000010678508,0.000091245965,0.0005423188,0.0003390849,0.00024334627,0.007587113,0.9851029,0.00001911263],"about_ca_topic_score_codex":0.17143857,"about_ca_topic_score_gemma":0.19814575,"teacher_disagreement_score":0.17143857,"about_ca_system_score_codex":0.011263922,"about_ca_system_score_gemma":0.024348771,"threshold_uncertainty_score":0.3408814},"labels":[],"label_agreement":null},{"id":"W2338154236","doi":"10.2166/aqua.2006.075","title":"Failure risk management of buried infrastructure using fuzzy-based techniques","year":2006,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":"National Research Council Canada","funders":"American Water Works Association Research Foundation; Water Research Foundation","keywords":"Officer; Phone; Library science; Research council; Icon; Political science; Government (linguistics); Computer science; Law","score_opus":0.00856855992104943,"score_gpt":0.2396869773522642,"score_spread":0.23111841743121478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338154236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034364354,0.00032322502,0.9632593,0.000104238396,0.000015701546,0.00003579742,0.000030026154,0.00008463053,0.0017828735],"genre_scores_gemma":[0.8935853,0.0004283641,0.10473495,0.000024173794,0.000026669319,0.000073786505,0.000045095323,0.000010492314,0.0010711785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993901,0.00020143979,0.000033795302,0.00007491691,0.00024136124,0.000058335718],"domain_scores_gemma":[0.99903345,0.0005492213,0.00014325717,0.000032160133,0.00021246409,0.00002941427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014954132,0.00064084935,0.0009959495,0.0011779093,0.00046897607,0.0011625168,0.0011452159,0.00080804277,0.0011020524],"category_scores_gemma":[0.0024950064,0.00034986794,0.0010398652,0.0007514019,0.00043814376,0.0010039648,0.00065925356,0.0005336191,0.0001169296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023877736,0.000017520228,0.0002613502,0.000023983057,0.000024549974,0.000030676027,0.00003540075,0.9786195,0.00071029377,0.003309192,0.00011041406,0.016833229],"study_design_scores_gemma":[0.0000033484075,0.000011869731,0.00006595166,0.000004558209,0.0000056638255,0.0000050432254,0.0000053582257,0.997542,0.00015278679,0.0021083679,0.00009072204,0.000004303633],"about_ca_topic_score_codex":0.0122748995,"about_ca_topic_score_gemma":0.005895659,"teacher_disagreement_score":0.0122748995,"about_ca_system_score_codex":0.0013853684,"about_ca_system_score_gemma":0.00092900114,"threshold_uncertainty_score":0.02440691},"labels":[],"label_agreement":null},{"id":"W2338611824","doi":"10.14288/1.0094724","title":"Towards a national infrastructure strategy: water distribution systems : a case study","year":2009,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Water Systems and 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":"Distribution (mathematics); Business; Environmental planning; Environmental resource management; Environmental science; Mathematics","score_opus":0.006201165509365274,"score_gpt":0.1647923910694224,"score_spread":0.15859122556005711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338611824","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":[0.79632753,0.0030515506,0.0025676938,0.01735344,0.000084652354,0.00043504816,0.00040626538,0.000054757096,0.17971903],"genre_scores_gemma":[0.9734894,0.0041632103,0.0035522147,0.0010714598,0.000024039864,0.00017977995,0.0001731174,0.000013172432,0.017333604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99633884,0.0017128084,0.00008528909,0.00011838643,0.0006647641,0.0010798792],"domain_scores_gemma":[0.9980679,0.0007831275,0.00013836725,0.00006309965,0.00054224394,0.00040527276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019885472,0.00032976738,0.0002472647,0.0012842054,0.00921417,0.0047281915,0.0012948057,0.0033633949,0.0023467455],"category_scores_gemma":[0.0028617298,0.00031586905,0.00035349774,0.0047861347,0.0031540962,0.0025184685,0.0025565252,0.0019566296,0.00025677768],"study_design_candidate":"observational","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":[0.00028325207,0.0027142544,0.11771424,0.0018980219,0.00012710244,0.064141504,0.18379238,0.04751084,0.003525978,0.38840067,0.08153478,0.10835687],"study_design_scores_gemma":[0.000091856935,0.00050176075,0.05251849,0.0007761157,0.000056946672,0.003960586,0.6158391,0.0152277425,0.0012726979,0.011414695,0.2982527,0.00008729329],"about_ca_topic_score_codex":0.6743628,"about_ca_topic_score_gemma":0.8686251,"teacher_disagreement_score":0.32563722,"about_ca_system_score_codex":0.04006621,"about_ca_system_score_gemma":0.022897776,"threshold_uncertainty_score":0.6551099},"labels":[],"label_agreement":null},{"id":"W2340805681","doi":"10.1080/15732479.2016.1144620","title":"Integrated performance assessment model for water distribution networks","year":2016,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"Qatar National Research Fund","keywords":"Pipeline transport; Reliability (semiconductor); Process (computing); Report card; Network performance; Critical infrastructure; Reliability engineering; Network information system; Computer science; Engineering; Civil engineering; Network architecture; Computer network; Computer security; Environmental engineering","score_opus":0.0026873556584196185,"score_gpt":0.1642204801259067,"score_spread":0.16153312446748708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340805681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04998221,0.0003462645,0.92991203,0.00029940298,0.000037651607,0.00025234485,0.00059614045,0.00041101399,0.018162834],"genre_scores_gemma":[0.91433185,0.0005845133,0.06713775,0.000059984777,0.000029004006,0.00072345836,0.0006810062,0.00006459673,0.01638786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897814,0.00032070195,0.000038046164,0.00021031064,0.00026920185,0.00018364117],"domain_scores_gemma":[0.9988165,0.00058192917,0.00014847444,0.00003312047,0.00037511144,0.000044994897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020661366,0.0015710233,0.0010081908,0.0015861163,0.00071277097,0.0017254567,0.0020484505,0.0018397481,0.00584675],"category_scores_gemma":[0.0034416264,0.0006467437,0.0013712595,0.0012740672,0.0007020085,0.0016030984,0.0012554922,0.0015337411,0.00081500167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008172402,0.000009446255,0.00014295414,0.000008606853,0.000006386842,0.000015783515,0.000015524816,0.9961838,0.000111595575,0.0017485501,0.00007721726,0.0016718286],"study_design_scores_gemma":[0.0000020414566,0.00000789237,0.000061094775,0.0000022517013,0.000003539509,0.0000032674193,0.000005669464,0.9987494,0.00004842489,0.00096878887,0.00014554142,0.0000020400944],"about_ca_topic_score_codex":0.030390128,"about_ca_topic_score_gemma":0.015841398,"teacher_disagreement_score":0.030390128,"about_ca_system_score_codex":0.002906662,"about_ca_system_score_gemma":0.0019189187,"threshold_uncertainty_score":0.060426533},"labels":[],"label_agreement":null},{"id":"W2340919198","doi":"10.7202/1035713ar","title":"Validation and Comparison of Different Statistical Models for the Prediction of Water Main Pipe Breaks in a Municipal Network in Québec, Canada","year":2016,"lang":"en","type":"article","venue":"Revue des sciences de l eau","topic":"Water Systems and 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":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weibull distribution; Exponential function; Maximum likelihood; Calibration; Statistics; Generalized least squares; Mathematics; Least-squares function approximation; Econometrics; Mathematical analysis","score_opus":0.04824742296378967,"score_gpt":0.25123486821751423,"score_spread":0.20298744525372456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340919198","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":[0.9577399,0.00033191865,0.034371555,0.00030804673,0.000021213264,0.00016408693,0.003517533,0.00073869864,0.0028069932],"genre_scores_gemma":[0.98527896,0.00014402937,0.01014986,0.000033044147,0.000003288023,0.00007930041,0.002820429,0.000047047062,0.0014440685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998872,0.00038832022,0.0000588424,0.00027828856,0.00025551833,0.000147033],"domain_scores_gemma":[0.99424404,0.0027209087,0.0004843148,0.0003060183,0.002044831,0.00019981684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003886573,0.00089846906,0.00046418005,0.0015929829,0.0007689745,0.0013808025,0.0021515193,0.0006262649,0.0013276245],"category_scores_gemma":[0.010005684,0.0003908033,0.00076940295,0.0015051636,0.00043787985,0.0006877037,0.00051254046,0.000622299,0.0002880723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014353739,0.00011659706,0.13461055,0.000065881926,0.00015581297,0.000082788734,0.00019695362,0.8395963,0.00078101683,0.0008052267,0.0012481959,0.022197185],"study_design_scores_gemma":[0.0000122736765,0.000038931485,0.041848935,0.000022288581,0.000014143617,0.000012774804,0.00017238276,0.9566991,0.00030769611,0.0001677204,0.00068432366,0.0000194022],"about_ca_topic_score_codex":0.94412065,"about_ca_topic_score_gemma":0.9208411,"teacher_disagreement_score":0.055879354,"about_ca_system_score_codex":0.013013758,"about_ca_system_score_gemma":0.009698794,"threshold_uncertainty_score":0.112416804},"labels":[],"label_agreement":null},{"id":"W2350822606","doi":"","title":"Discussion on Calculation of Water Supply Design Flow Design Load for Residential Quarter","year":2003,"lang":"en","type":"article","venue":"Journal Os Southern Institute of Metallurgy","topic":"Water Systems and 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":"Quarter (Canadian coin); Flow (mathematics); Water consumption; Environmental science; Consumption (sociology); Water supply; Engineering; Environmental engineering; Mathematics; Geography","score_opus":0.020651871443964347,"score_gpt":0.21244600944936923,"score_spread":0.19179413800540487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2350822606","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":[0.09736213,0.0007020409,0.8471806,0.0016574812,0.00032162227,0.000119791956,0.00039876517,0.0009808916,0.051276755],"genre_scores_gemma":[0.8262111,0.0011275021,0.13608208,0.0002981506,0.00022302548,0.00020712626,0.0005097602,0.00041555488,0.034925785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994642,0.0001289668,0.000030404875,0.00010245482,0.00023696365,0.000037034843],"domain_scores_gemma":[0.99969554,0.00009558894,0.000019862015,0.000029469942,0.00014936227,0.0000102034965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005948863,0.0003717076,0.0003063228,0.00071685587,0.0008748769,0.0008329025,0.0006833379,0.00044825408,0.0050979364],"category_scores_gemma":[0.001608167,0.00024282017,0.0006456345,0.0005597968,0.0003987793,0.0013956918,0.00048076923,0.0004920067,0.00059874327],"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":[0.0002669747,0.000085285814,0.011074538,0.0006764732,0.000042057552,0.0004898026,0.0012872476,0.382642,0.02351904,0.17629083,0.023836702,0.379789],"study_design_scores_gemma":[0.0000651157,0.00020083287,0.009142129,0.00011382706,0.00006161198,0.0004919395,0.00093307527,0.7404871,0.024627814,0.08551871,0.13826615,0.0000916617],"about_ca_topic_score_codex":0.007117222,"about_ca_topic_score_gemma":0.004791273,"teacher_disagreement_score":0.007117222,"about_ca_system_score_codex":0.0010533936,"about_ca_system_score_gemma":0.00093404687,"threshold_uncertainty_score":0.01705426},"labels":[],"label_agreement":null},{"id":"W236431183","doi":"10.1007/s10040-015-1257-y","title":"Semi-automated filtering of data outliers to improve spatial analysis of piezometric data","year":2015,"lang":"en","type":"article","venue":"Hydrogeology Journal","topic":"Water Systems and 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":"SNC-Lavalin (Canada); Université Laval","funders":"","keywords":"Outlier; Computer science; Data mining; Interpolation (computer graphics); Context (archaeology); Kriging; Reliability (semiconductor); Spatial analysis; Geostatistics; Statistics; Remote sensing; Artificial intelligence; Geology; Mathematics; Spatial variability; Machine learning","score_opus":0.05744813064470562,"score_gpt":0.28361597626740676,"score_spread":0.22616784562270115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W236431183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035518117,0.00016666194,0.96156406,0.00007427938,0.00007483284,0.000034682784,0.00023964989,0.0019771645,0.00035048538],"genre_scores_gemma":[0.43099138,0.00023262913,0.56412864,0.00006282179,0.000111975714,0.00011899513,0.0021011222,0.0004710426,0.0017814223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815637,0.00033906882,0.00021829655,0.00037901945,0.00070955424,0.00019761024],"domain_scores_gemma":[0.99521303,0.0014919945,0.0005527431,0.0008859921,0.0017565135,0.00009965719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020154947,0.001029621,0.0014593322,0.002556046,0.00073097687,0.001748564,0.0012292408,0.000841329,0.0014415344],"category_scores_gemma":[0.008014355,0.0005090907,0.0011254171,0.0026131421,0.00054803636,0.0010676765,0.0014990346,0.0011889243,0.0010506931],"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":[0.00083258504,0.00033638367,0.011438174,0.00040692798,0.00027616596,0.00040136796,0.0006233368,0.112648904,0.068187304,0.004249477,0.006899121,0.7937002],"study_design_scores_gemma":[0.00002348853,0.000084580446,0.009236506,0.000021972994,0.000053018663,0.00017154703,0.0001344385,0.960263,0.023419062,0.00245548,0.0040996773,0.000037207996],"about_ca_topic_score_codex":0.005755115,"about_ca_topic_score_gemma":0.007602404,"teacher_disagreement_score":0.005755115,"about_ca_system_score_codex":0.00034494544,"about_ca_system_score_gemma":0.0017263843,"threshold_uncertainty_score":0.011443257},"labels":[],"label_agreement":null},{"id":"W2367355799","doi":"","title":"Discussion on Flow Calculation of Water Distribution Networkfor Residential Quarter","year":2002,"lang":"en","type":"article","venue":"Journal of East China Jiaotong University","topic":"Water Systems and 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":"Quarter (Canadian coin); Flow (mathematics); Environmental science; Mathematics; Statistics; Engineering; Geography; Geometry; Archaeology","score_opus":0.005870161083662818,"score_gpt":0.15587082945428163,"score_spread":0.15000066837061882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2367355799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07376665,0.0010626671,0.901942,0.0023283446,0.00029907475,0.00006103402,0.00031173532,0.00048136606,0.019747192],"genre_scores_gemma":[0.83324176,0.0032620113,0.1398704,0.00029267833,0.0003532232,0.000110827066,0.0005022649,0.00019028033,0.02217652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999689,0.000082300176,0.000019870939,0.00006408328,0.00011444308,0.000030368617],"domain_scores_gemma":[0.99981743,0.000060078946,0.000014775685,0.000017021312,0.0000821963,0.000008499313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004930525,0.0002615816,0.0003068583,0.00063155155,0.00082800695,0.00079334725,0.00063021854,0.00044959004,0.0030084578],"category_scores_gemma":[0.0013609488,0.00016484871,0.0006383513,0.00075467955,0.00036610322,0.0019780847,0.00038240288,0.00042401528,0.00019777835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008109928,0.000046915095,0.006607841,0.00029131246,0.00002951204,0.0003922863,0.0005007179,0.5434851,0.0064484053,0.22179073,0.01218992,0.2081362],"study_design_scores_gemma":[0.000015561936,0.0000360385,0.0027126577,0.000036125177,0.000020317022,0.00019647353,0.00024614038,0.8905362,0.0038340252,0.07327144,0.029068148,0.000026806578],"about_ca_topic_score_codex":0.015665479,"about_ca_topic_score_gemma":0.0076293615,"teacher_disagreement_score":0.015665479,"about_ca_system_score_codex":0.00097343064,"about_ca_system_score_gemma":0.00095404923,"threshold_uncertainty_score":0.031148553},"labels":[],"label_agreement":null},{"id":"W2375105804","doi":"","title":"Oxygen transport and diffusion numerical simulation on water and air mixture of plug discharge","year":2013,"lang":"en","type":"article","venue":"Advances in Water Science","topic":"Water Systems and 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":"Oxygen; Diffusion; Spark plug; Volume (thermodynamics); Chemistry; Saturation (graph theory); Limiting oxygen concentration; Inlet; Plug flow; Volume fraction; Reynolds number; Analytical Chemistry (journal); Environmental chemistry; Thermodynamics; Turbulence; Geology","score_opus":0.003334858427651465,"score_gpt":0.1995150293680336,"score_spread":0.19618017094038212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2375105804","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":[0.9591529,0.0002461616,0.034271464,0.00023860505,0.000050501105,0.000045834106,0.00027719536,0.00016991589,0.0055474294],"genre_scores_gemma":[0.9907742,0.00013745515,0.007085782,0.00001833286,0.000005138115,0.000054362466,0.00012941072,0.000011614265,0.0017836296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998722,0.000019137857,0.000008058857,0.000024593894,0.000042951495,0.00003303639],"domain_scores_gemma":[0.99975353,0.00011866161,0.000030271827,0.000012818533,0.000057694437,0.000027005171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021993011,0.0004338959,0.00064063625,0.00031373187,0.0005483467,0.00047892003,0.000539338,0.0009898796,0.0007037699],"category_scores_gemma":[0.0006687208,0.00024043578,0.00055317156,0.0004675633,0.0004906822,0.00046973393,0.00052229624,0.00048926566,0.00006739112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016522793,0.00008474834,0.0033907583,0.00012036464,0.000013623,0.0002649327,0.0001302869,0.9634051,0.025866898,0.0029291345,0.00020245135,0.0034265814],"study_design_scores_gemma":[0.000017281242,0.00002729406,0.00032881455,0.0000018159554,0.000002473808,0.000010471921,0.000022296228,0.9969663,0.0023160113,0.00015040637,0.00015195947,0.000004943138],"about_ca_topic_score_codex":0.015724573,"about_ca_topic_score_gemma":0.0045524584,"teacher_disagreement_score":0.015724573,"about_ca_system_score_codex":0.0006805651,"about_ca_system_score_gemma":0.0006778054,"threshold_uncertainty_score":0.031266093},"labels":[],"label_agreement":null},{"id":"W2378169229","doi":"","title":"Achievements and Analysis of Pump Model Unit Test for the Pump Station in the East Line of the Project of South-to-North Water Transfers","year":2006,"lang":"en","type":"article","venue":"Drainage and Irrigation Machinery","topic":"Water Systems and 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":"CAE (Canada)","funders":"","keywords":"Engineering; Reliability (semiconductor); Head (geology); Unit testing; Line (geometry); Unit (ring theory); Mechanical engineering; Marine engineering; Computer science; Mathematics; Geology; Physics; Thermodynamics","score_opus":0.015446206381359743,"score_gpt":0.21851849579899216,"score_spread":0.20307228941763242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2378169229","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":[0.96851057,0.00007121645,0.029646518,0.000033159715,0.000022611235,0.0000307852,0.00034938735,0.00042546602,0.0009101971],"genre_scores_gemma":[0.9947679,0.000026116159,0.0043572085,0.0000052189343,0.0000032852843,0.000024603001,0.00033552173,0.000045645706,0.0004343525],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878556,0.0002837527,0.000088404646,0.0001800588,0.0005342665,0.00012803468],"domain_scores_gemma":[0.9975399,0.0007038355,0.00020248986,0.0004313919,0.0009895809,0.00013274165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014085444,0.00055352837,0.0006378137,0.001104826,0.0005145111,0.00046126827,0.0008374075,0.0005582125,0.0014531659],"category_scores_gemma":[0.0031526107,0.00027458317,0.0006379371,0.0009059116,0.00035161723,0.00080627186,0.0004734405,0.00038697483,0.00034344944],"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":[0.0034878936,0.0008763598,0.2560248,0.0005881054,0.00037001536,0.0007282134,0.0016025105,0.100167304,0.40777868,0.0014350133,0.0031482333,0.2237929],"study_design_scores_gemma":[0.00014014132,0.0052084485,0.28255334,0.000023577079,0.00041261534,0.0005399589,0.0010310265,0.28798324,0.41759726,0.00062638894,0.0037213059,0.000162647],"about_ca_topic_score_codex":0.0026157184,"about_ca_topic_score_gemma":0.003622441,"teacher_disagreement_score":0.0026157184,"about_ca_system_score_codex":0.00047877416,"about_ca_system_score_gemma":0.00048734792,"threshold_uncertainty_score":0.0074492097},"labels":[],"label_agreement":null},{"id":"W2388833621","doi":"","title":"Calculation Method of Design Flow for Water Supply Pipelinein Residential Quarter","year":2011,"lang":"en","type":"article","venue":"Journal of Shenyang Jianzhu University","topic":"Water Systems and 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":"Pipeline (software); Water supply; Flow (mathematics); Quarter (Canadian coin); Pipeline transport; Limit (mathematics); Environmental science; Engineering; Environmental engineering; Mathematics; Mechanical engineering","score_opus":0.020895239999575686,"score_gpt":0.19793639439322705,"score_spread":0.17704115439365137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2388833621","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":[0.012167832,0.00013005298,0.9807513,0.000078576,0.000029370276,0.00006849241,0.000080156875,0.00029830355,0.0063958312],"genre_scores_gemma":[0.47465476,0.0005912362,0.51484007,0.00005774199,0.000036891204,0.00041617395,0.00043364972,0.00028658178,0.00868289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992017,0.00014214728,0.000046634817,0.00014400252,0.00040133944,0.000064173844],"domain_scores_gemma":[0.99951506,0.00009863291,0.000049442166,0.000039811148,0.00028268844,0.000014363471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009088451,0.0006666108,0.0005065722,0.0015084235,0.00087758614,0.0007963785,0.00080204004,0.0005412963,0.0057156603],"category_scores_gemma":[0.0022108266,0.0003667848,0.0006401754,0.000943208,0.00042743297,0.0014371058,0.00053583644,0.0005027218,0.00059350673],"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":[0.00010871635,0.00007189797,0.005444513,0.00072290946,0.00002695845,0.0001415162,0.00047111954,0.554892,0.017989269,0.056468807,0.0062241214,0.3574381],"study_design_scores_gemma":[0.00003839558,0.00010826775,0.0017001441,0.00004845974,0.00002874668,0.00015150943,0.00012734815,0.95116496,0.0127949035,0.017019205,0.016785493,0.000032456388],"about_ca_topic_score_codex":0.00822693,"about_ca_topic_score_gemma":0.0056099114,"teacher_disagreement_score":0.00822693,"about_ca_system_score_codex":0.0014758931,"about_ca_system_score_gemma":0.0018926637,"threshold_uncertainty_score":0.019120812},"labels":[],"label_agreement":null},{"id":"W2389260339","doi":"","title":"The discussion about problems for water supply and sewerage at residential quarter","year":2002,"lang":"en","type":"article","venue":"Shanxi Agchitecture","topic":"Water Systems and 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":"Sewerage; Quarter (Canadian coin); Water supply; Engineering; Process (computing); Environmental science; Water resource management; Environmental engineering; Environmental planning; Civil engineering; Computer science; Geography; Archaeology","score_opus":0.004090652775007106,"score_gpt":0.160076155832587,"score_spread":0.15598550305757988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2389260339","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0906083,0.025569454,0.5611135,0.08193673,0.003647281,0.00020406272,0.00059304934,0.00034384604,0.2359838],"genre_scores_gemma":[0.6646333,0.03437927,0.10871663,0.009407253,0.0043772915,0.00046253676,0.00047430745,0.00035893152,0.17719047],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99947983,0.00024028322,0.0000214079,0.0000954347,0.00010286989,0.000060198407],"domain_scores_gemma":[0.9996753,0.00018892289,0.00004198156,0.000029797817,0.00004360681,0.000020413792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005234513,0.0004769896,0.0005706341,0.0004634709,0.0019098759,0.0015903874,0.0011589812,0.003058324,0.012656872],"category_scores_gemma":[0.0014076962,0.00027532192,0.0016169061,0.0012653816,0.0014409848,0.0027542727,0.0007956677,0.0022470271,0.0009848775],"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":[0.000062769395,0.00007627152,0.0012358349,0.00085594034,0.00003211725,0.0010091267,0.00076192606,0.053637855,0.0019118721,0.85725284,0.029872712,0.053290848],"study_design_scores_gemma":[0.000019013787,0.00015845239,0.0026603057,0.0002472065,0.000028922721,0.0015686129,0.0014734083,0.046810552,0.0019016182,0.73792636,0.20715465,0.00005083775],"about_ca_topic_score_codex":0.0034489513,"about_ca_topic_score_gemma":0.0025452746,"teacher_disagreement_score":0.012656872,"about_ca_system_score_codex":0.0012359087,"about_ca_system_score_gemma":0.0007827074,"threshold_uncertainty_score":0.04234141},"labels":[],"label_agreement":null},{"id":"W2389716254","doi":"","title":"Discussion on Economic Running of Water Supply in Residential Quarter","year":2006,"lang":"en","type":"article","venue":"Journal of Shenyang University","topic":"Water Systems and 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":"Quarter (Canadian coin); Water supply; Energy (signal processing); Water saving; Water resource management; Environmental economics; Engineering; Architectural engineering; Natural resource economics; Environmental science; Civil engineering; Business; Economics; Environmental engineering; Geography; Mathematics","score_opus":0.002506831394281913,"score_gpt":0.14546950690621305,"score_spread":0.14296267551193115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2389716254","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":[0.14513648,0.014905165,0.1167533,0.079239994,0.0021571089,0.00017113874,0.0005939275,0.000109914174,0.6409331],"genre_scores_gemma":[0.9111691,0.009953561,0.011352092,0.0022865972,0.0011928353,0.00012428396,0.0001108005,0.000054618828,0.06375609],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995378,0.0002057418,0.000020779284,0.000050549766,0.00011986562,0.0000653296],"domain_scores_gemma":[0.99953973,0.00026145225,0.000033957065,0.00002537757,0.00011187161,0.000027591419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085197063,0.00023348488,0.00026461677,0.000771791,0.0014325045,0.0023506517,0.00086992426,0.0012127056,0.008901005],"category_scores_gemma":[0.0017271742,0.00019030152,0.0008270398,0.0011384441,0.0013463458,0.002811543,0.0005566523,0.0013940306,0.00036763476],"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":[0.000042309963,0.000021200978,0.00091385597,0.00011726234,0.000012071339,0.00020606557,0.00015202338,0.011691517,0.0006081242,0.9678533,0.004205526,0.014176686],"study_design_scores_gemma":[0.000020522464,0.00018573055,0.008328741,0.00030921574,0.00004921407,0.00035715487,0.0019641644,0.037803747,0.0023123913,0.6886735,0.25994396,0.00005170612],"about_ca_topic_score_codex":0.004705122,"about_ca_topic_score_gemma":0.007573309,"teacher_disagreement_score":0.008901005,"about_ca_system_score_codex":0.0032542772,"about_ca_system_score_gemma":0.0011974222,"threshold_uncertainty_score":0.029776812},"labels":[],"label_agreement":null},{"id":"W2390653343","doi":"","title":"The Energy Saving Design of Domestic Water Supply for a Residential Quarter of High-rise Buildings","year":2008,"lang":"en","type":"article","venue":"Journal of Chongqing Jianzhu University","topic":"Water Systems and 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":"Water supply; Roof; Quarter (Canadian coin); Investment (military); Environmental science; Environmental engineering; Variable (mathematics); Engineering; Civil engineering; Mathematics","score_opus":0.006530971642076946,"score_gpt":0.1646983482992943,"score_spread":0.15816737665721736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2390653343","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":[0.5099007,0.0003470725,0.47244036,0.00029110795,0.000040206298,0.00014971427,0.00019690552,0.00040000063,0.016233921],"genre_scores_gemma":[0.9757033,0.00011120044,0.020573122,0.000012519359,0.0000056114695,0.000044217886,0.000059048874,0.000016843394,0.003474116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998853,0.000033367618,0.0000056865965,0.000024971512,0.000033237844,0.000017333294],"domain_scores_gemma":[0.99996173,0.0000067118453,0.0000068982995,0.000004088039,0.000016555865,0.000003996563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011688468,0.00020388096,0.00031278338,0.00020536885,0.00037218063,0.00041316915,0.00042450606,0.0002898277,0.0027187658],"category_scores_gemma":[0.00017872131,0.00019506071,0.00024865055,0.00033277873,0.00016797389,0.00038723167,0.00023851942,0.00013956393,0.00019641698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045975973,0.00017977213,0.0052493024,0.00039889707,0.000071067734,0.0002707353,0.0002271135,0.68577224,0.13957171,0.01425582,0.0028263642,0.15071727],"study_design_scores_gemma":[0.00003592951,0.0004100347,0.004185941,0.00000928235,0.00005812679,0.000103846374,0.00015791522,0.96729785,0.019640116,0.0021535824,0.0059296535,0.0000177002],"about_ca_topic_score_codex":0.0029482713,"about_ca_topic_score_gemma":0.004733821,"teacher_disagreement_score":0.0029482713,"about_ca_system_score_codex":0.0004985347,"about_ca_system_score_gemma":0.00045204902,"threshold_uncertainty_score":0.009095192},"labels":[],"label_agreement":null},{"id":"W2392708224","doi":"","title":"The discussion of municipal water supply channel' design flow in residential quarter","year":2008,"lang":"en","type":"article","venue":"Shanxi Architecture","topic":"Water Systems and 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":"Quarter (Canadian coin); Paragraph; Water supply; Flow (mathematics); Channel (broadcasting); Environmental science; Engineering; Environmental engineering; Computer science; Mathematics; Geography; Telecommunications","score_opus":0.008562095201440656,"score_gpt":0.18241888574054543,"score_spread":0.17385679053910477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2392708224","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":[0.26271507,0.00062525534,0.6124968,0.0039407364,0.00022604897,0.00021543265,0.0007369933,0.00070381217,0.11833989],"genre_scores_gemma":[0.90995395,0.0004584301,0.0455545,0.00039347415,0.000041805033,0.000095945245,0.00024827893,0.000094533454,0.043159205],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995241,0.00015693108,0.000017477125,0.00008125995,0.00013128649,0.000088842964],"domain_scores_gemma":[0.9997714,0.00007104153,0.000022130467,0.000022349946,0.00009684258,0.000016141214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005868834,0.00019780757,0.00020495706,0.00037385427,0.00081904494,0.001141247,0.00044240992,0.0005697145,0.0066186585],"category_scores_gemma":[0.0010536681,0.00021508227,0.00041795854,0.00062011194,0.00054565177,0.0010888448,0.00033171318,0.000538111,0.00046095726],"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":[0.0002445984,0.00011502465,0.013218774,0.000374623,0.000022079088,0.00060925353,0.0020667133,0.28290817,0.02051543,0.5004453,0.022876771,0.15660337],"study_design_scores_gemma":[0.00004234288,0.00029096325,0.015780283,0.00010198539,0.000038288315,0.0006289489,0.002907981,0.53577805,0.027335988,0.20110956,0.21588604,0.000099558885],"about_ca_topic_score_codex":0.013335031,"about_ca_topic_score_gemma":0.018140966,"teacher_disagreement_score":0.013335031,"about_ca_system_score_codex":0.0023147033,"about_ca_system_score_gemma":0.0020746635,"threshold_uncertainty_score":0.026514828},"labels":[],"label_agreement":null},{"id":"W2395315493","doi":"10.14796/jwmm.r208-07","title":"SWMM Calibration using Genetic Algorithms","year":2002,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Storm Water Management Model; Hydraulics; Genetic algorithm; Storm; Calibration; Computer science; Algorithm; Hydrology (agriculture); Environmental science; Mathematical optimization; Engineering; Surface runoff; Meteorology; Mathematics; Machine learning; Geotechnical engineering; Geography; Stormwater; Ecology; Aerospace engineering; Statistics; Biology","score_opus":0.027957378683837276,"score_gpt":0.1964165097670888,"score_spread":0.16845913108325153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395315493","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":[0.10529529,0.00022876733,0.8741665,0.00038922782,0.0001606869,0.00014410037,0.0010668993,0.0061779995,0.012370602],"genre_scores_gemma":[0.6127324,0.00014214437,0.38078144,0.00015966686,0.000026203672,0.00031118537,0.0020826035,0.00093452795,0.0028298239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994276,0.00019619038,0.000030974104,0.000147925,0.00014196291,0.000055298788],"domain_scores_gemma":[0.9984143,0.00049634755,0.00013292466,0.00026904684,0.0006452086,0.00004213564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017055057,0.00089580467,0.00055716775,0.0013027262,0.0006072767,0.0010580372,0.0011989503,0.0014806653,0.0044473577],"category_scores_gemma":[0.0073499163,0.00060920627,0.00063277944,0.0016547297,0.0003137277,0.001075759,0.0009535544,0.0011567897,0.0016507753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003635218,0.000028404813,0.001013431,0.000027467842,0.000031844786,0.000025263282,0.000028936638,0.95931387,0.0011112273,0.0014509102,0.0014525369,0.035479654],"study_design_scores_gemma":[0.000015890479,0.000009864543,0.00037914413,0.000010469173,0.0000057792067,0.0000069538432,0.000008907742,0.9954442,0.0011879726,0.00170406,0.0012157125,0.000010975012],"about_ca_topic_score_codex":0.00987487,"about_ca_topic_score_gemma":0.0070031723,"teacher_disagreement_score":0.00987487,"about_ca_system_score_codex":0.0010633994,"about_ca_system_score_gemma":0.0015600682,"threshold_uncertainty_score":0.019634783},"labels":[],"label_agreement":null},{"id":"W2396081891","doi":"10.1061/9780784479827.157","title":"Multi-Tiered Database Schema for Integrated Municipal Asset Management","year":2016,"lang":"en","type":"article","venue":"Construction Research Congress 2016","topic":"Water Systems and 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":"Asset management; Computer science; Database schema; Database; Schema (genetic algorithms); Data management; USable; Database design; World Wide Web; Information retrieval; Business; Finance","score_opus":0.06450676854821166,"score_gpt":0.3286843624273331,"score_spread":0.26417759387912143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396081891","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":[0.007968574,0.0010236609,0.9488481,0.00079495896,0.0002950016,0.0013583675,0.019130645,0.007882665,0.012697933],"genre_scores_gemma":[0.0680405,0.0012305279,0.8727832,0.00062726554,0.000070992704,0.0011064343,0.04925872,0.0007379015,0.006144545],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967494,0.00071937253,0.0010901334,0.0005451221,0.000743018,0.00015298836],"domain_scores_gemma":[0.9966485,0.00043286567,0.0002781207,0.0010345692,0.0014134764,0.00019250474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037980531,0.0005093691,0.0006574431,0.0020749765,0.00064637576,0.005884044,0.0023123622,0.0013068066,0.006082089],"category_scores_gemma":[0.0046141995,0.0005325147,0.0011076042,0.004043956,0.0004453065,0.0031189837,0.0019626739,0.0012605487,0.0032953122],"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":[0.00047690628,0.00044019552,0.0118877,0.0016248381,0.00042931762,0.00156252,0.0021892567,0.04546899,0.027079545,0.43583524,0.15052572,0.32247978],"study_design_scores_gemma":[0.00010768372,0.000115205396,0.0028091138,0.00045535577,0.00013316555,0.001714477,0.00063249894,0.08602769,0.014452731,0.04383076,0.84959304,0.00012836166],"about_ca_topic_score_codex":0.0050139916,"about_ca_topic_score_gemma":0.0045576687,"teacher_disagreement_score":0.006082089,"about_ca_system_score_codex":0.0013232026,"about_ca_system_score_gemma":0.0030893672,"threshold_uncertainty_score":0.020346582},"labels":[],"label_agreement":null},{"id":"W2399698128","doi":"10.1061/9780784479872.020","title":"Algebraic Water Hammer: Global Formulation for Simulating Transient Pipe Network Hydraulics","year":2016,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2016","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Water hammer; Hydraulics; Reflection (computer programming); Transient (computer programming); Pipe network analysis; Pipe flow; Boundary value problem; Flow (mathematics); Hammer; Boundary (topology); Algebraic number; Method of characteristics; Computer science; Mechanics; Mathematics; Mathematical analysis; Engineering; Mechanical engineering; Physics; Differential equation","score_opus":0.005215080042927717,"score_gpt":0.16975246731017732,"score_spread":0.1645373872672496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399698128","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":[0.033679135,0.00005384248,0.95573395,0.000088015426,0.000028263294,0.000050089788,0.00020146201,0.00080314593,0.009362083],"genre_scores_gemma":[0.84258217,0.00017994978,0.14869516,0.00004506044,0.00003109972,0.00024198313,0.0002720843,0.0002543807,0.007698148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998989,0.000029797291,0.0000046834975,0.000014449954,0.000035926903,0.000016358663],"domain_scores_gemma":[0.99969697,0.00016716243,0.00002837224,0.00003325508,0.000056246994,0.000018037468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035534732,0.0005243397,0.0005785752,0.0004018381,0.0002941848,0.000692849,0.0008663198,0.00081839977,0.0034053917],"category_scores_gemma":[0.00092211424,0.0002669114,0.00034831127,0.00049420784,0.00074257614,0.0008962678,0.000824534,0.00055595534,0.0003425708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014364338,0.000008445904,0.0002625485,0.000019241195,0.0000038392104,0.00002840829,0.000020639562,0.9861688,0.0012478181,0.006556416,0.00030720216,0.0053622965],"study_design_scores_gemma":[0.0000024097296,0.0000064945652,0.000026844471,9.394344e-7,8.7690347e-7,0.000003880953,0.0000055348955,0.9984725,0.0002683706,0.0008707771,0.00033973352,0.0000016097621],"about_ca_topic_score_codex":0.0031634907,"about_ca_topic_score_gemma":0.003232655,"teacher_disagreement_score":0.0034053917,"about_ca_system_score_codex":0.00043672055,"about_ca_system_score_gemma":0.00067725556,"threshold_uncertainty_score":0.011392176},"labels":[],"label_agreement":null},{"id":"W2406683702","doi":"10.1061/(asce)hy.1943-7900.0001171","title":"CFD Approach for Column Separation in Water Pipelines","year":2016,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":45,"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":"Pipeline transport; Separation (statistics); Water column; Column (typography); Petroleum engineering; Computational fluid dynamics; Environmental science; Marine engineering; Geology; Hydrology (agriculture); Geotechnical engineering; Mechanics; Engineering; Environmental engineering; Computer science; Oceanography; Mechanical engineering; Physics","score_opus":0.0076058849106522145,"score_gpt":0.2014653771071146,"score_spread":0.19385949219646237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406683702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026882395,0.00094893767,0.9472421,0.000547571,0.00024613558,0.00016862273,0.00055633613,0.0011437099,0.022264222],"genre_scores_gemma":[0.65465826,0.001584025,0.31686813,0.0003673208,0.0002013885,0.0005727567,0.0008553188,0.0003611601,0.02453171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998803,0.00002030392,0.0000057014577,0.00002121288,0.000050620496,0.000021813921],"domain_scores_gemma":[0.99981374,0.00007714872,0.000021725378,0.0000135477385,0.000057349884,0.000016607746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015192638,0.00057789235,0.00066206773,0.0005802877,0.0007314295,0.0008243073,0.0008895454,0.0012144089,0.004633851],"category_scores_gemma":[0.0005746517,0.00037389132,0.00066021935,0.00044142036,0.0006103765,0.00056816335,0.0008478945,0.0008723946,0.0006947367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002930598,0.00003384947,0.0006369043,0.00009393675,0.000012605991,0.00021023637,0.00006725449,0.95173055,0.009356894,0.020377712,0.0014881435,0.015962627],"study_design_scores_gemma":[0.0000043609266,0.0000044791113,0.000056852507,0.0000043171754,0.0000011279739,0.000017240876,0.0000072923135,0.9964624,0.000570787,0.0013135106,0.0015528684,0.0000047979806],"about_ca_topic_score_codex":0.015586742,"about_ca_topic_score_gemma":0.0066242823,"teacher_disagreement_score":0.015586742,"about_ca_system_score_codex":0.0010471591,"about_ca_system_score_gemma":0.0018475173,"threshold_uncertainty_score":0.030992031},"labels":[],"label_agreement":null},{"id":"W2407724010","doi":"10.1061/9780784479858.021","title":"A Streamlined Approach for Estimating the Energy Efficiency of a Water Distribution System","year":2016,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2016","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Energy (signal processing); Efficient energy use; Computer science; Environmental science; Mathematical optimization; Engineering; Mathematics; Statistics","score_opus":0.004296712899143057,"score_gpt":0.1528517844174099,"score_spread":0.14855507151826686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407724010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028938647,0.00023431676,0.968538,0.000043544813,0.000015184327,0.00008607193,0.00028493823,0.00031637997,0.0015429795],"genre_scores_gemma":[0.3278453,0.0005001567,0.6689893,0.000040207717,0.000030794094,0.00027749463,0.00053201715,0.00008485479,0.0016998948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994912,0.00019285602,0.000025610612,0.00008332754,0.00018931045,0.000017643779],"domain_scores_gemma":[0.9992532,0.0003084151,0.00016058746,0.00007055872,0.00019146697,0.000015797577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005571101,0.0007419802,0.0005487886,0.0016614262,0.0003150522,0.00067461474,0.0005551737,0.00045319434,0.0018403613],"category_scores_gemma":[0.00232885,0.00043491338,0.000501823,0.0008278228,0.00023251607,0.0010873885,0.00041996184,0.0004813867,0.0003027502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004228964,0.0000434758,0.0040460243,0.00009732254,0.000059406753,0.000049485654,0.000056999153,0.88197047,0.004671235,0.005516495,0.00072475977,0.10272209],"study_design_scores_gemma":[0.0000067800115,0.00006516174,0.00081184355,0.00001293199,0.000012231787,0.00003446078,0.000008989563,0.99403787,0.0016751311,0.001536818,0.0017869828,0.000010744477],"about_ca_topic_score_codex":0.0057939338,"about_ca_topic_score_gemma":0.0064876885,"teacher_disagreement_score":0.0057939338,"about_ca_system_score_codex":0.00085059326,"about_ca_system_score_gemma":0.0009254523,"threshold_uncertainty_score":0.011520386},"labels":[],"label_agreement":null},{"id":"W2408466719","doi":"10.1061/9780784479889.030","title":"Optimal Design of Water-Supply Pipe Systems Using Economic and Technical Analysis","year":2016,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2016","topic":"Water Systems and 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 Calgary","funders":"Florida International University","keywords":"Water supply; Computer science; Optimal design; Population; Resource (disambiguation); Risk analysis (engineering); Software; Economic analysis; Water resources; Operations research; Environmental economics; Engineering; Environmental engineering; Business; Economics","score_opus":0.006758191092119319,"score_gpt":0.16666493127900903,"score_spread":0.15990674018688972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408466719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120110385,0.00036953576,0.8594207,0.0003195928,0.000038007878,0.00045031362,0.00016895523,0.00016134986,0.018961081],"genre_scores_gemma":[0.8987139,0.0004160436,0.09666069,0.000034078384,0.0000170091,0.0005710597,0.000115353774,0.000045011664,0.0034268307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990495,0.00038775156,0.000042379128,0.00011930696,0.000273719,0.00012731909],"domain_scores_gemma":[0.9990922,0.00054619135,0.000141727,0.000028394234,0.000148302,0.000043133987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021081725,0.0009981133,0.0009439254,0.0019151772,0.0008617748,0.0019823604,0.00070129265,0.0013745492,0.0028160708],"category_scores_gemma":[0.0035906194,0.0011457861,0.00091695297,0.00095347856,0.0011414933,0.0013661146,0.0010554338,0.0006350493,0.00023664655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012473829,0.000011889871,0.0002078244,0.000024224333,0.000007827922,0.000020454141,0.000011829033,0.9935057,0.00058232626,0.0029630247,0.000044502798,0.0026079216],"study_design_scores_gemma":[0.000011635199,0.0000441157,0.00020307007,0.0000072397224,0.000010918381,0.0000062179033,0.000024809286,0.9955367,0.00051787635,0.0032383418,0.00039328352,0.000005795507],"about_ca_topic_score_codex":0.0043580164,"about_ca_topic_score_gemma":0.00662166,"teacher_disagreement_score":0.0043580164,"about_ca_system_score_codex":0.0020798885,"about_ca_system_score_gemma":0.0029301778,"threshold_uncertainty_score":0.015090704},"labels":[],"label_agreement":null},{"id":"W2433887005","doi":"10.2166/hydro.2015.049","title":"An object-oriented development environment to optimally design cyclic storage systems","year":2015,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"Generalization; Development (topology); Computer science; Pareto principle; Multi-objective optimization; Set (abstract data type); Object (grammar); Mathematical optimization; Scale (ratio); Distributed computing; Mathematics; Artificial intelligence","score_opus":0.019369644332878463,"score_gpt":0.20161951524578112,"score_spread":0.18224987091290265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2433887005","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":[0.0055678813,0.00005931016,0.9890955,0.000041162246,0.000017706918,0.00009286907,0.000105346095,0.001279531,0.0037406576],"genre_scores_gemma":[0.06011507,0.00021334241,0.9364719,0.000041473682,0.00000932589,0.00050142815,0.0004227267,0.00028249857,0.001942213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996495,0.00010367582,0.000027502236,0.00003860543,0.00013919728,0.000041518546],"domain_scores_gemma":[0.9993913,0.0003376453,0.00006273037,0.000038849117,0.00014300349,0.000026464262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013286789,0.0012037032,0.000603458,0.00068106566,0.0003385365,0.0008154841,0.0011722127,0.0005652132,0.004324259],"category_scores_gemma":[0.0015303071,0.00056399003,0.0008975044,0.00064095383,0.00031341202,0.00047207077,0.0009531952,0.0008071235,0.00073866505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057579167,0.0001035852,0.00092372874,0.0002168444,0.000043470212,0.00015753922,0.000098241086,0.87938887,0.0051628533,0.019190338,0.0022086345,0.09244837],"study_design_scores_gemma":[0.000044783606,0.000057177425,0.00013714778,0.000030254345,0.000022753546,0.000043588658,0.000022878923,0.9785513,0.0030340117,0.0045557315,0.01348838,0.000011823064],"about_ca_topic_score_codex":0.0019295554,"about_ca_topic_score_gemma":0.002178135,"teacher_disagreement_score":0.004324259,"about_ca_system_score_codex":0.0004430622,"about_ca_system_score_gemma":0.0012678403,"threshold_uncertainty_score":0.014466107},"labels":[],"label_agreement":null},{"id":"W2441420450","doi":"10.1021/es5059742","title":"Nondeterministic Computational Fluid Dynamics Modeling of <i>Escherichia coli</i> Inactivation by Peracetic Acid in Municipal Wastewater Contact Tanks","year":2015,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Water Systems and 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":"Western University","funders":"","keywords":"Peracetic acid; Wastewater; Effluent; Chemistry; Sizing; Kinetics; Escherichia coli; Nondeterministic algorithm; Dilution; Environmental science; Environmental engineering; Mathematics; Biochemistry; Physics; Thermodynamics; Organic chemistry","score_opus":0.007341497753865604,"score_gpt":0.19322566761438223,"score_spread":0.18588416986051662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2441420450","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":[0.8831894,0.00017884745,0.112639494,0.00028104766,0.00002479058,0.00005820303,0.00014969465,0.0000948558,0.003383698],"genre_scores_gemma":[0.9949633,0.00006047027,0.004344198,0.000012733972,0.000003541435,0.00003863835,0.00003415039,0.0000071639392,0.0005359329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999788,0.000075504366,0.000011633825,0.000041283944,0.000039069488,0.00004448033],"domain_scores_gemma":[0.9991416,0.0005774003,0.0001316317,0.000030693514,0.00008991127,0.000028742466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048511816,0.0004689043,0.0005242247,0.00030006116,0.00043379606,0.0007844469,0.00044357116,0.0007025536,0.00035327775],"category_scores_gemma":[0.001303934,0.00036796383,0.00057494617,0.00020683877,0.00066142064,0.0004075636,0.00042274597,0.0005025455,0.000044258908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000096068725,0.0000072712805,0.00046699666,0.00000436416,0.0000023278717,0.000008103767,0.0000080244245,0.99834216,0.00050466415,0.00031122423,0.000008437254,0.0003268041],"study_design_scores_gemma":[0.0000013758905,0.0000047855283,0.000097543016,4.0949718e-7,8.29543e-7,8.3693305e-7,0.000002358013,0.99959105,0.00021041078,0.000068812806,0.000020583999,0.0000010750554],"about_ca_topic_score_codex":0.029705696,"about_ca_topic_score_gemma":0.009840197,"teacher_disagreement_score":0.029705696,"about_ca_system_score_codex":0.0011199035,"about_ca_system_score_gemma":0.00148712,"threshold_uncertainty_score":0.05906558},"labels":[],"label_agreement":null},{"id":"W2460110901","doi":"","title":"Challenges with the management of boil water advisories in British Columbia, Canada","year":2013,"lang":"en","type":"article","venue":"141st APHA Annual Meeting (November 2 - November 6, 2013)","topic":"Water Systems and 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":"Geography; Environmental resource management; Business; Environmental planning; Environmental science","score_opus":0.005144272118485965,"score_gpt":0.16296295726595136,"score_spread":0.1578186851474654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460110901","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":[0.51350766,0.011986652,0.004672856,0.21699335,0.00086254,0.0008982993,0.008427309,0.0004732447,0.24217811],"genre_scores_gemma":[0.9190556,0.006089161,0.0074099144,0.0071083778,0.00009760889,0.00012517978,0.0015042366,0.00014097417,0.058468953],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962243,0.0005010756,0.00013907746,0.00026799622,0.0013543898,0.001513097],"domain_scores_gemma":[0.9900448,0.0013021177,0.0003806502,0.00011870648,0.005747827,0.0024059792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024674993,0.00044423828,0.000512785,0.0019289587,0.008536635,0.00965168,0.0026383582,0.0019195983,0.008467649],"category_scores_gemma":[0.008967286,0.000571978,0.0003249683,0.0038867518,0.0014192394,0.0016202967,0.0018870563,0.0027088933,0.00062787737],"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":[0.0004221279,0.0006380345,0.1355431,0.0012301187,0.00035327804,0.0034033405,0.009859442,0.06349333,0.0029604228,0.044377252,0.36065578,0.3770638],"study_design_scores_gemma":[0.00016508938,0.00014857895,0.3104098,0.0016835824,0.00018716285,0.00046147098,0.07360226,0.062458884,0.0017353237,0.0108578075,0.5378014,0.0004885819],"about_ca_topic_score_codex":0.9981306,"about_ca_topic_score_gemma":0.99934214,"teacher_disagreement_score":0.11133095,"about_ca_system_score_codex":0.11133095,"about_ca_system_score_gemma":0.21962175,"threshold_uncertainty_score":0.8077663},"labels":[],"label_agreement":null},{"id":"W2460189921","doi":"10.14796/jwmm.r235-22","title":"Current Technologies For On-Line Monitoring of Drinking Water Distribution Systems","year":2009,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Current (fluid); Environmental science; Line (geometry); Engineering; Electrical engineering; Mathematics","score_opus":0.026210807680352728,"score_gpt":0.24154652203740776,"score_spread":0.21533571435705504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460189921","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":[0.0448499,0.017169027,0.833691,0.0025269564,0.00071300555,0.0010698938,0.004103674,0.00874811,0.087128505],"genre_scores_gemma":[0.4515258,0.02464442,0.45208865,0.0019199878,0.00065263023,0.0021619825,0.0048895064,0.00034859832,0.061768387],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959472,0.0005640476,0.0001153203,0.00054961076,0.0026691942,0.00015468338],"domain_scores_gemma":[0.99834824,0.00026435393,0.00030505922,0.0002807297,0.00074573525,0.00005592006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011802651,0.00090100145,0.00065286196,0.0019278143,0.0005712964,0.0018533205,0.0021554427,0.0010998348,0.009580044],"category_scores_gemma":[0.0017894623,0.00033163032,0.00036510048,0.0025818213,0.00037721478,0.0025560788,0.00090083067,0.0008371131,0.0041516162],"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":[0.00023058223,0.00022088258,0.009064476,0.0017480941,0.0000652808,0.00008368595,0.00023710982,0.0042341193,0.13090834,0.00732074,0.025628166,0.8202586],"study_design_scores_gemma":[0.000111854344,0.0008417453,0.03413753,0.0007849222,0.00030346034,0.00085636246,0.0005260941,0.06317855,0.26805162,0.014409467,0.61664426,0.00015412773],"about_ca_topic_score_codex":0.0017257319,"about_ca_topic_score_gemma":0.0020720835,"teacher_disagreement_score":0.009580044,"about_ca_system_score_codex":0.0010582866,"about_ca_system_score_gemma":0.0009777355,"threshold_uncertainty_score":0.032048464},"labels":[],"label_agreement":null},{"id":"W2460406066","doi":"10.1061/9780784479957.025","title":"An Accelerometer-Based Real-Time Monitoring and Leak Detection System for Pressurized Water Pipelines","year":2016,"lang":"en","type":"article","venue":"Pipelines 2016","topic":"Water Systems and 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":"Concordia University","funders":"Qatar National Research Fund","keywords":"Pipeline transport; Leak; Accelerometer; Real-time computing; Pipeline (software); Condition monitoring; Wireless sensor network; Computer science; SIGNAL (programming language); Leak detection; Threshold limit value; Engineering; Marine engineering; Simulation; Electrical engineering; Environmental engineering; Mechanical engineering","score_opus":0.012271747704406108,"score_gpt":0.21664243653458412,"score_spread":0.204370688830178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460406066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46320787,0.00041807635,0.52623343,0.0003333979,0.00015695284,0.0002881752,0.0006218181,0.0048839636,0.003856429],"genre_scores_gemma":[0.9731555,0.00014663005,0.023610482,0.00003761901,0.000009963228,0.00007627858,0.00015354536,0.000014468166,0.002795556],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999747,0.000032977143,0.0000148410445,0.00007061311,0.0001167549,0.000017775183],"domain_scores_gemma":[0.999833,0.000027422759,0.000035099038,0.000030184057,0.000063374995,0.000010927104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022836053,0.0004959378,0.00047710308,0.00024291537,0.00020725414,0.00031234976,0.00077080383,0.00042724638,0.0014018747],"category_scores_gemma":[0.00041376593,0.00022170009,0.00026106485,0.0002514386,0.0001493349,0.0004416971,0.00023685714,0.00026190202,0.0004483647],"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":[0.0008934884,0.0005620101,0.019393658,0.00070325844,0.00011730831,0.00056897936,0.00047490752,0.08704275,0.69525427,0.0012091146,0.0039689033,0.18981136],"study_design_scores_gemma":[0.00009321668,0.0025567887,0.023035558,0.000050233753,0.00010732858,0.0006290071,0.00012023348,0.8423531,0.12374798,0.0003605641,0.0068654534,0.00008048888],"about_ca_topic_score_codex":0.0024466503,"about_ca_topic_score_gemma":0.0036465654,"teacher_disagreement_score":0.0024466503,"about_ca_system_score_codex":0.0003425866,"about_ca_system_score_gemma":0.00043161464,"threshold_uncertainty_score":0.004864812},"labels":[],"label_agreement":null},{"id":"W2462907370","doi":"10.1061/9780784479957.064","title":"Performance Assessment Model for Water Networks","year":2016,"lang":"en","type":"article","venue":"Pipelines 2016","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Ranking (information retrieval); Computer science; Exploit; Fuzzy logic; Performance indicator; Network performance; Key (lock); Index (typography); Service (business); Operations research; Reliability engineering; Data mining; Artificial intelligence; Engineering; Computer security","score_opus":0.012764669138819042,"score_gpt":0.2127197812782901,"score_spread":0.19995511213947106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2462907370","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":[0.014485916,0.000483768,0.95697737,0.00092098804,0.00007463771,0.00016356351,0.00062212604,0.00031037122,0.025961287],"genre_scores_gemma":[0.8681702,0.0014393126,0.09159522,0.00023222712,0.00012554588,0.00090851256,0.0011059635,0.00012618843,0.036296792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983285,0.0006198404,0.00006744135,0.00035740997,0.00037412363,0.00025258656],"domain_scores_gemma":[0.99820876,0.0009616643,0.00020393926,0.000047714035,0.00050853833,0.00006929433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026975756,0.0017319098,0.00093065336,0.0016433656,0.00079635077,0.0024078486,0.0020993792,0.0024232618,0.008309442],"category_scores_gemma":[0.00593513,0.00047557632,0.0012110468,0.0015268212,0.0009535767,0.0027735466,0.0014718383,0.0020792147,0.0012861181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012737993,0.000017966482,0.00032667682,0.00003611785,0.000014121607,0.000049413313,0.000044665696,0.9648676,0.00017813027,0.02843703,0.0006541892,0.0053613614],"study_design_scores_gemma":[0.0000031473633,0.000010057338,0.00006860089,0.0000062220583,0.0000049212745,0.000010014297,0.000014057092,0.989755,0.000051413608,0.009174685,0.0008975728,0.0000042804386],"about_ca_topic_score_codex":0.02231426,"about_ca_topic_score_gemma":0.009992798,"teacher_disagreement_score":0.02231426,"about_ca_system_score_codex":0.0032440494,"about_ca_system_score_gemma":0.0022395593,"threshold_uncertainty_score":0.044368744},"labels":[],"label_agreement":null},{"id":"W2465665672","doi":"10.1061/9780784479957.052","title":"PIPEiD: Pipeline Infrastructure Database","year":2016,"lang":"en","type":"article","venue":"Pipelines 2016","topic":"Water Systems and 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":"American Water (Canada)","funders":"","keywords":"Pipeline (software); Asset (computer security); Sustainability; Computer science; Asset management; Database; Engineering; Engineering management; Computer security; Business; Finance","score_opus":0.0063408154190379845,"score_gpt":0.19158223924073461,"score_spread":0.18524142382169662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465665672","genre_codex":"software","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030171538,0.0012233523,0.17892249,0.0040462404,0.0006307333,0.0014027135,0.33420587,0.37988773,0.09666376],"genre_scores_gemma":[0.051320564,0.0017600466,0.09468566,0.0035574418,0.00039788266,0.001858027,0.785753,0.026476396,0.034191012],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948166,0.00069805724,0.0008558561,0.00093221583,0.0022937714,0.0004034055],"domain_scores_gemma":[0.99113363,0.0018591925,0.00081207155,0.0026474108,0.0025861524,0.0009615611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059758644,0.0016838401,0.001427262,0.0051825023,0.0013695883,0.007170582,0.006870978,0.0022484611,0.07871939],"category_scores_gemma":[0.020459168,0.0015430738,0.0009144315,0.0063521657,0.000746061,0.012386654,0.0070321136,0.0032479882,0.07785028],"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":[0.0002517328,0.000052740303,0.001215409,0.00052242866,0.000032104872,0.00014238551,0.0002209044,0.001122147,0.0012454644,0.014911704,0.93165594,0.04862693],"study_design_scores_gemma":[0.00014035334,0.000046729394,0.0012710954,0.0001264117,0.000020937898,0.0001887222,0.000121655685,0.007000301,0.0034597027,0.007664539,0.9798758,0.000083803134],"about_ca_topic_score_codex":0.00707797,"about_ca_topic_score_gemma":0.0035847537,"teacher_disagreement_score":0.07871939,"about_ca_system_score_codex":0.0021894127,"about_ca_system_score_gemma":0.0050498107,"threshold_uncertainty_score":0.26334262},"labels":[],"label_agreement":null},{"id":"W2468132608","doi":"10.1139/cjce-2015-0382","title":"Characterization of surge superposition following 2-stage load rejection in hydroelectric power plant","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Surge; Superposition principle; Hydroelectricity; Surge tank; Stage (stratigraphy); Load rejection; Environmental science; Control theory (sociology); Mathematics; Engineering; Computer science; Geology; Electrical engineering; Mechanical engineering; Mathematical analysis","score_opus":0.0037824951569582115,"score_gpt":0.1453741569541614,"score_spread":0.14159166179720317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468132608","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":[0.9883758,0.000031264743,0.010266225,0.000015111578,0.0000033818126,0.000014979928,0.00006432292,0.00009293433,0.00113594],"genre_scores_gemma":[0.99939287,0.000006771815,0.00044884792,0.0000013771928,0.0000010792611,0.0000031539773,0.00003625094,0.0000038335597,0.00010585105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998253,0.000024167057,0.000009602151,0.000019858568,0.00008009585,0.00004091044],"domain_scores_gemma":[0.999255,0.00033787047,0.00022314907,0.000044488643,0.00008559241,0.000053858374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002797636,0.0002021383,0.0003150441,0.00053206953,0.0002019524,0.00030573548,0.00026850638,0.00024708724,0.0014017313],"category_scores_gemma":[0.0013507997,0.00015975375,0.0002924811,0.00055634254,0.00036328725,0.0003692723,0.0003262376,0.00031004896,0.0001025315],"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":[0.002976104,0.0003119028,0.06406793,0.0003174518,0.000108673405,0.0032572981,0.0008883518,0.5513555,0.3228203,0.0019598382,0.00050870946,0.051428013],"study_design_scores_gemma":[0.00001948397,0.0007231763,0.08805339,0.000008884355,0.000021607408,0.00023754792,0.00019613533,0.8758475,0.03429856,0.00035362114,0.0001988182,0.0000413633],"about_ca_topic_score_codex":0.0015996463,"about_ca_topic_score_gemma":0.001304644,"teacher_disagreement_score":0.0015996463,"about_ca_system_score_codex":0.00026925726,"about_ca_system_score_gemma":0.0001784463,"threshold_uncertainty_score":0.0046892166},"labels":[],"label_agreement":null},{"id":"W2468494510","doi":"10.1061/9780784479957.028","title":"Application of Neural Networks in Predicting the Remaining Useful Life of Water Pipelines","year":2016,"lang":"en","type":"article","venue":"Pipelines 2016","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline transport; Robustness (evolution); Artificial neural network; Breakage; Computer science; Engineering; Deep water; Reliability engineering; Operations research; Marine engineering; Artificial intelligence; Environmental engineering","score_opus":0.009160561232686253,"score_gpt":0.1949837572634584,"score_spread":0.18582319603077216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468494510","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":[0.7197417,0.0014690565,0.270109,0.00071505946,0.00013141707,0.000087825036,0.0005529605,0.00052282057,0.0066701397],"genre_scores_gemma":[0.99088913,0.00022989365,0.0077541173,0.000023647675,0.000012840475,0.000026544065,0.00014048441,0.000006584004,0.0009166755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997975,0.000061481405,0.000016532951,0.000046424313,0.00004588043,0.000032119755],"domain_scores_gemma":[0.9993333,0.0004362939,0.00006748072,0.000020101623,0.00012388744,0.000018964833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005884293,0.00063342205,0.00043429257,0.00058456993,0.0002791617,0.0006092225,0.00050207414,0.0007124304,0.0006714954],"category_scores_gemma":[0.0021251917,0.0002641764,0.00037823655,0.00053447834,0.00022829915,0.0005866267,0.00033689226,0.00052620866,0.00009984763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019547482,0.000020437268,0.0021135497,0.000009307382,0.000009146285,0.000023628647,0.000006060369,0.9896293,0.00015437862,0.00017850159,0.00011127644,0.00772479],"study_design_scores_gemma":[4.238046e-7,0.0000038260346,0.00021586569,9.3759047e-7,0.0000010595172,9.721896e-7,0.0000018095949,0.9996176,0.000060493556,0.000078018544,0.000018024017,9.19518e-7],"about_ca_topic_score_codex":0.046453454,"about_ca_topic_score_gemma":0.027584024,"teacher_disagreement_score":0.046453454,"about_ca_system_score_codex":0.0009802282,"about_ca_system_score_gemma":0.00078183645,"threshold_uncertainty_score":0.0923661},"labels":[],"label_agreement":null},{"id":"W2468904108","doi":"10.1007/s12517-016-2552-1","title":"Optimization of chlorination boosters in drinking water distribution network for Al-Khobar City in Saudi Arabia","year":2016,"lang":"en","type":"article","venue":"Arabian Journal of Geosciences","topic":"Water Systems and 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":"Université Laval; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"King Abdulaziz City for Science and Technology; King Fahd University of Petroleum and Minerals","keywords":"Environmental science; Environmental engineering","score_opus":0.011850368157526011,"score_gpt":0.210293437442746,"score_spread":0.19844306928522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468904108","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":[0.9871941,0.00026987595,0.0067606475,0.0003153458,0.000021966605,0.00006873287,0.00030932904,0.00011824125,0.004941659],"genre_scores_gemma":[0.9971064,0.000063963336,0.0013484126,0.000010480417,0.0000021718877,0.000011055083,0.000113086106,0.0000057551247,0.0013386714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975973,0.00006075784,0.000008188483,0.000039698192,0.000033651406,0.000097978125],"domain_scores_gemma":[0.99973947,0.00007428241,0.00003366581,0.000009721687,0.00010190841,0.00004105262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040480492,0.00059619383,0.0005198826,0.0004578077,0.0007010842,0.0009250442,0.0006382817,0.0006442205,0.0028878972],"category_scores_gemma":[0.00055524043,0.0003179262,0.00048839755,0.00044491867,0.00024951046,0.0004461841,0.0003305193,0.00040989261,0.00024425169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048182099,0.00030257212,0.010494297,0.00012034708,0.000059915597,0.00031764826,0.0000800433,0.9612126,0.008783634,0.0009188758,0.0018297465,0.015398522],"study_design_scores_gemma":[0.00006752606,0.00039221006,0.013160616,0.000012499336,0.00009426166,0.00004560118,0.0007366493,0.9780692,0.005461566,0.00052020524,0.0014189554,0.000020677848],"about_ca_topic_score_codex":0.061204374,"about_ca_topic_score_gemma":0.07833075,"teacher_disagreement_score":0.061204374,"about_ca_system_score_codex":0.002673301,"about_ca_system_score_gemma":0.0016812948,"threshold_uncertainty_score":0.121696234},"labels":[],"label_agreement":null},{"id":"W2472169529","doi":"10.14796/jwmm.r225-01","title":"A Model Maintenance Tool - Moving Forward with an Investment in a System-Wide Model","year":2006,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Investment (military); Computer science; Political science","score_opus":0.009473721976252665,"score_gpt":0.17606339836433654,"score_spread":0.1665896763880839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2472169529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029856343,0.00004585297,0.9335035,0.00055923813,0.00015998232,0.00019737742,0.0013929118,0.022121968,0.0121627785],"genre_scores_gemma":[0.33307377,0.000108471424,0.6538232,0.00015148989,0.000060680006,0.00040778998,0.0024739197,0.004183211,0.005717447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994935,0.00016650303,0.000036452486,0.00009725281,0.0001698045,0.000036451893],"domain_scores_gemma":[0.99683076,0.0013275251,0.00015429556,0.00080719916,0.00078443065,0.00009591693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018656319,0.00071253564,0.0006117906,0.0007194322,0.00049366284,0.0014689609,0.0024821325,0.000953044,0.011347984],"category_scores_gemma":[0.008146348,0.0007057761,0.0007589245,0.0007366142,0.00031182074,0.00282246,0.001015538,0.0015971761,0.0020540045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002387019,0.00017910216,0.0030665267,0.00014600018,0.00010953204,0.00018524642,0.00014804344,0.82192034,0.0032511684,0.01580654,0.016116122,0.13883261],"study_design_scores_gemma":[0.00005120317,0.00004129924,0.0002255502,0.00001700813,0.000031136362,0.000031825686,0.000021410448,0.9859605,0.0014754214,0.0033132383,0.008816024,0.000015473472],"about_ca_topic_score_codex":0.006186836,"about_ca_topic_score_gemma":0.0059299446,"teacher_disagreement_score":0.011347984,"about_ca_system_score_codex":0.00061420084,"about_ca_system_score_gemma":0.0015873573,"threshold_uncertainty_score":0.037962794},"labels":[],"label_agreement":null},{"id":"W2473924155","doi":"10.1061/(asce)hy.1943-7900.0001195","title":"Adaptive Hybrid Transient Formulation for Simulating Incompressible Pipe Network Hydraulics","year":2016,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Hydraulics; Transient flow; Transient (computer programming); Compressibility; Pipe network analysis; Unsteady flow; Mechanics; Engineering; Geotechnical engineering; Computer science; Geology; Physics; Surge; Aerospace engineering; Geomorphology","score_opus":0.01028406673538622,"score_gpt":0.1968899962984311,"score_spread":0.18660592956304486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473924155","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":[0.05737241,0.000101831516,0.935427,0.00014597268,0.000035888803,0.000060957453,0.00009158471,0.00039306693,0.0063712625],"genre_scores_gemma":[0.9142381,0.00011438354,0.08140381,0.000055022112,0.000021569338,0.00017461096,0.00008304724,0.00007968568,0.0038297467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988544,0.00004513985,0.000005294192,0.000016113938,0.000033751585,0.000014334726],"domain_scores_gemma":[0.9997509,0.0001432511,0.000034450535,0.000019450563,0.000036439487,0.000015452333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003121664,0.0003859223,0.00037910038,0.00034912146,0.00022914878,0.0005336148,0.0007892674,0.0009110048,0.0017791128],"category_scores_gemma":[0.0009525705,0.00020184883,0.00036410778,0.0003605435,0.0007307561,0.0006915109,0.0005370278,0.0004983473,0.00015850134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012361197,0.000008841929,0.00021426793,0.000010028786,0.0000031628506,0.0000221388,0.000016046937,0.9922018,0.0009692465,0.004240889,0.00010434462,0.0021968023],"study_design_scores_gemma":[0.000001437588,0.0000039805204,0.000020906862,6.51298e-7,4.33425e-7,0.0000018835241,0.000002370444,0.99936455,0.00012198069,0.000376976,0.00010385999,9.514635e-7],"about_ca_topic_score_codex":0.003892679,"about_ca_topic_score_gemma":0.0026864263,"teacher_disagreement_score":0.003892679,"about_ca_system_score_codex":0.0005545293,"about_ca_system_score_gemma":0.0005735808,"threshold_uncertainty_score":0.0077400804},"labels":[],"label_agreement":null},{"id":"W2474548818","doi":"10.1061/9780784479957.054","title":"Prioritize Your Capital Spending: Make Informed Decisions Using Non-Destructive Acoustic Testing","year":2016,"lang":"en","type":"article","venue":"Pipelines 2016","topic":"Water Systems and 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":"Hydrogenics (Canada)","funders":"","keywords":"Asset (computer security); Service (business); Asset management; Computer science; Plan (archaeology); Capital (architecture); Pyramid (geometry); Capital expenditure; Risk analysis (engineering); Business; Finance; Computer security; Marketing","score_opus":0.04433926453835942,"score_gpt":0.262100730128548,"score_spread":0.21776146559018858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474548818","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":[0.1224888,0.00734194,0.5041985,0.23098941,0.001527597,0.0044829967,0.0022302133,0.0030592922,0.123681284],"genre_scores_gemma":[0.46705174,0.0046270746,0.5089524,0.007557301,0.0005808777,0.0015602877,0.0010642064,0.00048722548,0.008118896],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98777777,0.0070962096,0.0007228248,0.0007540211,0.002558478,0.0010906311],"domain_scores_gemma":[0.9526494,0.028892383,0.0044890363,0.002590556,0.0070277387,0.004350956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024383152,0.0016282209,0.0015763936,0.0037286778,0.0025658987,0.011007226,0.003393429,0.004234367,0.012276594],"category_scores_gemma":[0.060021084,0.0010959844,0.00087797176,0.0018586958,0.0024362644,0.00958295,0.0045955624,0.0056535895,0.005522355],"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":[0.00055724016,0.0013070971,0.02677367,0.0009031312,0.00035654934,0.0013447963,0.003369593,0.059390027,0.0044324286,0.04049566,0.15617591,0.7048938],"study_design_scores_gemma":[0.00036463054,0.0010809865,0.031582475,0.0021881047,0.00034402826,0.0006885285,0.018238926,0.1754311,0.01109103,0.52665055,0.23144871,0.0008910123],"about_ca_topic_score_codex":0.0055379,"about_ca_topic_score_gemma":0.010933495,"teacher_disagreement_score":0.024383152,"about_ca_system_score_codex":0.0036739013,"about_ca_system_score_gemma":0.013263556,"threshold_uncertainty_score":0.12895203},"labels":[],"label_agreement":null},{"id":"W2505074026","doi":"10.1016/j.knosys.2016.07.024","title":"Predicting water main failures: A Bayesian model updating approach","year":2016,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Water Systems and Optimization","field":"Engineering","cited_by":41,"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 British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Weibull distribution; Bayesian probability; Bayesian network; Data mining; Hazard; Machine learning; Artificial intelligence","score_opus":0.010802590889998367,"score_gpt":0.1915416843279791,"score_spread":0.18073909343798072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505074026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036993917,0.00040015165,0.9592401,0.00054473063,0.000057374175,0.00005832795,0.0001864004,0.00035042258,0.002168582],"genre_scores_gemma":[0.89716464,0.00050982257,0.09894821,0.00021722988,0.00013873879,0.00015732608,0.0003896722,0.00006960328,0.0024047417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899024,0.0003324772,0.00007836311,0.0002518152,0.00025729704,0.00008979998],"domain_scores_gemma":[0.99523854,0.0035938602,0.00035011314,0.00019883375,0.0005252799,0.00009338669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026924785,0.0011216651,0.0027748607,0.0016441592,0.0007049681,0.0019251334,0.0029592726,0.002004908,0.0022445559],"category_scores_gemma":[0.0107168965,0.0013459187,0.0012524471,0.0012518473,0.0009947043,0.0029909073,0.0013736102,0.0023315628,0.00040049406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041653988,0.00005169127,0.00065748196,0.000029441535,0.00006030391,0.00002025132,0.000019647618,0.9764946,0.00016141626,0.0024055701,0.00035914575,0.01969882],"study_design_scores_gemma":[0.0000051246475,0.0000053298627,0.000090240945,0.000002649252,0.000010950465,0.0000033822819,0.0000016342518,0.99761885,0.000050038707,0.0021578288,0.00004957176,0.000004365771],"about_ca_topic_score_codex":0.0287414,"about_ca_topic_score_gemma":0.024587724,"teacher_disagreement_score":0.0287414,"about_ca_system_score_codex":0.0012871786,"about_ca_system_score_gemma":0.0017260368,"threshold_uncertainty_score":0.05714822},"labels":[],"label_agreement":null},{"id":"W2511484140","doi":"10.2166/wh.2016.007","title":"Exploring the relationships between small non-community drinking water system characteristics and water system performance in Ontario, Canada","year":2016,"lang":"en","type":"article","venue":"Journal of Water and Health","topic":"Water Systems and 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":true,"ca_institutions":"Public Health Agency of Canada; Toronto Metropolitan University; University of Guelph","funders":"Ministry of Education, India; Ministry of Earth Sciences; Ontario Ministry of Health and Long-Term Care","keywords":"Environmental science; Water resource management; Environmental planning; Environmental engineering","score_opus":0.07748337098281124,"score_gpt":0.20138387311807818,"score_spread":0.12390050213526693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511484140","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":[0.9965144,0.00018197238,0.00018252162,0.0002479873,0.0000045223883,0.000031988646,0.0011702864,0.000006074962,0.0016602228],"genre_scores_gemma":[0.9987184,0.00011512378,0.00014122544,0.000030750612,0.0000018391411,0.000009728259,0.00042266113,0.0000023779994,0.00055804837],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986494,0.00016021439,0.00008719798,0.00019931172,0.00052387785,0.00038001902],"domain_scores_gemma":[0.99391186,0.0007651396,0.0015777057,0.00015808964,0.00224712,0.0013400072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011220494,0.0002595549,0.00032157937,0.0008119467,0.0016507407,0.0013876277,0.0009423718,0.00025577977,0.0014487361],"category_scores_gemma":[0.0040675057,0.0002819198,0.0004900472,0.0031677927,0.0010011476,0.00053699786,0.00073558756,0.00046444364,0.00011983089],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027863316,0.000017110897,0.9964185,0.000025480966,0.000030999792,0.00003396213,0.0008406827,0.00019016223,0.00013095836,0.00006768658,0.00027449225,0.0019420341],"study_design_scores_gemma":[0.0000016746767,0.000018579218,0.9978567,0.000009495654,0.000008054325,0.00000962201,0.0013268982,0.0002820144,0.000030256693,0.000014306186,0.00043854967,0.0000038393437],"about_ca_topic_score_codex":0.9946557,"about_ca_topic_score_gemma":0.9978351,"teacher_disagreement_score":0.028165704,"about_ca_system_score_codex":0.028165704,"about_ca_system_score_gemma":0.03334589,"threshold_uncertainty_score":0.20435745},"labels":[],"label_agreement":null},{"id":"W2512022364","doi":"10.2166/ws.2006.824","title":"The Canadian National Water and Wastewater Benchmarking Initiative. Using process to drive improvement: strategic management of water in urban areas","year":2006,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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":"Benchmarking; Benchmark (surveying); Best practice; Teamwork; Process (computing); Work (physics); Metric (unit); Process management; Business; Environmental economics; Computer science; Operations management; Environmental planning; Engineering; Marketing; Environmental science; Economics","score_opus":0.00862645696555485,"score_gpt":0.20227489983410774,"score_spread":0.19364844286855287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512022364","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":[0.040063705,0.0322118,0.06470053,0.08531315,0.0027326257,0.0031848252,0.074046984,0.004915707,0.6928306],"genre_scores_gemma":[0.494834,0.028900124,0.19918431,0.0074847885,0.00023908053,0.0017420164,0.07060575,0.0013922162,0.1956177],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.98750997,0.0014810506,0.00035687993,0.00035387446,0.009052268,0.0012459144],"domain_scores_gemma":[0.9842845,0.0006013015,0.0005890949,0.00047378166,0.012705679,0.0013456055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008311831,0.0011214552,0.00045957326,0.005526169,0.004666972,0.0036452438,0.0015941482,0.0009661162,0.005405828],"category_scores_gemma":[0.011724422,0.00033086454,0.00041208588,0.013343926,0.001530439,0.002099557,0.0023651393,0.001209816,0.0009563272],"study_design_candidate":"observational","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":[0.00013522552,0.00014801054,0.012838536,0.0007461331,0.000052923166,0.00010257379,0.0011420029,0.0032481793,0.0011929281,0.048940994,0.62003887,0.31141362],"study_design_scores_gemma":[0.00005857197,0.00010330107,0.074152015,0.0006266647,0.00007624843,0.00007133333,0.0024580997,0.004472205,0.0028026327,0.008362872,0.9066946,0.000121390694],"about_ca_topic_score_codex":0.9790862,"about_ca_topic_score_gemma":0.98434526,"teacher_disagreement_score":0.05822546,"about_ca_system_score_codex":0.05822546,"about_ca_system_score_gemma":0.1750591,"threshold_uncertainty_score":0.42245728},"labels":[],"label_agreement":null},{"id":"W2513859524","doi":"10.1080/1573062x.2016.1223323","title":"A practical overview of unsteady pipe flow modeling: from physics to numerical solutions","year":2016,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Water hammer; Transient (computer programming); Confusion; Stability (learning theory); Computer science; Flow (mathematics); Unsteady flow; Numerical analysis; Applied mathematics; Engineering; Mechanics; Mechanical engineering; Mathematics; Physics","score_opus":0.06791399298666562,"score_gpt":0.2561112467963144,"score_spread":0.1881972538096488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513859524","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":[0.0011894852,0.036839828,0.93958485,0.0023787753,0.0006487683,0.000069447655,0.00012589061,0.00048679442,0.018676084],"genre_scores_gemma":[0.07043323,0.21711561,0.6742793,0.0019014076,0.0033091132,0.00070643396,0.00049608987,0.0006010498,0.03115781],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934775,0.00023232913,0.000062149804,0.000077101875,0.00025038395,0.000030165149],"domain_scores_gemma":[0.9995284,0.00022250027,0.00005444103,0.000062815605,0.00011114223,0.000020663365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010386707,0.0014031075,0.0008323491,0.0017107715,0.00063868845,0.002401033,0.0018875324,0.002104463,0.0047833417],"category_scores_gemma":[0.0018124846,0.0008600172,0.0008476626,0.0023909358,0.0019700064,0.0034363775,0.0015065114,0.0025389679,0.0029940913],"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":[0.000027895232,0.000114661925,0.0004877965,0.001387559,0.000029330324,0.0002277698,0.00033903783,0.12881324,0.0050312653,0.67147076,0.02175509,0.1703157],"study_design_scores_gemma":[0.000010675652,0.00016900705,0.0006315766,0.0007623043,0.00001834311,0.00043995763,0.00014698207,0.2587195,0.002383386,0.3869286,0.34970665,0.00008300916],"about_ca_topic_score_codex":0.0019383101,"about_ca_topic_score_gemma":0.0013531432,"teacher_disagreement_score":0.0047833417,"about_ca_system_score_codex":0.0008813799,"about_ca_system_score_gemma":0.0010843416,"threshold_uncertainty_score":0.01600182},"labels":[],"label_agreement":null},{"id":"W2531814490","doi":"","title":"Investigating Drinking Water Advisories in First Nations Communities through Data Mining","year":2016,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"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":"Geography; Data science; Environmental planning; Engineering; Computer science","score_opus":0.028222770824009563,"score_gpt":0.2152030576600987,"score_spread":0.18698028683608914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531814490","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":[0.9782491,0.0003554586,0.010617263,0.0004746914,0.000009423508,0.00017360717,0.0069293287,0.000099295015,0.0030918822],"genre_scores_gemma":[0.95994574,0.00049710285,0.029939383,0.000047409125,0.0000068258164,0.000095058946,0.0086658215,0.000012796551,0.00078985636],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99872774,0.00039414625,0.000108279935,0.00017911365,0.00044391977,0.00014677277],"domain_scores_gemma":[0.9931577,0.0040753423,0.0010648214,0.00029466863,0.0012520807,0.00015539471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019524829,0.00035877217,0.00035850465,0.0035118973,0.0006964407,0.0012696021,0.0006353235,0.00036192976,0.0005782364],"category_scores_gemma":[0.007980667,0.0002406011,0.00055978907,0.0052482556,0.00032568537,0.00071754545,0.0004814413,0.0005957164,0.00011329119],"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":[0.00012714484,0.00039578634,0.84150547,0.00031142085,0.000277085,0.00029649236,0.0015120504,0.045471385,0.0016154513,0.001482362,0.0025990945,0.104406305],"study_design_scores_gemma":[0.000016790758,0.00016846586,0.6973463,0.0002236506,0.00014137586,0.00017926701,0.010097691,0.27506703,0.0038505297,0.0026041975,0.010235156,0.00006957732],"about_ca_topic_score_codex":0.3269456,"about_ca_topic_score_gemma":0.3978573,"teacher_disagreement_score":0.6730544,"about_ca_system_score_codex":0.0022734194,"about_ca_system_score_gemma":0.0037427563,"threshold_uncertainty_score":0.6500851},"labels":[],"label_agreement":null},{"id":"W2535971359","doi":"10.1139/cjce-2016-0209","title":"Numerical modelling of pipelines with air pockets and air valves","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":38,"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":"Water hammer; Adiabatic process; Isothermal process; Mechanics; Residual; Work (physics); Environmental science; Air water; Pipeline transport; Transient (computer programming); Engineering; Thermodynamics; Mechanical engineering; Physics; Computer science; Environmental engineering","score_opus":0.007257569912712644,"score_gpt":0.14803686294882246,"score_spread":0.1407792930361098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535971359","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":[0.62161165,0.0018152747,0.314044,0.0009498333,0.00029197708,0.00024001078,0.0014282061,0.00078863144,0.05883054],"genre_scores_gemma":[0.9776393,0.00044877036,0.012771647,0.000036375368,0.000028999451,0.00010968289,0.00024130687,0.00006513039,0.008658785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997081,0.000074231146,0.000020534077,0.000048262515,0.000070971146,0.00007787426],"domain_scores_gemma":[0.9994387,0.00028370577,0.00009130294,0.000037759495,0.000081790764,0.00006678248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041801113,0.0007673826,0.0010269075,0.0006133196,0.0007034068,0.0016854488,0.0011359222,0.0024791274,0.0031515888],"category_scores_gemma":[0.0016731913,0.0006889028,0.00095556205,0.00075368857,0.001317961,0.0009289706,0.001162302,0.0008399632,0.00042776638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024162922,0.000015470052,0.0004021468,0.000029068795,0.000005340319,0.00006960551,0.000021679118,0.9962186,0.00093385414,0.0014227062,0.00009028865,0.0007670789],"study_design_scores_gemma":[0.000006364866,0.000013210589,0.000161792,0.000004709581,0.0000024793453,0.000012756911,0.000012779354,0.99896824,0.00017518847,0.0003496589,0.0002889182,0.0000040320424],"about_ca_topic_score_codex":0.018699836,"about_ca_topic_score_gemma":0.0076035806,"teacher_disagreement_score":0.018699836,"about_ca_system_score_codex":0.0008975198,"about_ca_system_score_gemma":0.0014108347,"threshold_uncertainty_score":0.037181973},"labels":[],"label_agreement":null},{"id":"W2538127543","doi":"10.1115/ipc2000-248","title":"False Flow Registration in the Return Run of a Meter Station Due to Pulsation Amplifications","year":2000,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Nova Chemicals (Canada)","funders":"","keywords":"Flow (mathematics); Metering mode; Metre; Flow measurement; Acoustics; Ultrasonic flow meter; Computer science; Engineering; Simulation; Electrical engineering; Mechanical engineering; Physics; Mechanics","score_opus":0.014018758702962585,"score_gpt":0.21319185098801116,"score_spread":0.19917309228504856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2538127543","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":[0.97515523,0.000083069426,0.022808798,0.000060175957,0.00006389358,0.00002903449,0.000058618294,0.0008689001,0.00087225507],"genre_scores_gemma":[0.9954436,0.000019501018,0.0036477286,0.000023209901,0.000016155738,0.000009979009,0.00006653608,0.000050199025,0.00072317664],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989931,0.00012874971,0.00005374924,0.00024294207,0.0004669123,0.00011452286],"domain_scores_gemma":[0.9976144,0.00095409184,0.000613631,0.0004113136,0.00028313932,0.00012337137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009099222,0.0003936707,0.0005390376,0.0009035527,0.00037669067,0.00047280305,0.00073480146,0.00072595914,0.0014620675],"category_scores_gemma":[0.0029151554,0.00039202347,0.00034699202,0.000399796,0.0005468414,0.00045758105,0.0006421773,0.0007916145,0.0004215781],"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":[0.0019805965,0.0001674939,0.14372745,0.00024115512,0.00009902548,0.0074515925,0.0037208716,0.00784256,0.74043554,0.0006716479,0.0011473104,0.09251477],"study_design_scores_gemma":[0.000086429485,0.003709861,0.41868526,0.00007525856,0.00024377975,0.0047973306,0.0011459627,0.06021889,0.5029455,0.0006493732,0.0072401213,0.00020217549],"about_ca_topic_score_codex":0.00065924914,"about_ca_topic_score_gemma":0.0007490184,"teacher_disagreement_score":0.0014620675,"about_ca_system_score_codex":0.00029649652,"about_ca_system_score_gemma":0.00020055122,"threshold_uncertainty_score":0.0048910975},"labels":[],"label_agreement":null},{"id":"W2539874559","doi":"10.1109/epc.2007.4520313","title":"Electricity Usage in Water Distribution Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"University of Toronto","funders":"","keywords":"Peaking power plant; Electricity; Leakage (economics); Electricity generation; Leverage (statistics); Energy conservation; Energy storage; Environmental science; Electric potential energy; Distributed generation; Pumped-storage hydroelectricity; Mains electricity; Water conservation; Electric power distribution; Environmental economics; Engineering; Renewable energy; Power (physics); Computer science; Electrical engineering; Water resources; Economics; Physics","score_opus":0.0037521775815763914,"score_gpt":0.1704210211975968,"score_spread":0.1666688436160204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2539874559","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":[0.91917634,0.00059137325,0.0029359926,0.00022060321,0.000008953024,0.000016001612,0.0012309282,0.000033189895,0.075786695],"genre_scores_gemma":[0.9943614,0.00021967149,0.00027139756,0.0000054662337,0.0000019103725,0.0000024767035,0.00020882726,0.0000068764857,0.0049220603],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997309,0.000050564162,0.0000076115393,0.000017738588,0.00013113256,0.000062021245],"domain_scores_gemma":[0.99977416,0.00006496093,0.00002788616,0.000012899984,0.00010243705,0.000017761397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011207539,0.00009771569,0.0000950088,0.0005706507,0.0003640604,0.00061898533,0.00023910549,0.00012390132,0.0029385332],"category_scores_gemma":[0.0008363162,0.000060614922,0.00009257223,0.0018110028,0.00023432967,0.0003369393,0.00015791993,0.00006426375,0.00018036562],"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":[0.0003842171,0.00007638326,0.3844897,0.0002789814,0.000107051696,0.0034291204,0.0016332705,0.28454632,0.022263914,0.055632785,0.009644907,0.23751345],"study_design_scores_gemma":[0.000019770092,0.00012253721,0.6830438,0.00010775113,0.00007410488,0.0014379099,0.004043869,0.17336853,0.010233868,0.0108794235,0.11661072,0.000057802994],"about_ca_topic_score_codex":0.3198672,"about_ca_topic_score_gemma":0.57923263,"teacher_disagreement_score":0.3198672,"about_ca_system_score_codex":0.0036096775,"about_ca_system_score_gemma":0.00086256873,"threshold_uncertainty_score":0.6360107},"labels":[],"label_agreement":null},{"id":"W2541570499","doi":"10.1109/epc.2007.4520312","title":"Smaller Hydro, Higher Risk","year":2007,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Hydroelectricity; Documentation; Hydropower; Risk analysis (engineering); Computer science; Engineering management; Engineering; Business","score_opus":0.006268255689055079,"score_gpt":0.17172038601944195,"score_spread":0.16545213033038686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2541570499","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":[0.26991865,0.0122863585,0.070645586,0.05334942,0.0031663359,0.0005325859,0.00365147,0.0012786683,0.5851709],"genre_scores_gemma":[0.8298921,0.0060332925,0.028864864,0.005476851,0.0011703715,0.00033307064,0.0009435835,0.000654251,0.1266317],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.997974,0.0005077926,0.00013086927,0.00029066092,0.0009056393,0.00019114527],"domain_scores_gemma":[0.9955122,0.001133384,0.0010836174,0.0005073367,0.0009031791,0.00086021505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001983574,0.0004730848,0.00039891733,0.0011987922,0.0010644951,0.003461604,0.00070832315,0.0010358543,0.058872793],"category_scores_gemma":[0.0095512895,0.00023227632,0.0003011551,0.0012961495,0.0020869044,0.0029961958,0.0025558297,0.001307974,0.007324477],"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":[0.00058644597,0.00039211128,0.06511856,0.0011971248,0.000118434786,0.0024402705,0.0030155084,0.0055859867,0.0076681143,0.3709505,0.110968016,0.43195888],"study_design_scores_gemma":[0.000120545876,0.00042329892,0.04849954,0.00045065588,0.00008000038,0.0041689547,0.0039720414,0.0021461619,0.0022801196,0.20157059,0.7361834,0.000104753344],"about_ca_topic_score_codex":0.004346825,"about_ca_topic_score_gemma":0.0051476853,"teacher_disagreement_score":0.058872793,"about_ca_system_score_codex":0.001656824,"about_ca_system_score_gemma":0.0020335305,"threshold_uncertainty_score":0.19694912},"labels":[],"label_agreement":null},{"id":"W2546423191","doi":"10.1115/1.4035116","title":"On the History, Science, and Technology Included in the Moody Diagram","year":2016,"lang":"en","type":"article","venue":"Journal of Fluids Engineering","topic":"Water Systems and 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":"Royal Military College of Canada","funders":"","keywords":"Diagram; Buckingham; Dimensionless quantity; Flow (mathematics); Computer science; Calculus (dental); Mathematics; Mechanics; Physics; Geometry; History; Art history","score_opus":0.0062010599960669955,"score_gpt":0.16882974618306484,"score_spread":0.16262868618699786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546423191","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":[0.01324665,0.06442835,0.16960746,0.0166946,0.008189177,0.00007314372,0.00030623155,0.00045327892,0.72700113],"genre_scores_gemma":[0.62221164,0.06791028,0.13005312,0.010585497,0.010901339,0.00046135654,0.00039405844,0.00094807864,0.1565347],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99905354,0.00045688704,0.00005218707,0.00017172213,0.00018704242,0.00007861015],"domain_scores_gemma":[0.99914956,0.00044837865,0.0000682532,0.000099970785,0.00015825393,0.00007555595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013284321,0.00061413384,0.000509181,0.0017593974,0.0024390253,0.004379795,0.0005481539,0.00110046,0.007838352],"category_scores_gemma":[0.0031336513,0.00024007796,0.00038115165,0.0016123219,0.008639475,0.007449085,0.001551177,0.0028361764,0.0026063703],"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":[0.000005281045,0.0000020731895,0.000022528064,0.000027184142,0.0000010443687,0.000016359565,0.00019518749,0.0001093521,0.00005479232,0.9895057,0.0034129478,0.0066474914],"study_design_scores_gemma":[0.0000040729847,0.000013003592,0.00007899834,0.000074211996,0.0000023839098,0.00007323766,0.0001171278,0.00034405687,0.000095766925,0.82820594,0.17097971,0.000011377935],"about_ca_topic_score_codex":0.0011818244,"about_ca_topic_score_gemma":0.00075066264,"teacher_disagreement_score":0.007838352,"about_ca_system_score_codex":0.0018008687,"about_ca_system_score_gemma":0.00082507194,"threshold_uncertainty_score":0.026221871},"labels":[],"label_agreement":null},{"id":"W2550447112","doi":"10.1109/iecon.2005.1569244","title":"Minimization of energy use in pipeline operations-an application to natural gas transmission system","year":2005,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Mathematical optimization; Pipeline (software); Computer science; Heuristics; Pipeline transport; Energy consumption; Optimization problem; Compressor station; Minification; Process (computing); Engineering; Mathematics","score_opus":0.006073308638949293,"score_gpt":0.19286228516294157,"score_spread":0.1867889765239923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550447112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30111343,0.0014000126,0.6740421,0.000984597,0.000039734965,0.00013414389,0.00017678514,0.0002468013,0.021862352],"genre_scores_gemma":[0.9518999,0.00058816705,0.042517547,0.000026538224,0.000015725147,0.000064764274,0.000047411984,0.000045604833,0.0047943722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998727,0.000046816487,0.0000037544291,0.000017375476,0.00004237386,0.000016977605],"domain_scores_gemma":[0.99978155,0.00016118927,0.000018648925,0.0000050703648,0.000026362428,0.0000073048977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042128284,0.0005251829,0.00046515875,0.00038205873,0.00034367887,0.000576284,0.00033693353,0.00060016103,0.0013106801],"category_scores_gemma":[0.00092950766,0.00021412752,0.00033307105,0.0006956125,0.0003839863,0.0005728687,0.00033746925,0.000339806,0.00007044849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003003079,0.000024931944,0.00032137736,0.0000577538,0.000010313451,0.000052332038,0.000023590665,0.9857026,0.001961501,0.0020064076,0.00020048127,0.009608689],"study_design_scores_gemma":[0.000005960941,0.000052004034,0.0003137648,0.000003624207,0.000007180564,0.000015884567,0.00001975924,0.9969405,0.0011665922,0.0010983755,0.0003719277,0.0000044076046],"about_ca_topic_score_codex":0.013779881,"about_ca_topic_score_gemma":0.010202335,"teacher_disagreement_score":0.013779881,"about_ca_system_score_codex":0.00091949274,"about_ca_system_score_gemma":0.00080979255,"threshold_uncertainty_score":0.027399361},"labels":[],"label_agreement":null},{"id":"W2551541876","doi":"10.1115/ipc2016-64193","title":"Pipeline Diagnostics With Ultrasonic Meters","year":2016,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring and Maintenance; Materials and Joining","topic":"Water Systems and 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":"Pipeline (software); Pipeline transport; Ultrasonic sensor; Metering mode; Instrumentation (computer programming); Leak; Computer science; Ultrasonic flow meter; Flow measurement; Real-time computing; Transient (computer programming); Engineering; Reliability engineering; Mechanical engineering; Operating system; Acoustics","score_opus":0.007244105538934597,"score_gpt":0.18423931845108915,"score_spread":0.17699521291215456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551541876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41838324,0.0036426007,0.49934772,0.0012704962,0.0004792656,0.0008254065,0.0059633544,0.022599207,0.047488824],"genre_scores_gemma":[0.821815,0.0015344074,0.1558937,0.00037862183,0.000073919655,0.00015611689,0.002019279,0.00030836504,0.017820567],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981623,0.0001398938,0.00006273179,0.00021579248,0.0013193991,0.00009991802],"domain_scores_gemma":[0.99910456,0.00023569846,0.00013772146,0.000094745155,0.00040024304,0.000027103864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062857906,0.0005879872,0.0005016474,0.0019212215,0.00047834084,0.0011919513,0.0009717765,0.0006416674,0.00359087],"category_scores_gemma":[0.0023804123,0.0003556164,0.00023881297,0.0028256318,0.00039940857,0.0012491941,0.0008095233,0.00065899413,0.0017518371],"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":[0.0007434261,0.00026035012,0.06964616,0.0007780436,0.00007180279,0.0006137926,0.0018846325,0.016864091,0.19812597,0.004230658,0.021458065,0.68532294],"study_design_scores_gemma":[0.0001250156,0.0015570532,0.11086418,0.00045868199,0.00021898422,0.0020262515,0.0024287845,0.17663184,0.40741673,0.0042908406,0.29366675,0.0003149243],"about_ca_topic_score_codex":0.0121993655,"about_ca_topic_score_gemma":0.021180945,"teacher_disagreement_score":0.0121993655,"about_ca_system_score_codex":0.0011273861,"about_ca_system_score_gemma":0.0011909895,"threshold_uncertainty_score":0.024256706},"labels":[],"label_agreement":null},{"id":"W2554363084","doi":"10.1115/ipc2016-64698","title":"A Novel Approach to Leak Sensitivity Testing of Computational Pipeline Monitoring Systems for Hydrocarbon Liquid Pipelines With Hydraulic Simulators","year":2016,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring and Maintenance; Materials and Joining","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"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":"Leak; Leak detection; Pipeline transport; Pipeline (software); Sensitivity (control systems); Computer science; Petroleum engineering; Engineering; Simulation; Mechanical engineering; Electronic engineering","score_opus":0.020273602538736352,"score_gpt":0.21559737092865902,"score_spread":0.19532376838992266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554363084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020726692,0.000029560537,0.97457325,0.00007147224,0.00002515397,0.00009727995,0.000056766487,0.0019790537,0.0024407187],"genre_scores_gemma":[0.4193969,0.00006812909,0.5770122,0.000086041146,0.000013878301,0.0002088818,0.00016390612,0.00025063474,0.0027993263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990403,0.00025124487,0.00005162991,0.00013393619,0.00046519513,0.000057596564],"domain_scores_gemma":[0.9988728,0.0004128667,0.0001354813,0.00027166106,0.0002677602,0.000039385326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072789605,0.00061920064,0.00044739075,0.00053070957,0.00040842898,0.00083169143,0.0013516047,0.000656619,0.0023580783],"category_scores_gemma":[0.0030965966,0.0004101514,0.00066708494,0.00029658302,0.0007147817,0.001006471,0.001142048,0.00093783234,0.00033930098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029337968,0.00030524543,0.003938208,0.0002823169,0.0001090139,0.00035123833,0.00039983657,0.72858876,0.103511885,0.046185482,0.001875917,0.11415882],"study_design_scores_gemma":[0.000009469275,0.000081625934,0.00016981085,0.00000868889,0.0000076040246,0.00004273548,0.00001431032,0.9779946,0.017331738,0.0021361636,0.002192683,0.0000105831305],"about_ca_topic_score_codex":0.0022862267,"about_ca_topic_score_gemma":0.0022228824,"teacher_disagreement_score":0.0023580783,"about_ca_system_score_codex":0.00075232156,"about_ca_system_score_gemma":0.001233036,"threshold_uncertainty_score":0.0078885555},"labels":[],"label_agreement":null},{"id":"W2554436524","doi":"10.1115/ipc2016-64076","title":"Technical Evaluation of the Consequence of HCDP Upsets From Gas Producers Along Pipeline Laterals","year":2016,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring and Maintenance; Materials and Joining","topic":"Water Systems and 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":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Upset; Pipeline transport; Natural gas; Dew point; Pipeline (software); Flow (mathematics); Condensation; Environmental science; Petroleum engineering; Nuclear engineering; Engineering; Mechanical engineering; Mechanics; Waste management; Physics; Thermodynamics","score_opus":0.018126085488643574,"score_gpt":0.23013005754828622,"score_spread":0.21200397205964264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554436524","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":[0.97938603,0.00013902898,0.017981643,0.00006278802,0.000047504935,0.000116922056,0.00021080137,0.00022970102,0.0018256175],"genre_scores_gemma":[0.9931311,0.00013236563,0.0043715974,0.00001553758,0.0000076540555,0.000036601225,0.00016955237,0.00003897804,0.0020965426],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849164,0.00022929926,0.000078028694,0.00027561924,0.00079118466,0.00013413765],"domain_scores_gemma":[0.99723107,0.0007591675,0.000503486,0.00032419976,0.0010702068,0.000111908244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018905991,0.0004643392,0.00028609208,0.00050680956,0.00078844134,0.0006768274,0.00052449433,0.0006470072,0.0018380474],"category_scores_gemma":[0.0032520245,0.00024937253,0.00043756294,0.0004355743,0.0006593613,0.00058398384,0.00060923584,0.0004968143,0.00052310387],"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":[0.0012837242,0.0002530719,0.039928615,0.000656803,0.00005796761,0.0013456759,0.0008882046,0.032825742,0.87550956,0.001042569,0.0005650291,0.04564308],"study_design_scores_gemma":[0.000024171512,0.0030417321,0.032239765,0.00002616746,0.000074067524,0.00032083862,0.00068812375,0.033131007,0.9266897,0.00023938586,0.0034742476,0.000050827133],"about_ca_topic_score_codex":0.002434102,"about_ca_topic_score_gemma":0.0033624922,"teacher_disagreement_score":0.002434102,"about_ca_system_score_codex":0.0015214904,"about_ca_system_score_gemma":0.0005997787,"threshold_uncertainty_score":0.011039197},"labels":[],"label_agreement":null},{"id":"W2560202645","doi":"10.5383/ijtee.11.01.009","title":"Energy Savings Potential Associated with Reactive Power in Water Pumping Systems","year":2016,"lang":"en","type":"article","venue":"International Journal of Thermal and Environmental Engineering","topic":"Water Systems and 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":"AC power; Work (physics); Power (physics); Environmental science; Energy (signal processing); Water pumping; Current (fluid); Process engineering; Electrical engineering; Engineering; Mathematics; Physics; Mechanical engineering","score_opus":0.0017926098746978917,"score_gpt":0.12961818554107746,"score_spread":0.12782557566637956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560202645","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":[0.9593175,0.00095917046,0.024072908,0.00028667797,0.000032431424,0.00003430384,0.00060419313,0.00015756008,0.014535202],"genre_scores_gemma":[0.9960575,0.00015598285,0.0018357603,0.000008796762,0.0000065887343,0.000009541294,0.00013644359,0.000014609969,0.0017748084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961275,0.00011529128,0.000014641603,0.000043012566,0.00015523788,0.000059090813],"domain_scores_gemma":[0.9996549,0.00014116763,0.000050707014,0.000034629244,0.00010777676,0.000010849343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003493923,0.00047622708,0.0002613273,0.0006296242,0.00024820407,0.0006017542,0.00036173535,0.00023762803,0.003067191],"category_scores_gemma":[0.000666473,0.000120970806,0.00032379714,0.001056226,0.00023663188,0.00087717146,0.00032030928,0.0002032928,0.00037095498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024986877,0.00025234284,0.028765945,0.0005997036,0.0002408249,0.00089018367,0.00019867382,0.45011055,0.1441675,0.008118497,0.001635145,0.36252183],"study_design_scores_gemma":[0.00012882458,0.0033717202,0.13961409,0.00016079641,0.0005578659,0.0011599262,0.0012441196,0.4795679,0.31915942,0.018578427,0.036342833,0.00011405555],"about_ca_topic_score_codex":0.00075393316,"about_ca_topic_score_gemma":0.0015936041,"teacher_disagreement_score":0.003067191,"about_ca_system_score_codex":0.0004973118,"about_ca_system_score_gemma":0.00023543464,"threshold_uncertainty_score":0.01026082},"labels":[],"label_agreement":null},{"id":"W2560706285","doi":"10.1139/cjce-2016-0244","title":"A framework for optimal reliability-based storm sewer network design in flat areas","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"Reliability (semiconductor); Flooding (psychology); Storm; Network planning and design; Optimal design; Storm Water Management Model; Stormwater; Civil engineering; Reliability engineering; Sanitary sewer; Computer science; Surface runoff; Engineering; Environmental engineering","score_opus":0.010585212282370316,"score_gpt":0.18291065003369117,"score_spread":0.17232543775132086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560706285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00665941,0.0001489683,0.98926544,0.00008311118,0.000012589312,0.000054208762,0.0000571648,0.000090113914,0.0036289357],"genre_scores_gemma":[0.5829531,0.00093935285,0.40771192,0.00007394531,0.00004355287,0.00058079325,0.00026625226,0.00018950898,0.007241461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996464,0.00011207749,0.000012979158,0.000060360737,0.000116705865,0.00005151153],"domain_scores_gemma":[0.99971515,0.00012728125,0.000041565647,0.000013485228,0.000083403946,0.00001905233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090842095,0.0011941708,0.0009841111,0.00090641977,0.0005496109,0.0009765206,0.0014367871,0.0010182079,0.0033653826],"category_scores_gemma":[0.0013544783,0.0007814197,0.00096594775,0.00064866093,0.00084724516,0.00079552137,0.0008705781,0.00087682053,0.00033717632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032593916,0.0000053087397,0.00004776622,0.000015267628,0.0000036638844,0.000019753255,0.0000111993895,0.9905372,0.000349577,0.0068777627,0.00012746628,0.002001759],"study_design_scores_gemma":[0.0000046769183,0.00001696586,0.000045909288,0.000005452276,0.000003670773,0.0000073940514,0.000009667049,0.9944706,0.00011057593,0.004705675,0.00061596563,0.000003409075],"about_ca_topic_score_codex":0.010202466,"about_ca_topic_score_gemma":0.009461949,"teacher_disagreement_score":0.010202466,"about_ca_system_score_codex":0.0013227819,"about_ca_system_score_gemma":0.0019109788,"threshold_uncertainty_score":0.020286143},"labels":[],"label_agreement":null},{"id":"W2562868041","doi":"10.4236/am.2016.718184","title":"Hydraulic Reliability Assessment and Optimal Rehabilitation/Upgrading Schedule for Water Distribution Systems","year":2016,"lang":"en","type":"article","venue":"Applied Mathematics","topic":"Water Systems and 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 Regina","funders":"","keywords":"Schedule; Reliability (semiconductor); Benchmark (surveying); Computer science; Monte Carlo method; Genetic algorithm; Hydraulic machinery; Exponential distribution; Mathematical optimization; Reliability engineering; Engineering; Mathematics; Statistics; Mechanical engineering; Geology","score_opus":0.007674948509190377,"score_gpt":0.21762986622992034,"score_spread":0.20995491772072997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562868041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11344911,0.00023784369,0.88260555,0.00016225393,0.00001390483,0.000049052946,0.00004999334,0.00016435687,0.0032679006],"genre_scores_gemma":[0.96200657,0.000109575354,0.037042752,0.000011940008,0.000008379632,0.00004514172,0.000039506645,0.00002681406,0.0007092629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996468,0.00013319071,0.000010878264,0.000050122977,0.00010933799,0.000049690014],"domain_scores_gemma":[0.9995937,0.00018546506,0.00008459486,0.000021888989,0.000091330774,0.000023074908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067176356,0.0005867866,0.00047718544,0.0006886494,0.00030449306,0.00044271408,0.00042951625,0.000494843,0.00065587193],"category_scores_gemma":[0.0017633404,0.0003137534,0.00039235957,0.00037222265,0.00049067143,0.0005344243,0.0004141789,0.00035999942,0.000060068953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060441325,0.0000043852356,0.00015725302,0.0000067782676,0.000002770316,0.0000073015954,0.000006035274,0.9954554,0.00036240855,0.001020855,0.000035618898,0.0029352098],"study_design_scores_gemma":[0.000002425097,0.000012432804,0.00013216192,0.0000015294878,0.0000032560902,0.000003965877,0.0000064790543,0.9981171,0.00032568607,0.0012868758,0.00010558515,0.0000025242134],"about_ca_topic_score_codex":0.008597406,"about_ca_topic_score_gemma":0.005395555,"teacher_disagreement_score":0.008597406,"about_ca_system_score_codex":0.0012886948,"about_ca_system_score_gemma":0.0016821658,"threshold_uncertainty_score":0.017094731},"labels":[],"label_agreement":null},{"id":"W2563665997","doi":"10.5942/jawwa.2017.109.0001","title":"Impact of Urban Development on Energy Use in a Distribution System","year":2016,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Kingston Health Sciences Centre","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Lift (data mining); Energy (signal processing); Enhanced Data Rates for GSM Evolution; Energy system; Energy source; Energy distribution; Renewable energy; Computer science; Engineering; Mathematics; Telecommunications; Statistics; Physics","score_opus":0.00431119273931165,"score_gpt":0.1770412656792725,"score_spread":0.17273007293996084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563665997","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":[0.99671835,0.000045732893,0.00051811215,0.00004906704,0.0000022395145,0.0000053012823,0.000059606395,0.0000052360483,0.0025963357],"genre_scores_gemma":[0.9997212,0.000033531713,0.00008350497,0.0000033831673,7.2634066e-7,0.0000013959319,0.000021783906,0.0000013570735,0.0001331676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951184,0.0001977273,0.00002487075,0.000040398478,0.00007896178,0.00014616431],"domain_scores_gemma":[0.999345,0.00023847986,0.00016705865,0.000054117838,0.00012583807,0.000069575224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003687056,0.00022798503,0.00011831385,0.00048524924,0.00042396152,0.0009698024,0.00017910941,0.00017091531,0.0018814837],"category_scores_gemma":[0.0016167869,0.00014431655,0.00024209199,0.0009855194,0.0006994185,0.00058355764,0.0008457208,0.00020247085,0.00009191077],"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":[0.0003152407,0.00018052635,0.828965,0.00007667571,0.00015660634,0.0007284182,0.00039896686,0.12648484,0.005269405,0.0048270533,0.00047891226,0.032118388],"study_design_scores_gemma":[0.00002191501,0.00041766858,0.923293,0.000037224014,0.0000970286,0.00035251142,0.002929179,0.05933711,0.0065253507,0.0018672429,0.0050887107,0.00003305882],"about_ca_topic_score_codex":0.012946752,"about_ca_topic_score_gemma":0.029902035,"teacher_disagreement_score":0.012946752,"about_ca_system_score_codex":0.0013405822,"about_ca_system_score_gemma":0.00054312526,"threshold_uncertainty_score":0.02574277},"labels":[],"label_agreement":null},{"id":"W2564012164","doi":"10.2495/sdp-v12-n1-89-97","title":"Pressure management by combining pressure reducing valves and pumps as turbines for water loss reduction and energy recovery","year":2016,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Water Systems and Optimization","field":"Engineering","cited_by":25,"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":"Reduction (mathematics); Environmental science; Pressure regulator; Energy recovery; Waste management; Petroleum engineering; Environmental engineering; Energy (signal processing); Engineering; Mechanical engineering","score_opus":0.004974960459851068,"score_gpt":0.19632400803598968,"score_spread":0.1913490475761386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564012164","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":[0.5786105,0.001364429,0.41043478,0.0002594016,0.00013647992,0.00017783506,0.00032965458,0.0016563954,0.007030458],"genre_scores_gemma":[0.97777313,0.0002842483,0.02057167,0.000015493426,0.000014128884,0.000026839387,0.000083539926,0.000025795707,0.0012052757],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954647,0.000097240714,0.000027463939,0.00008820948,0.00019428316,0.000046344496],"domain_scores_gemma":[0.9998022,0.000070265865,0.000037981146,0.000031874777,0.000047469057,0.00001021024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037111383,0.0005144523,0.0003782948,0.0004382475,0.00023736875,0.0008314819,0.00062114384,0.00039388714,0.0012703569],"category_scores_gemma":[0.00060884055,0.00022539594,0.0004556142,0.000423582,0.00030906973,0.00095809234,0.0003620811,0.00033215273,0.0003237876],"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":[0.00058137707,0.00038209686,0.01529473,0.00093044236,0.00015603576,0.00042273337,0.00021538806,0.20576423,0.43329203,0.004161916,0.0020660155,0.33673298],"study_design_scores_gemma":[0.0001347623,0.0019709996,0.018615324,0.00007652244,0.00032046283,0.00057527784,0.00018817239,0.6639471,0.28765526,0.0030170905,0.023397712,0.00010131719],"about_ca_topic_score_codex":0.0011134254,"about_ca_topic_score_gemma":0.0014826928,"teacher_disagreement_score":0.0012703569,"about_ca_system_score_codex":0.00030826047,"about_ca_system_score_gemma":0.00037517902,"threshold_uncertainty_score":0.0042497516},"labels":[],"label_agreement":null},{"id":"W2565778241","doi":"10.5539/mas.v11n3p76","title":"Application of Central Force Optimization Method to Design Transient Protection Devices for Water Transmission Pipelines","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Water hammer; Pipeline (software); Pipeline transport; Volume (thermodynamics); Reliability (semiconductor); Computer science; Transient (computer programming); Genetic algorithm; Transmission (telecommunications); Reduction (mathematics); Environmental science; Mechanical engineering; Engineering; Environmental engineering; Telecommunications; Mathematics","score_opus":0.017896742978503473,"score_gpt":0.23053682949841459,"score_spread":0.21264008651991112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565778241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07608311,0.0006114355,0.90981007,0.0002873143,0.00006722915,0.00011391652,0.00006606401,0.00033810083,0.012622819],"genre_scores_gemma":[0.85865057,0.00031633774,0.13626064,0.000060176,0.000020188274,0.00025655451,0.00006118686,0.00007232795,0.0043020495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998616,0.000051451767,0.0000050285653,0.000017040158,0.000042909644,0.000021974516],"domain_scores_gemma":[0.99972314,0.00015969953,0.00003565846,0.000012626978,0.000058461774,0.000010421545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053622684,0.00075152225,0.00070100435,0.0005681057,0.00041236723,0.00068813673,0.00055950583,0.0010865233,0.0021962067],"category_scores_gemma":[0.00084550073,0.0004441635,0.0006855204,0.0003578799,0.00044582254,0.00029455134,0.00039147187,0.0004936916,0.00019594672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012957736,0.000012322039,0.00010971135,0.000025272333,0.000008181067,0.00002313531,0.000010839683,0.9931552,0.0009289719,0.0007465058,0.00011642871,0.004850394],"study_design_scores_gemma":[0.0000030402314,0.000013101829,0.000029935894,0.0000021848234,0.0000019082793,0.0000035170972,0.0000029480534,0.9994567,0.00018637061,0.00012769828,0.00017133691,0.0000012380813],"about_ca_topic_score_codex":0.007971024,"about_ca_topic_score_gemma":0.006498615,"teacher_disagreement_score":0.007971024,"about_ca_system_score_codex":0.00056938257,"about_ca_system_score_gemma":0.0011801285,"threshold_uncertainty_score":0.015849233},"labels":[],"label_agreement":null},{"id":"W2565952787","doi":"10.1088/1755-1315/49/8/082009","title":"Cavitating vortex characterization based on acoustic signal detection","year":2016,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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":"Cavitation; Vortex; Acoustics; Sound pressure; SIGNAL (programming language); Mechanics; Vortex ring; Physics; Pressure sensor; Materials science; Computer science","score_opus":0.006051514148030555,"score_gpt":0.1579555909322948,"score_spread":0.15190407678426424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565952787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3619694,0.00046486067,0.6301126,0.00020643733,0.00015514053,0.000114572285,0.00022419184,0.0010975589,0.005655226],"genre_scores_gemma":[0.90041614,0.0003203997,0.09509586,0.00005664411,0.00010678946,0.00005521303,0.000293744,0.00010359179,0.0035516175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998122,0.00003601064,0.00000976811,0.000038723116,0.00007537302,0.000028042037],"domain_scores_gemma":[0.99948967,0.00018250107,0.00006482704,0.000034427772,0.00019578535,0.00003275661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034910362,0.00058182597,0.0004885433,0.0012121779,0.00019760586,0.0007890343,0.0003114574,0.00051871396,0.0024185253],"category_scores_gemma":[0.0011488047,0.00018613008,0.00022095602,0.0005142912,0.00029666422,0.0007340578,0.00046035156,0.00037739103,0.0009908038],"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":[0.0004949874,0.00012364087,0.0080834115,0.00015809051,0.000033374803,0.0001351717,0.000092908085,0.01494026,0.6789437,0.0009880728,0.0012471104,0.2947593],"study_design_scores_gemma":[0.000029160157,0.00037054357,0.013881779,0.000029362642,0.000052097406,0.0003195135,0.0001282456,0.7540855,0.22570229,0.0013007098,0.004050719,0.00005010383],"about_ca_topic_score_codex":0.0003055586,"about_ca_topic_score_gemma":0.00052774296,"teacher_disagreement_score":0.0024185253,"about_ca_system_score_codex":0.00010905518,"about_ca_system_score_gemma":0.00018874364,"threshold_uncertainty_score":0.0080907345},"labels":[],"label_agreement":null},{"id":"W2566310771","doi":"","title":"priority assessment model for water distribution networks","year":2016,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Water Systems and 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":"","keywords":"Criticality; Ranking (information retrieval); Pipeline transport; Business; Agency (philosophy); Critical infrastructure; Distribution (mathematics); Report card; Environmental economics; Asset management; Environmental planning; Engineering; Environmental science; Environmental engineering; Finance; Computer science; Economics; Computer security; Mathematics","score_opus":0.017501480174703685,"score_gpt":0.25851182296502573,"score_spread":0.24101034279032205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566310771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03252046,0.001208715,0.92441666,0.00212324,0.00027477278,0.0005448248,0.0022395386,0.00074096024,0.035930783],"genre_scores_gemma":[0.8096583,0.001557481,0.13650775,0.0004790151,0.00026231096,0.001145984,0.0026485496,0.00020094613,0.047539644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987255,0.0004142804,0.000056028646,0.00027166892,0.00024296409,0.00028948806],"domain_scores_gemma":[0.99768686,0.0012728187,0.00017365333,0.000038741753,0.00067332515,0.00015459326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026513666,0.0015072634,0.0012723106,0.0021310425,0.0008930665,0.0028766366,0.0029398985,0.0025432885,0.017959893],"category_scores_gemma":[0.0057522887,0.00069324,0.0015232778,0.0016337492,0.000847491,0.002619792,0.0016711513,0.0023890906,0.0016006799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046404137,0.000039269005,0.0009868458,0.00008361959,0.000031061023,0.00012798353,0.00007665159,0.9559463,0.00022313595,0.026738271,0.002582989,0.013117431],"study_design_scores_gemma":[0.000008710486,0.0000146097345,0.00011031777,0.000011809544,0.000009563405,0.000018110688,0.00003630685,0.9876044,0.00005217164,0.010769338,0.001359306,0.000005213555],"about_ca_topic_score_codex":0.033520877,"about_ca_topic_score_gemma":0.021127703,"teacher_disagreement_score":0.033520877,"about_ca_system_score_codex":0.003948399,"about_ca_system_score_gemma":0.0030312124,"threshold_uncertainty_score":0.06665152},"labels":[],"label_agreement":null},{"id":"W2567336766","doi":"10.1115/ipc2016-64081","title":"Valve Characteristics and Their Effect on Transient Surge Pressures in Delivery Terminals","year":2016,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Petroleum Technology Alliance Canada","funders":"","keywords":"Surge; Piping; Transient (computer programming); Closure (psychology); Pipeline (software); Closing (real estate); Mechanics; Flow (mathematics); Pipeline transport; Marine engineering; Control valves; Engineering; Environmental science; Mechanical engineering; Computer science; Electrical engineering; Physics","score_opus":0.00536343986064235,"score_gpt":0.1732111159015963,"score_spread":0.16784767604095394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567336766","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":[0.984318,0.00062801957,0.011464792,0.00009468548,0.00008251623,0.00004371351,0.0002812926,0.00056213606,0.0025248786],"genre_scores_gemma":[0.99874324,0.000108440865,0.0006357655,0.000012460041,0.0000045602605,0.00000705146,0.00007224731,0.000032330383,0.00038399745],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940205,0.00009096161,0.000050993945,0.00009202032,0.00022481952,0.00013914106],"domain_scores_gemma":[0.9958222,0.0027925163,0.0005944173,0.00015389652,0.00048548295,0.00015152762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000609476,0.0003369997,0.0004604742,0.0006207793,0.00042665156,0.0010861269,0.0004909844,0.00066292577,0.0025344386],"category_scores_gemma":[0.004726711,0.00026641792,0.00046360257,0.0004411024,0.00046293705,0.00086045574,0.0004045353,0.0006783349,0.00041355393],"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":[0.0043164357,0.00045244442,0.03321953,0.0014642418,0.00014341726,0.0032761903,0.0010914038,0.29772204,0.5935505,0.0016189822,0.0016760005,0.061468728],"study_design_scores_gemma":[0.00012400729,0.0058967457,0.05834456,0.00014068616,0.000255879,0.0018402095,0.0012958547,0.279084,0.6468951,0.0007058864,0.0052370075,0.0001800066],"about_ca_topic_score_codex":0.0012171158,"about_ca_topic_score_gemma":0.00080944266,"teacher_disagreement_score":0.0025344386,"about_ca_system_score_codex":0.0005326,"about_ca_system_score_gemma":0.0003738466,"threshold_uncertainty_score":0.008478582},"labels":[],"label_agreement":null},{"id":"W2567620487","doi":"10.2495/sdp-v12-n1-79-88","title":"Optimization of pump operations in a complex water supply network: new genetic algorithm frameworks","year":2016,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Genetic algorithm; Computer science; Water supply; Optimization algorithm; Mathematical optimization; Algorithm; Engineering; Environmental engineering; Mathematics; Machine learning","score_opus":0.00971811572527213,"score_gpt":0.21538443873640936,"score_spread":0.20566632301113724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567620487","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":[0.019642094,0.0009217525,0.97389585,0.00037149742,0.000054418233,0.000059881928,0.000041610416,0.00010077518,0.004912226],"genre_scores_gemma":[0.47324255,0.0024105583,0.5184937,0.00020448846,0.00016446481,0.0003648778,0.00010455595,0.00010880687,0.0049060294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962306,0.00017927404,0.000015580657,0.00004576825,0.000105119456,0.00003116992],"domain_scores_gemma":[0.9995253,0.00031778778,0.000043269552,0.000024088693,0.000063688436,0.000025861367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014217063,0.0007652146,0.0010315822,0.00069541135,0.00026266294,0.0012524221,0.0015843908,0.0014855964,0.001172588],"category_scores_gemma":[0.0018118047,0.0003596886,0.00094232504,0.0009545575,0.0011525325,0.0011858796,0.0007971525,0.0010453705,0.00015644888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000835053,0.000014602454,0.000120629025,0.000025346491,0.000016317688,0.000018845833,0.000018935727,0.9804734,0.00021352284,0.012965522,0.00014140346,0.005983045],"study_design_scores_gemma":[0.0000048489496,0.000006963827,0.000027589029,0.0000048722754,0.000003484297,0.0000050197664,0.0000032297335,0.9966272,0.000040447492,0.0028888197,0.0003852661,0.0000022809234],"about_ca_topic_score_codex":0.0036377127,"about_ca_topic_score_gemma":0.0029360363,"teacher_disagreement_score":0.0036377127,"about_ca_system_score_codex":0.0008997743,"about_ca_system_score_gemma":0.0010129219,"threshold_uncertainty_score":0.0075187683},"labels":[],"label_agreement":null},{"id":"W2571344044","doi":"10.14796/jwmm.r228-21","title":"Locating Leaks in Water Distribution Systems Using Network Modeling","year":2008,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Coquitlam College","funders":"","keywords":"Environmental science; Computer science; Distribution (mathematics); Mathematics","score_opus":0.026923074255170515,"score_gpt":0.1956719138475215,"score_spread":0.16874883959235099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571344044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03202747,0.00036745667,0.9601062,0.00020018936,0.000017192555,0.0000850005,0.00015850153,0.0005007663,0.0065372125],"genre_scores_gemma":[0.77295905,0.001997234,0.21297346,0.00006472371,0.000036829675,0.00045138548,0.00038896274,0.00016120297,0.010967093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996642,0.00014363196,0.000014890767,0.00007551618,0.00006923057,0.000032544467],"domain_scores_gemma":[0.9995239,0.00033625105,0.000060906914,0.000015594094,0.000049792714,0.000013622311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051539956,0.0012908764,0.0006798575,0.0010158723,0.00060361676,0.0013386888,0.0007884901,0.0009680483,0.0019001124],"category_scores_gemma":[0.0014142509,0.0006007101,0.0008446464,0.0009706805,0.0006353577,0.0015671558,0.00081088825,0.000647026,0.00027239032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006531802,0.000006175475,0.00028162514,0.000011471652,0.000004215342,0.000013842364,0.00001358609,0.99548584,0.00032102628,0.0015030568,0.000047252495,0.002305363],"study_design_scores_gemma":[0.0000011531426,0.0000036506292,0.00004972653,0.0000029254456,0.0000019636898,0.0000036243898,0.0000071578165,0.99840087,0.0001142906,0.0012030666,0.00020958083,0.000002019238],"about_ca_topic_score_codex":0.027339065,"about_ca_topic_score_gemma":0.0208993,"teacher_disagreement_score":0.027339065,"about_ca_system_score_codex":0.0014577613,"about_ca_system_score_gemma":0.0012454909,"threshold_uncertainty_score":0.054359853},"labels":[],"label_agreement":null},{"id":"W2573940776","doi":"10.1007/s12182-016-0144-z","title":"A parametric study of the hydrodynamic roughness produced by a wall coating layer of heavy oil","year":2017,"lang":"en","type":"article","venue":"Petroleum Science","topic":"Water Systems and 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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude; Shell Canada; Canadian Natural Resources Limited","keywords":"Coating; Materials science; Viscosity; Surface finish; Pipeline transport; Surface roughness; Composite material; Flow (mathematics); Volumetric flow rate; Petroleum engineering; Geotechnical engineering; Mechanics; Engineering; Mechanical engineering","score_opus":0.013110359330554615,"score_gpt":0.2323547990614636,"score_spread":0.21924443973090899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573940776","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":[0.9952179,0.000046898625,0.003921774,0.0000145692675,0.000005619648,0.000015624819,0.000042874704,0.00003076861,0.0007040201],"genre_scores_gemma":[0.99871075,0.000032610133,0.0010427322,0.0000019195468,0.0000013717637,0.0000065070776,0.000015243417,0.0000033535007,0.00018554546],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997867,0.000028577839,0.000010312589,0.00003295739,0.00007909324,0.00006224462],"domain_scores_gemma":[0.99934286,0.0003577023,0.0001035861,0.000092903676,0.000078187244,0.000024762228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031966376,0.00033975806,0.00029148257,0.00020649216,0.00018317733,0.00031533706,0.00023464735,0.0003679619,0.00048494016],"category_scores_gemma":[0.000964583,0.0001684774,0.0003377892,0.00019483599,0.0003897677,0.000269132,0.00027516627,0.00031246862,0.000068203335],"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":[0.00042873534,0.00023839652,0.0037419202,0.00016032667,0.000023917408,0.0003444192,0.00017376397,0.111581616,0.86964196,0.0004649103,0.00015716693,0.013042836],"study_design_scores_gemma":[0.000035146342,0.0026944154,0.015235023,0.000010358835,0.000061133316,0.00016480482,0.00013773084,0.28598073,0.69460875,0.00015253456,0.00085090735,0.00006846005],"about_ca_topic_score_codex":0.001000903,"about_ca_topic_score_gemma":0.00065309374,"teacher_disagreement_score":0.001000903,"about_ca_system_score_codex":0.00021645695,"about_ca_system_score_gemma":0.00020061032,"threshold_uncertainty_score":0.0019901395},"labels":[],"label_agreement":null},{"id":"W2576403206","doi":"10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0108","title":"Simulation-Based Optimization Methods Applied to Large Scale Water Systems Control","year":2016,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":6,"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; Scale (ratio); Optimal control; Control (management); Flood control; Industrial engineering; Operations research; Systems engineering; Control engineering; Flood myth; Mathematical optimization; Engineering","score_opus":0.007553146303721984,"score_gpt":0.233976481669831,"score_spread":0.22642333536610904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576403206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021901801,0.001678918,0.990815,0.00024652478,0.00012926938,0.00004904435,0.000027358408,0.00015572415,0.004707984],"genre_scores_gemma":[0.5595194,0.009580051,0.41546276,0.0002769395,0.0004820116,0.0009790204,0.00020497119,0.00042661632,0.013068105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992137,0.00048006224,0.000036718437,0.00004647953,0.00019475693,0.000028346925],"domain_scores_gemma":[0.9989353,0.00077201,0.00007701319,0.000049437906,0.00013842518,0.000027816488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010729209,0.0011821392,0.0013792943,0.0005873604,0.0004712202,0.0009359724,0.0007690081,0.00089395745,0.002457757],"category_scores_gemma":[0.0023052546,0.00058965036,0.0008446863,0.001133847,0.0010654574,0.0005893414,0.0010165878,0.00153867,0.00039389948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075962002,0.000009100118,0.000069345064,0.000046629655,0.000025521209,0.000015634,0.000011828058,0.97683924,0.0003459483,0.015020619,0.00024953976,0.0073590437],"study_design_scores_gemma":[0.000004842223,0.000006427292,0.00002828003,0.000008253915,0.0000035352887,0.000004369723,0.0000024792726,0.99219316,0.00011385034,0.0063714418,0.001260036,0.0000033906454],"about_ca_topic_score_codex":0.006734523,"about_ca_topic_score_gemma":0.0039816913,"teacher_disagreement_score":0.006734523,"about_ca_system_score_codex":0.0008818851,"about_ca_system_score_gemma":0.0014378936,"threshold_uncertainty_score":0.01339066},"labels":[],"label_agreement":null},{"id":"W2586499936","doi":"10.1007/s11269-017-1582-0","title":"Influence of Topography, Peak Demand, and Topology on Energy Use Patterns in four Small to Medium-Sized Systems in Ontario, Canada","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and 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":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Statistics; Efficient energy use; Energy (signal processing); Energy consumption; Environmental science; Mathematics; Simulation; Computer science; Engineering; Reliability engineering; Electrical engineering","score_opus":0.009798833935049668,"score_gpt":0.1720298473392747,"score_spread":0.16223101340422505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586499936","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":[0.9981902,0.000045930512,0.00008147844,0.0000800762,0.0000017521119,0.00001609561,0.00042110463,0.0000069017,0.0011564199],"genre_scores_gemma":[0.9986331,0.000045142595,0.00010324654,0.000010058919,6.796103e-7,0.000005341737,0.00029148796,0.000002370083,0.00090850587],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996037,0.0000454877,0.00002007872,0.00004734752,0.00009688462,0.00018662232],"domain_scores_gemma":[0.998841,0.00018697997,0.00011247557,0.000031690448,0.00049027894,0.00033745842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000281113,0.00026838738,0.00039147693,0.0007327009,0.0023522982,0.00161179,0.0010617645,0.0003543247,0.0017820421],"category_scores_gemma":[0.0013550165,0.00033520054,0.0004971552,0.0021385134,0.0014881762,0.00036346566,0.0008600131,0.00035544793,0.00014274893],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004196204,0.00013259331,0.965203,0.0000807496,0.00016555839,0.00065741345,0.0025933033,0.016570916,0.0021004432,0.0009893164,0.0020140116,0.009073162],"study_design_scores_gemma":[0.000026481857,0.000038017748,0.98104215,0.000016372263,0.000045268596,0.000061737504,0.0068695587,0.010056816,0.00027283307,0.00010660155,0.0014380974,0.000026013671],"about_ca_topic_score_codex":0.99501,"about_ca_topic_score_gemma":0.99809974,"teacher_disagreement_score":0.027949095,"about_ca_system_score_codex":0.027949095,"about_ca_system_score_gemma":0.017686723,"threshold_uncertainty_score":0.20278585},"labels":[],"label_agreement":null},{"id":"W2586634116","doi":"","title":"Apprentissage multiclasse en environnement incertain","year":2014,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Water Systems and 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":"Humanities; Political science; Computer science; Philosophy","score_opus":0.0075780624618601265,"score_gpt":0.1907203240140706,"score_spread":0.18314226155221047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586634116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47344464,0.001304001,0.5092922,0.00088149594,0.000658021,0.00010076985,0.0005443972,0.004465831,0.009308589],"genre_scores_gemma":[0.8447019,0.00083099504,0.12878054,0.00028466166,0.00040905562,0.000091805254,0.00083264185,0.000821038,0.023247372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972669,0.0005975639,0.000096334836,0.000706011,0.0007513796,0.0005819327],"domain_scores_gemma":[0.9942158,0.0028704046,0.0002547793,0.0009854935,0.0012639081,0.00040968298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033449712,0.00167732,0.0020478806,0.0012938639,0.0012607685,0.0028446487,0.0011369105,0.0019505036,0.0042426647],"category_scores_gemma":[0.0075126146,0.00040498364,0.0021528904,0.0010103211,0.0009088597,0.0022574456,0.0012261005,0.0029109989,0.001388409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002239048,0.0010372221,0.014497955,0.00040558865,0.00043931717,0.0034869658,0.0009214505,0.6026636,0.041965295,0.028935663,0.007844694,0.29556328],"study_design_scores_gemma":[0.000038934686,0.00022053416,0.0048637735,0.000037678103,0.000063473744,0.00067035126,0.00016345283,0.964257,0.0115516875,0.010511318,0.007578897,0.000042854954],"about_ca_topic_score_codex":0.028961126,"about_ca_topic_score_gemma":0.01600389,"teacher_disagreement_score":0.028961126,"about_ca_system_score_codex":0.0015418864,"about_ca_system_score_gemma":0.0011076223,"threshold_uncertainty_score":0.05758512},"labels":[],"label_agreement":null},{"id":"W2587160937","doi":"10.14796/jwmm.r225-11","title":"Comparison of Analytical and Simulation Approaches for Assessing Robustness of Reliability for Water Distribution Systems","year":2006,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robustness (evolution); Reliability engineering; Reliability (semiconductor); Computer science; Environmental science; Engineering; Physics; Thermodynamics; Chemistry","score_opus":0.05542254958375193,"score_gpt":0.2760292195012183,"score_spread":0.22060666991746636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587160937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12549886,0.004515557,0.85471666,0.0007751162,0.0001521198,0.0002072775,0.00020994054,0.00095716916,0.012967311],"genre_scores_gemma":[0.90694106,0.0020442388,0.08960085,0.00008013646,0.000093919196,0.00022741617,0.00019888571,0.00012034922,0.0006930248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99316967,0.0040604076,0.00033105933,0.00026226087,0.001814989,0.00036167228],"domain_scores_gemma":[0.9632982,0.029535258,0.0016087606,0.0020959203,0.0031536836,0.00030820273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00872752,0.0014093455,0.0010340298,0.004360769,0.0004776951,0.0013851982,0.0017681733,0.0012649165,0.0012347437],"category_scores_gemma":[0.0368556,0.00052959635,0.0011744093,0.0019217508,0.0010551633,0.0018643866,0.0012902318,0.0009787692,0.00015928024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019590196,0.000061069564,0.0011553174,0.00012382583,0.00008180541,0.000025977566,0.00007997153,0.95863956,0.00085340266,0.012151292,0.00030237812,0.026329476],"study_design_scores_gemma":[0.000012739755,0.00007565455,0.000354751,0.000028039583,0.000020633945,0.000020318967,0.000032510823,0.99405473,0.00071241107,0.00420442,0.00046803273,0.000015811393],"about_ca_topic_score_codex":0.0059343316,"about_ca_topic_score_gemma":0.0033063232,"teacher_disagreement_score":0.00872752,"about_ca_system_score_codex":0.002080213,"about_ca_system_score_gemma":0.001508815,"threshold_uncertainty_score":0.04615611},"labels":[],"label_agreement":null},{"id":"W2588084795","doi":"10.1109/tcns.2017.2670501","title":"Energy-Optimal Pump Scheduling and Water Flow","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Control of Network Systems","topic":"Water Systems and Optimization","field":"Engineering","cited_by":107,"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":"Mathematical optimization; Computer science; Relaxation (psychology); Scheduling (production processes); Integer (computer science); Control theory (sociology); Mathematics","score_opus":0.008301524251108645,"score_gpt":0.18458281740303784,"score_spread":0.1762812931519292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588084795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025983825,0.00034946387,0.9657188,0.00034104803,0.000055844033,0.00006345174,0.00022332458,0.00021737022,0.0070469673],"genre_scores_gemma":[0.78613204,0.0006127521,0.20478436,0.00013150048,0.00006772596,0.00016016485,0.0003931634,0.00014602618,0.0075723296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947876,0.00019539394,0.000016540705,0.00009738112,0.00011930315,0.0000926445],"domain_scores_gemma":[0.9994678,0.00032072823,0.0000825503,0.000026746615,0.00006296185,0.000039297247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079540163,0.0009172078,0.0009904277,0.0005109471,0.00040089837,0.0010822796,0.0007196136,0.0010023443,0.003342011],"category_scores_gemma":[0.002494233,0.0006382488,0.00052351766,0.001015807,0.00077259453,0.0015848374,0.00074953324,0.00090286764,0.0003052965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032076492,0.000016470878,0.000079498495,0.000025181282,0.000004974009,0.000018174658,0.000009011372,0.9841081,0.000433245,0.01039101,0.00042857602,0.0044536563],"study_design_scores_gemma":[0.000005557114,0.000008974097,0.000026946653,0.0000020234809,0.000001070381,0.0000034505135,0.0000040328587,0.9948984,0.00015111416,0.0045957556,0.00030084638,0.0000019078002],"about_ca_topic_score_codex":0.00950757,"about_ca_topic_score_gemma":0.0065220515,"teacher_disagreement_score":0.00950757,"about_ca_system_score_codex":0.0012263954,"about_ca_system_score_gemma":0.0019934287,"threshold_uncertainty_score":0.018904448},"labels":[],"label_agreement":null},{"id":"W2594867531","doi":"10.1016/j.ejor.2017.03.011","title":"Polynomial optimization for water networks: Global solutions for the valve setting problem","year":2017,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Water Systems and 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 Waterloo","funders":"","keywords":"Solver; Mathematical optimization; Polynomial; Quadratic programming; Optimization problem; Mathematics; Quadratic equation; Semidefinite programming; Computer science; Applied mathematics","score_opus":0.06819483357295815,"score_gpt":0.32004444419336636,"score_spread":0.2518496106204082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594867531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031110886,0.0010041678,0.9421935,0.0015762759,0.00008999582,0.00005574352,0.00022254538,0.0001499599,0.023596931],"genre_scores_gemma":[0.78506273,0.0019808596,0.18671453,0.0003396301,0.00025133984,0.00018172624,0.000336909,0.0003215271,0.02481083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996474,0.0001481596,0.000007833247,0.00007107995,0.000056099303,0.00006933532],"domain_scores_gemma":[0.9989786,0.00071722944,0.00007927131,0.000057298595,0.00008582659,0.000081729486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010286581,0.00097227935,0.0011308732,0.0004789184,0.00056253426,0.0015239661,0.0011863065,0.0017561512,0.0058146045],"category_scores_gemma":[0.0052353367,0.00040211724,0.00086745305,0.0009519174,0.0012099296,0.002300235,0.0016368319,0.002748615,0.0005708598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012250195,0.00009003306,0.00020687105,0.00021068605,0.000030018446,0.000075295706,0.00009929352,0.6898423,0.0013035485,0.27081695,0.007828634,0.029373797],"study_design_scores_gemma":[0.000041769938,0.000042686166,0.000082911465,0.000016562946,0.000012714242,0.000029119736,0.000046144756,0.8408457,0.0003380722,0.15616141,0.0023725452,0.000010376479],"about_ca_topic_score_codex":0.0029906414,"about_ca_topic_score_gemma":0.0036294044,"teacher_disagreement_score":0.0058146045,"about_ca_system_score_codex":0.0009609162,"about_ca_system_score_gemma":0.0012459375,"threshold_uncertainty_score":0.019451797},"labels":[],"label_agreement":null},{"id":"W2604184361","doi":"10.1111/wej.12248","title":"Performance management of small water treatment plant operations: a decision support system","year":2017,"lang":"en","type":"article","venue":"Water and Environment Journal","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Brock University; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fault tree analysis; Decision support system; Decision tree; Computer science; Risk analysis (engineering); Event (particle physics); Reliability engineering; Engineering; Process management; Data mining; Business","score_opus":0.011660986148839694,"score_gpt":0.1723095077236839,"score_spread":0.1606485215748442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604184361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36986628,0.0003989007,0.5953682,0.001726415,0.00013596124,0.0014907273,0.0028354586,0.018363839,0.009814178],"genre_scores_gemma":[0.91477776,0.00010202357,0.08260188,0.0000953476,0.000051433228,0.0002957346,0.00085138547,0.00007377891,0.0011506733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892044,0.00036502237,0.00013478732,0.0002226858,0.00029736027,0.000059762246],"domain_scores_gemma":[0.9963648,0.0020784596,0.00041728545,0.00021500817,0.00064494426,0.00027951997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025273063,0.00084125483,0.000667373,0.0014794841,0.00052366365,0.002261947,0.0009993084,0.0007046047,0.0038688967],"category_scores_gemma":[0.005675683,0.00026502024,0.00031459687,0.0008545605,0.00029880818,0.0012307345,0.0007111012,0.00054731965,0.00073700846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018022782,0.0018299425,0.01931616,0.00042110978,0.00019040951,0.00049086096,0.00046456765,0.4924973,0.018236915,0.0032546087,0.010411218,0.45108455],"study_design_scores_gemma":[0.00015125974,0.00038202514,0.0030206917,0.000034592493,0.000057210695,0.000041681848,0.00008454459,0.9834179,0.0081670405,0.0021360624,0.0024674055,0.00003961018],"about_ca_topic_score_codex":0.00356932,"about_ca_topic_score_gemma":0.0016217212,"teacher_disagreement_score":0.0038688967,"about_ca_system_score_codex":0.0011029568,"about_ca_system_score_gemma":0.0012354414,"threshold_uncertainty_score":0.013365865},"labels":[],"label_agreement":null},{"id":"W2604431481","doi":"10.1115/pvp2010-26004","title":"Liquid Leak Predictions in Micro and Nano-Porous Gaskets","year":2010,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Gasket; Leak; Extrapolation; Porous medium; Porosity; Materials science; Mechanics; Volumetric flow rate; Slip (aerodynamics); Leakage (economics); Boundary value problem; Thermodynamics; Composite material; Physics; Mathematics","score_opus":0.0035038451893443343,"score_gpt":0.16695724909186216,"score_spread":0.16345340390251784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604431481","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":[0.90023464,0.00058761134,0.09743682,0.00006163446,0.000013452238,0.000023928114,0.00008382102,0.00034998613,0.0012081726],"genre_scores_gemma":[0.99410456,0.00015447274,0.005361548,0.0000047579865,0.0000019950737,0.000009527509,0.000030930663,0.000011467179,0.00032065422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974483,0.000032132648,0.000015068457,0.000040945626,0.00013728188,0.000029776083],"domain_scores_gemma":[0.9994215,0.0003033275,0.00012015643,0.000031190484,0.00009846898,0.000025394515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034516156,0.00036390513,0.00044098968,0.0004619212,0.00017192133,0.00041146766,0.0004971253,0.0005613131,0.0003956953],"category_scores_gemma":[0.0013060838,0.00019858996,0.00030423136,0.00024226795,0.00068070873,0.00092540827,0.00045050515,0.0002784631,0.000108968794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027997408,0.00009376446,0.006650932,0.00020872566,0.00001568497,0.00020122745,0.00008375603,0.8738269,0.099754386,0.0014790869,0.00016446605,0.017241154],"study_design_scores_gemma":[0.0000084974345,0.00011191787,0.00086095626,0.000007185742,0.0000040481873,0.000041193372,0.000025020834,0.97061586,0.027902028,0.00026504416,0.00014987124,0.000008270454],"about_ca_topic_score_codex":0.0020234962,"about_ca_topic_score_gemma":0.0013231188,"teacher_disagreement_score":0.0020234962,"about_ca_system_score_codex":0.0005162238,"about_ca_system_score_gemma":0.00037028475,"threshold_uncertainty_score":0.0040234923},"labels":[],"label_agreement":null},{"id":"W2605562265","doi":"10.2166/ws.2017.044","title":"Integrated sensing technologies for detection and location of leaks in water distribution networks","year":2017,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Leak; Leak detection; Emissivity; Environmental science; Thermography; Computer science; Relative humidity; Ground-penetrating radar; Flexibility (engineering); Remote sensing; Infrared; Radar; Environmental engineering; Geology; Statistics; Meteorology; Telecommunications; Mathematics","score_opus":0.006174914645484893,"score_gpt":0.19735336860761732,"score_spread":0.19117845396213243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605562265","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":[0.50469524,0.0015404074,0.49046752,0.00011698824,0.00006344231,0.0000675876,0.00012750004,0.00083058374,0.0020907812],"genre_scores_gemma":[0.9468744,0.0003532378,0.05215929,0.000024011839,0.000008655357,0.000028574055,0.00003669636,0.00000926875,0.0005059041],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99951994,0.000110114015,0.000020944079,0.00009598949,0.0002211639,0.00003181383],"domain_scores_gemma":[0.9997173,0.00007440455,0.00008268887,0.000024287103,0.00009348741,0.00000782673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039625622,0.00035454772,0.00037626666,0.00061200256,0.00012828423,0.00040192524,0.00041797227,0.0003589441,0.00046680804],"category_scores_gemma":[0.0006200133,0.0002119274,0.00021679381,0.00062428426,0.00018104656,0.0006776978,0.0003950154,0.00027209337,0.000119597666],"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":[0.00036817335,0.00021805738,0.012048993,0.00038226967,0.000083421866,0.00013797241,0.00022777112,0.048240673,0.69966257,0.0010190873,0.00053810014,0.23707291],"study_design_scores_gemma":[0.000023817516,0.0010934904,0.016612662,0.000052850133,0.00011269599,0.00022310595,0.00029106563,0.62569624,0.3523253,0.0011607078,0.002353853,0.0000541996],"about_ca_topic_score_codex":0.0005651792,"about_ca_topic_score_gemma":0.00080228545,"teacher_disagreement_score":0.00061200256,"about_ca_system_score_codex":0.0002627431,"about_ca_system_score_gemma":0.0001532295,"threshold_uncertainty_score":0.00209558},"labels":[],"label_agreement":null},{"id":"W2605592202","doi":"10.14796/jwmm.r223-19","title":"Applications and Limitations of Single-Phase Models to the Description of the Rapid Filling Pipe Problem","year":2005,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Phase (matter); Computer science; Geology; Physics","score_opus":0.055564563199435354,"score_gpt":0.21101435540198088,"score_spread":0.1554497922025455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605592202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06635631,0.0019918568,0.91702145,0.0009945319,0.00020820127,0.00019061232,0.00034615185,0.0003309794,0.012559922],"genre_scores_gemma":[0.81926626,0.0035385948,0.16489005,0.0002896164,0.0003152938,0.0006670862,0.00040885876,0.00026028254,0.010364043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949074,0.00019612495,0.000030970714,0.000060576334,0.00016433976,0.000057223857],"domain_scores_gemma":[0.9987149,0.00077481696,0.00014930758,0.0001169807,0.0001816788,0.00006233928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008453876,0.0010131776,0.0015411262,0.0006657166,0.000508002,0.0011387202,0.0021920851,0.0022260626,0.0023189357],"category_scores_gemma":[0.002907657,0.0006676354,0.001629654,0.00064086675,0.0011442717,0.0018537518,0.0012766491,0.0015850292,0.00051613257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001371534,0.00003802336,0.000388432,0.000090748574,0.000015302114,0.000054783406,0.000046712474,0.9751496,0.0011994084,0.018637197,0.00023510482,0.0041309325],"study_design_scores_gemma":[0.000003598835,0.000008469334,0.000032040756,0.0000060281623,0.0000016608383,0.000010350278,0.000006304736,0.9958026,0.00011155627,0.0035224808,0.0004914926,0.000003415315],"about_ca_topic_score_codex":0.007527166,"about_ca_topic_score_gemma":0.0026817305,"teacher_disagreement_score":0.007527166,"about_ca_system_score_codex":0.00069394696,"about_ca_system_score_gemma":0.0015805179,"threshold_uncertainty_score":0.014966726},"labels":[],"label_agreement":null},{"id":"W2606254131","doi":"10.1007/s00477-017-1415-y","title":"Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments","year":2017,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Agence Nationale de la Recherche","keywords":"Vulnerability (computing); Computer science; Risk analysis (engineering); Warning system; Sensitivity (control systems); Adaptation (eye); Harm; Water supply; Vulnerability assessment; Environmental resource management; Environmental science; Computer security; Business; Psychological resilience; Environmental engineering; Engineering","score_opus":0.021017594878184814,"score_gpt":0.322289084351694,"score_spread":0.3012714894735092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606254131","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":[0.6197691,0.0004451967,0.37302935,0.00024229856,0.000021325803,0.0001794711,0.0007952423,0.00026820455,0.0052497583],"genre_scores_gemma":[0.98150486,0.00018780166,0.017697431,0.000010327319,0.000007561928,0.000055470668,0.00024581497,0.000011716344,0.00027899307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981729,0.0009404876,0.000091728594,0.00016231727,0.00047321364,0.00015933179],"domain_scores_gemma":[0.99413043,0.0024938602,0.0018380793,0.0006107633,0.0007355488,0.00019130151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031027985,0.00076803454,0.00040058378,0.003042246,0.00031996897,0.0014170955,0.00045532,0.00065404334,0.0005040805],"category_scores_gemma":[0.010616529,0.00027864584,0.0005080965,0.0021162159,0.0009889384,0.0021978144,0.0019716262,0.0005176947,0.00007731284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018208139,0.00015217882,0.14548568,0.0001712233,0.00026051962,0.0001595791,0.0002307307,0.7641532,0.007941113,0.016735498,0.0006715725,0.06385659],"study_design_scores_gemma":[0.0000119052165,0.00021738879,0.03604291,0.000057148856,0.00010716974,0.00016928981,0.00031772171,0.92051387,0.014100579,0.026765393,0.0016567201,0.000039825016],"about_ca_topic_score_codex":0.0030435624,"about_ca_topic_score_gemma":0.0028231216,"teacher_disagreement_score":0.0031027985,"about_ca_system_score_codex":0.0014160434,"about_ca_system_score_gemma":0.00097424944,"threshold_uncertainty_score":0.016409338},"labels":[],"label_agreement":null},{"id":"W2606456276","doi":"10.14796/jwmm.r227-22","title":"Identifying Potential Pipe Failures: Toronto Case Study","year":2007,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Forensic engineering; Engineering","score_opus":0.015571254208560304,"score_gpt":0.23589539665308817,"score_spread":0.22032414244452786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606456276","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":[0.9746695,0.00033407408,0.006609223,0.0013537913,0.0000200794,0.0003731498,0.002440455,0.00017455612,0.01402511],"genre_scores_gemma":[0.98980904,0.00034624359,0.004278779,0.00008600904,0.00001202553,0.000061781066,0.00096987677,0.000019608226,0.004416604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993635,0.0001927104,0.00003579016,0.00008197748,0.00015612744,0.00016994991],"domain_scores_gemma":[0.99585104,0.002509498,0.00028889006,0.00018112587,0.00066777563,0.0005017494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086253585,0.0007934663,0.00039085455,0.0013181826,0.002492525,0.00094425824,0.0014150861,0.0023994162,0.0049838447],"category_scores_gemma":[0.004477221,0.0004322514,0.0006127169,0.0023598997,0.0012430879,0.00072488375,0.00093195646,0.0007901701,0.0003952655],"study_design_candidate":"observational","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":[0.0018955035,0.0018505148,0.1846543,0.0006013606,0.00027176976,0.03927391,0.007156517,0.65268433,0.004819867,0.008679452,0.02892443,0.069188036],"study_design_scores_gemma":[0.0010570203,0.0018195278,0.17850399,0.00020053878,0.0003610311,0.0035923366,0.023784,0.7590002,0.005374013,0.0048439438,0.021187022,0.0002764692],"about_ca_topic_score_codex":0.75119835,"about_ca_topic_score_gemma":0.8638966,"teacher_disagreement_score":0.24880165,"about_ca_system_score_codex":0.011099066,"about_ca_system_score_gemma":0.005898345,"threshold_uncertainty_score":0.50053376},"labels":[],"label_agreement":null},{"id":"W2606694804","doi":"","title":"Predictive Analytics for Planning Inspections of Linear Water and Wastewater Infrastructure","year":2014,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Water Systems and 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":"","keywords":"Analytics; Predictive analytics; Wastewater; Water infrastructure; Environmental science; Business; Computer science; Environmental planning; Data science; Environmental engineering; Water supply","score_opus":0.007644749186193854,"score_gpt":0.19227079446131315,"score_spread":0.1846260452751193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606694804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17120712,0.00054167345,0.81537306,0.0009970565,0.000042720545,0.00033048674,0.0035165006,0.0048113107,0.003180005],"genre_scores_gemma":[0.887001,0.0003040733,0.108865745,0.00006352459,0.0000277248,0.00020002047,0.0028571798,0.000052756226,0.0006280119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993586,0.00017304423,0.00005183199,0.0001512516,0.00019625055,0.000069077396],"domain_scores_gemma":[0.9967462,0.0021836848,0.00042220886,0.00019666713,0.00037589652,0.000075354656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015194908,0.001112086,0.0006477763,0.0018125267,0.00043406695,0.001086662,0.0012749915,0.0005714178,0.0011274914],"category_scores_gemma":[0.0059444155,0.00046431329,0.00060683116,0.0019038129,0.00042662164,0.0012830452,0.0007825736,0.0010273013,0.00024982827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000628312,0.0000876486,0.0072373403,0.00006532308,0.000026987023,0.00006428459,0.000056692388,0.9469026,0.0006935635,0.0019120002,0.0012007578,0.04169009],"study_design_scores_gemma":[0.000001889597,0.000014434415,0.0007367595,0.0000064482274,0.0000036154518,0.000005730569,0.000029458086,0.9967141,0.00030286724,0.0019866119,0.00019508887,0.0000029441164],"about_ca_topic_score_codex":0.038389802,"about_ca_topic_score_gemma":0.03956634,"teacher_disagreement_score":0.038389802,"about_ca_system_score_codex":0.0015832175,"about_ca_system_score_gemma":0.0015391103,"threshold_uncertainty_score":0.07633269},"labels":[],"label_agreement":null},{"id":"W2608093724","doi":"10.14796/jwmm.r223-05","title":"GISRed 1.0, a GIS-based Tool for Water Distribution Models for Master Plans","year":2005,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Universitat Politècnica de València; Universitat de València","keywords":"Distribution (mathematics); Master plan; Computer science; Geography; Environmental planning; Environmental science; Mathematics","score_opus":0.023606581326336563,"score_gpt":0.20657447196678452,"score_spread":0.18296789064044797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608093724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008725311,0.00016849878,0.7485109,0.00024665933,0.000110399706,0.00027219456,0.033584427,0.17911313,0.029268514],"genre_scores_gemma":[0.13126603,0.0006593174,0.74155474,0.00024055348,0.00007648387,0.0009674236,0.06157547,0.03354231,0.030117696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976426,0.00003520329,0.00002002608,0.000044700704,0.00011122858,0.000024633324],"domain_scores_gemma":[0.9995171,0.00017388914,0.000045322213,0.00009430388,0.00014278325,0.00002668831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073686766,0.0010346486,0.0007074957,0.0015379288,0.00046845377,0.0011262574,0.0020797323,0.00076278835,0.043588594],"category_scores_gemma":[0.0022291096,0.0011287916,0.0008061132,0.0016799145,0.00030026518,0.0017423219,0.0010756447,0.001083288,0.009563829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014386527,0.000118189164,0.0032403604,0.0007369837,0.00012349342,0.0003575006,0.00043650554,0.42809042,0.005442592,0.036339354,0.3461967,0.17877401],"study_design_scores_gemma":[0.00011343143,0.000033290213,0.00089357694,0.000056245455,0.000036882695,0.0001930369,0.000081227525,0.6764922,0.0061913715,0.0148041,0.3010414,0.00006329627],"about_ca_topic_score_codex":0.00867638,"about_ca_topic_score_gemma":0.010808824,"teacher_disagreement_score":0.043588594,"about_ca_system_score_codex":0.00068925705,"about_ca_system_score_gemma":0.0014699433,"threshold_uncertainty_score":0.14581841},"labels":[],"label_agreement":null},{"id":"W2613090107","doi":"10.1016/j.proeng.2017.03.220","title":"Generation and Validation of Synthetic WDS Case Studies Using Graph Theory and Reliability Indexes","year":2017,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Reliability (semiconductor); Computer science; Similarity (geometry); Data mining; Graph; Reliability engineering; Synthetic data; Artificial intelligence; Engineering; Theoretical computer science","score_opus":0.025132453174630177,"score_gpt":0.24431936599729726,"score_spread":0.21918691282266708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613090107","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":[0.8166841,0.0001967588,0.17228548,0.00028303574,0.00009306867,0.0018277507,0.0030998345,0.0003642893,0.0051655904],"genre_scores_gemma":[0.8714839,0.00013282306,0.12419434,0.000036235804,0.000017814493,0.0010999496,0.0022469969,0.000050501912,0.000737532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99621516,0.0020701385,0.00030919557,0.00041278297,0.00079003675,0.00020269651],"domain_scores_gemma":[0.96030927,0.0295187,0.0014286945,0.0041915807,0.004174025,0.00037772986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062729814,0.001083882,0.0005726129,0.0034338995,0.00062923605,0.00089822564,0.0018680869,0.0013703561,0.0016165737],"category_scores_gemma":[0.02446362,0.00047908485,0.0012008185,0.002424086,0.0011993245,0.0011074097,0.0010628155,0.00096142536,0.0001615093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028746313,0.00086631195,0.013954504,0.00042125554,0.00013354121,0.0010276128,0.0005073007,0.93334985,0.004112181,0.009590781,0.0018159179,0.033933323],"study_design_scores_gemma":[0.00013246042,0.00058845227,0.005214962,0.00008573937,0.000057042173,0.0002611326,0.00076707744,0.9714953,0.011529476,0.006095912,0.0037328328,0.0000395181],"about_ca_topic_score_codex":0.0044367635,"about_ca_topic_score_gemma":0.0052276165,"teacher_disagreement_score":0.0062729814,"about_ca_system_score_codex":0.0019899986,"about_ca_system_score_gemma":0.0008334304,"threshold_uncertainty_score":0.03317511},"labels":[],"label_agreement":null},{"id":"W2615369482","doi":"10.5006/c2011-11306","title":"Reducing Pipeline Failures in a Complex System Using Statistical Methods","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Devon Energy (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Reliability engineering; Pipeline transport; Engineering; Mechanical engineering; Programming language","score_opus":0.08596117191133416,"score_gpt":0.3005509640469603,"score_spread":0.21458979213562612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615369482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07005894,0.00029461618,0.9267683,0.0005411601,0.00004027158,0.0002077929,0.00013563923,0.0005671366,0.0013860926],"genre_scores_gemma":[0.83662283,0.00029735436,0.16154434,0.00010764415,0.00005383601,0.00029321565,0.00024545004,0.00007873882,0.00075657957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9956287,0.002706057,0.0002348169,0.0003866908,0.0008608402,0.00018291308],"domain_scores_gemma":[0.9589685,0.033479363,0.0028946782,0.0011309828,0.0031996705,0.00032684588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010082255,0.0011731817,0.0016072546,0.003315371,0.00055690383,0.0014797098,0.0013160349,0.0007277386,0.0017525711],"category_scores_gemma":[0.030824456,0.000792359,0.001210999,0.0020860934,0.001214107,0.0018560418,0.0012231587,0.0011593766,0.00016934471],"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":[0.00003548829,0.00006194925,0.0018556758,0.000043559026,0.000066705994,0.000023655813,0.000021101729,0.98072904,0.00018787365,0.0030959432,0.00021263633,0.013666386],"study_design_scores_gemma":[0.000004506754,0.000036324418,0.00025380758,0.000003828723,0.000007356923,0.000003174692,0.000008527915,0.99711967,0.00009432537,0.00236418,0.00009979509,0.0000044561484],"about_ca_topic_score_codex":0.012943207,"about_ca_topic_score_gemma":0.008689444,"teacher_disagreement_score":0.012943207,"about_ca_system_score_codex":0.0019412893,"about_ca_system_score_gemma":0.00412295,"threshold_uncertainty_score":0.053320706},"labels":[],"label_agreement":null},{"id":"W2616236537","doi":"10.1061/(asce)hy.1943-7900.0001333","title":"Finite-Volume Solutions to the Water-Hammer Equations in Conservation Form Incorporating Dynamic Friction Using the Godunov Scheme","year":2017,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":15,"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":"Water hammer; Finite volume method; Conservation of mass; Conservation law; Mechanics; Dynamical friction; Momentum (technical analysis); Partial differential equation; Hammer; Control volume; Mathematics; Physics; Mathematical analysis; Engineering; Structural engineering","score_opus":0.02360679760377217,"score_gpt":0.22920774281137954,"score_spread":0.20560094520760736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616236537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026865644,0.0002257652,0.9648215,0.00014166193,0.00006390717,0.00007298219,0.000050087696,0.00019309505,0.007565387],"genre_scores_gemma":[0.61244965,0.00040314626,0.37434366,0.0000659336,0.000055576962,0.0002531413,0.00014785568,0.00014251807,0.012138567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998172,0.00005776346,0.0000110852225,0.000014023556,0.00007842947,0.00002148984],"domain_scores_gemma":[0.9996668,0.00015982016,0.000042491054,0.000028633935,0.00008472546,0.000017427523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063860446,0.00043594567,0.000811325,0.00040089153,0.00043722062,0.00086056395,0.0007194788,0.0007659347,0.0017372452],"category_scores_gemma":[0.0014581715,0.00026745402,0.0005366799,0.0003625835,0.00088805077,0.0006042056,0.00077999354,0.00079836656,0.00029012215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006190273,0.00005557547,0.00071560097,0.00016950199,0.000022421522,0.00018804597,0.00025630897,0.81720823,0.011535191,0.13336979,0.0010409391,0.03537646],"study_design_scores_gemma":[0.00001235354,0.000017736702,0.000072040624,0.0000089727555,0.0000023992113,0.000025310545,0.000014852196,0.9909094,0.0010981077,0.0062545175,0.0015786946,0.000005690624],"about_ca_topic_score_codex":0.0053018574,"about_ca_topic_score_gemma":0.004521548,"teacher_disagreement_score":0.0053018574,"about_ca_system_score_codex":0.0006437666,"about_ca_system_score_gemma":0.0014377703,"threshold_uncertainty_score":0.010542035},"labels":[],"label_agreement":null},{"id":"W2616500248","doi":"10.1061/9780784480595.012","title":"Modeling Chlorine Residual in a Ground Water Supply Tank for a Small Community in Cold Conditions","year":2017,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2017","topic":"Water Systems and 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":"PCL Construction (Canada)","funders":"Water Resources Research Institute, North Carolina State University","keywords":"Residual; Computational fluid dynamics; Environmental science; Chlorine; Storage tank; Flow conditions; Flow (mathematics); Marine engineering; Petroleum engineering; Engineering; Environmental engineering; Computer science; Mechanics; Waste management; Chemistry; Aerospace engineering","score_opus":0.022209498651789464,"score_gpt":0.20827455975966952,"score_spread":0.18606506110788007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616500248","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":[0.9787273,0.00010248758,0.013764348,0.0002187605,0.00002395856,0.00007097658,0.00068210246,0.00020519589,0.0062049804],"genre_scores_gemma":[0.99328727,0.00007525756,0.0027703934,0.000018627481,0.0000032549779,0.000043116783,0.00022375191,0.000025859377,0.0035526566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998926,0.000014286312,0.0000048651063,0.000025529676,0.000021389715,0.000041401192],"domain_scores_gemma":[0.9998499,0.00006649284,0.00001735496,0.000007742537,0.00003929578,0.000019035497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019541664,0.00046931257,0.00042906572,0.00042008737,0.0008149531,0.0009829258,0.0008471406,0.0015854344,0.003319341],"category_scores_gemma":[0.00041063206,0.00035725167,0.0007934187,0.00039404447,0.00040169156,0.0005305325,0.00059150055,0.000545464,0.00023819557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010751503,0.00008998239,0.0060695964,0.00007338051,0.00001611862,0.00034663058,0.00006019106,0.98111707,0.0083606355,0.0005115148,0.00033049315,0.002916802],"study_design_scores_gemma":[0.00001865751,0.000073754316,0.0014284938,0.000004937976,0.000012672107,0.00002138362,0.00011000161,0.9961551,0.0017355456,0.0001495972,0.0002796806,0.000010112173],"about_ca_topic_score_codex":0.1121221,"about_ca_topic_score_gemma":0.0718107,"teacher_disagreement_score":0.1121221,"about_ca_system_score_codex":0.0014564575,"about_ca_system_score_gemma":0.0021068128,"threshold_uncertainty_score":0.22293901},"labels":[],"label_agreement":null},{"id":"W2617068761","doi":"","title":"Development of a risk scoring method for water security characterization","year":2012,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Water Systems and 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":"","keywords":"Characterization (materials science); Computer science; Risk analysis (engineering); Business; Materials science; Nanotechnology","score_opus":0.01151800364828434,"score_gpt":0.20559240949767763,"score_spread":0.19407440584939328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617068761","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":[0.017297104,0.00003080359,0.97978055,0.000061376864,0.000012099889,0.00017660802,0.00023359086,0.0008809769,0.0015268207],"genre_scores_gemma":[0.23080884,0.000074972624,0.76578677,0.000023093446,0.000013555212,0.00034129657,0.00051300816,0.00010014101,0.0023383053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982547,0.00030333188,0.00018048286,0.00019213621,0.00097518816,0.00009409383],"domain_scores_gemma":[0.99566096,0.0015270307,0.00045804062,0.00022899488,0.0020048432,0.00012009961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024326746,0.0008588429,0.0007513124,0.0027507073,0.0005264297,0.0015913026,0.0009571658,0.00056290545,0.0027357007],"category_scores_gemma":[0.009120577,0.00037333622,0.0006468728,0.0016328675,0.00035264765,0.0011454746,0.0009899716,0.0008815499,0.00069921487],"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":[0.0001143962,0.00019187304,0.020956473,0.00017480107,0.00012654041,0.00022686033,0.00045290682,0.19080494,0.022252286,0.009686239,0.0052660736,0.7497467],"study_design_scores_gemma":[0.0000087218905,0.00006652984,0.005018985,0.0000233348,0.000018725845,0.00010341507,0.00012798476,0.9838178,0.004901724,0.0032489244,0.002626821,0.00003707417],"about_ca_topic_score_codex":0.012842124,"about_ca_topic_score_gemma":0.014109587,"teacher_disagreement_score":0.012842124,"about_ca_system_score_codex":0.0010113881,"about_ca_system_score_gemma":0.0020062525,"threshold_uncertainty_score":0.025534749},"labels":[],"label_agreement":null},{"id":"W2619368383","doi":"10.1007/978-3-319-55944-5_2","title":"An Overview of Transient Fault Detection Techniques","year":2017,"lang":"en","type":"book-chapter","venue":"Applied condition monitoring","topic":"Water Systems and Optimization","field":"Engineering","cited_by":59,"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":"Software deployment; Transient (computer programming); Commercialization; Computer science; Set (abstract data type); Domain (mathematical analysis); Fault detection and isolation; State (computer science); Data science; Systems engineering; Engineering; Artificial intelligence; Political science; Software engineering; Algorithm; Mathematics; Law","score_opus":0.031060501100634276,"score_gpt":0.26392264376471825,"score_spread":0.23286214266408398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619368383","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":[0.001701247,0.26712406,0.5496383,0.0011873879,0.0032667103,0.000140445,0.0007016453,0.0028533814,0.17338681],"genre_scores_gemma":[0.025269303,0.3236543,0.3487516,0.0013981754,0.00351162,0.00024109246,0.0022761037,0.0013067337,0.29359105],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995552,0.000035434714,0.000030103238,0.00010391656,0.00024680214,0.000028561304],"domain_scores_gemma":[0.9996277,0.00015791062,0.000018802122,0.0000427673,0.00013888226,0.000013996931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035381402,0.0012024015,0.00082906766,0.0025585883,0.0003565982,0.0020561903,0.0014829887,0.0012048595,0.021152839],"category_scores_gemma":[0.0008164471,0.00068506325,0.000755321,0.0038393382,0.00054356287,0.0028680926,0.0008439662,0.0019670106,0.016868431],"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":[0.000031147763,0.00007278978,0.000104860665,0.0014012065,0.000024629713,0.000061385574,0.00007818324,0.005702731,0.007694555,0.038094435,0.05780233,0.88893175],"study_design_scores_gemma":[0.000008051797,0.000081961116,0.000575018,0.0007822501,0.000030070516,0.0008507694,0.000052923326,0.013603344,0.0060070357,0.06012304,0.9178345,0.000051093455],"about_ca_topic_score_codex":0.00087740476,"about_ca_topic_score_gemma":0.0012727354,"teacher_disagreement_score":0.021152839,"about_ca_system_score_codex":0.00066343014,"about_ca_system_score_gemma":0.00059741276,"threshold_uncertainty_score":0.07076323},"labels":[],"label_agreement":null},{"id":"W2620139300","doi":"","title":"Vulnerability Assessment of Water Supply Systems: Status, Gaps and Opportunities","year":2015,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Water Systems and 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 Saskatchewan","funders":"","keywords":"Vulnerability (computing); Vulnerability assessment; Water supply; Business; Environmental resource management; Risk analysis (engineering); Environmental planning; Environmental science; Computer science; Computer security; Psychological resilience; Environmental engineering","score_opus":0.03835125487091976,"score_gpt":0.2488395485842132,"score_spread":0.21048829371329345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620139300","genre_codex":"empirical","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":[0.52812344,0.17295718,0.14304411,0.097321995,0.0008326481,0.0001294857,0.002192512,0.0004916158,0.05490703],"genre_scores_gemma":[0.95693433,0.030888876,0.009514002,0.00046052097,0.0003243051,0.000029464838,0.00032315878,0.000023644367,0.001501671],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99818414,0.0008645947,0.00010818106,0.000156881,0.00048730368,0.00019893558],"domain_scores_gemma":[0.99377054,0.0036819528,0.00071842363,0.00018145074,0.0012549298,0.000392762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042091855,0.0008055361,0.0010028344,0.0027040408,0.0005408169,0.003363886,0.0010165103,0.0011931151,0.0032451244],"category_scores_gemma":[0.0075287516,0.00021106229,0.00053821923,0.0025626924,0.0011392637,0.0050332732,0.0025268414,0.00083299854,0.00021115165],"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":[0.0002864094,0.0002632452,0.09459031,0.0024407671,0.00062298705,0.00033169522,0.0016528448,0.10159202,0.0013524578,0.051115137,0.015085057,0.730667],"study_design_scores_gemma":[0.000029726802,0.00070877076,0.09247952,0.005690488,0.00035515794,0.0007561443,0.01961926,0.30309993,0.0031450025,0.50677365,0.06708481,0.00025761867],"about_ca_topic_score_codex":0.0066952165,"about_ca_topic_score_gemma":0.008113001,"teacher_disagreement_score":0.0066952165,"about_ca_system_score_codex":0.0017263243,"about_ca_system_score_gemma":0.0025710207,"threshold_uncertainty_score":0.022260606},"labels":[],"label_agreement":null},{"id":"W2620837839","doi":"10.1007/s10661-017-5989-0","title":"Risk-based framework for optimizing residual chlorine in large water distribution systems","year":2017,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Systems and 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; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"King Abdulaziz City for Science and Technology","keywords":"Residual; Water quality; Environmental science; Water supply; Risk assessment; Environmental engineering; Computer science; Water resource management; Computer security","score_opus":0.010757367670883623,"score_gpt":0.24897859293818356,"score_spread":0.23822122526729994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620837839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02140149,0.00062055525,0.9686284,0.00069187017,0.000055909713,0.00011231645,0.00022207186,0.00030136344,0.007965963],"genre_scores_gemma":[0.8940394,0.00054000644,0.09379289,0.00024730383,0.0001025371,0.00032872777,0.0003027295,0.00020917966,0.010437282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858403,0.0005734086,0.00004393465,0.00017675734,0.0003942179,0.00022767698],"domain_scores_gemma":[0.99837446,0.0009471336,0.00014627016,0.00006666964,0.00038263277,0.00008285793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034178263,0.0017876074,0.0021132315,0.0013528578,0.0005738961,0.0025120405,0.002365676,0.002405067,0.0034807043],"category_scores_gemma":[0.005245513,0.0011983436,0.0012468862,0.0010914374,0.0011186219,0.0016822016,0.0017112633,0.0017121825,0.00026609984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074520713,0.0000092084,0.00005344043,0.0000129164,0.000010773404,0.000012522421,0.000005352313,0.99621326,0.0000778816,0.002463948,0.00013852208,0.0009946638],"study_design_scores_gemma":[0.0000033342262,0.0000048540664,0.000022846554,0.000002428715,0.0000040328796,0.000002324045,0.000002720312,0.9984126,0.000046062836,0.0014127641,0.00008403716,0.0000019740119],"about_ca_topic_score_codex":0.024605641,"about_ca_topic_score_gemma":0.016070286,"teacher_disagreement_score":0.024605641,"about_ca_system_score_codex":0.0032853028,"about_ca_system_score_gemma":0.0034211606,"threshold_uncertainty_score":0.048924804},"labels":[],"label_agreement":null},{"id":"W2621018622","doi":"10.1061/9780784480724.015","title":"Operational Safety of Dams and Hydropower Installations","year":2017,"lang":"en","type":"article","venue":"Geo-Risk 2017","topic":"Water Systems and 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":"Ontario Power Generation; BC Hydro (Canada)","funders":"","keywords":"Hydropower; Spillway; Abutment; Embankment dam; Engineering; Hydroelectricity; Civil engineering; Piping; Flood myth; Levee; Geotechnical engineering","score_opus":0.008661182610781826,"score_gpt":0.22398979031504213,"score_spread":0.2153286077042603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621018622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36107036,0.0012781912,0.5450598,0.0014103964,0.00016012345,0.00021889765,0.001994548,0.00053125643,0.088276416],"genre_scores_gemma":[0.9822688,0.0006940399,0.0122822635,0.000038122464,0.00002870834,0.00006774245,0.0005482418,0.00005520621,0.004016837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99807537,0.00074823666,0.00009787296,0.00023543615,0.00059906417,0.00024398885],"domain_scores_gemma":[0.9957703,0.0020555581,0.0008857546,0.00036631618,0.0007308721,0.00019118958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017991584,0.0005047113,0.00039951882,0.00079192914,0.00046983044,0.0015072763,0.0008270863,0.0005899972,0.004495374],"category_scores_gemma":[0.0087511735,0.00033759704,0.00042272726,0.0006606483,0.0012467591,0.0014817126,0.0012231872,0.00063881656,0.0005067828],"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":[0.000069905844,0.00002978022,0.00422281,0.000055221022,0.000023444038,0.00009053687,0.000103942686,0.90066856,0.0006300025,0.07462333,0.0010813948,0.018401138],"study_design_scores_gemma":[0.000014032223,0.000100076795,0.0028037606,0.0000548305,0.000015943975,0.00008600568,0.00027428643,0.91194093,0.0009504154,0.07541082,0.008326256,0.000022640523],"about_ca_topic_score_codex":0.009084689,"about_ca_topic_score_gemma":0.0041076504,"teacher_disagreement_score":0.009084689,"about_ca_system_score_codex":0.0014817781,"about_ca_system_score_gemma":0.0025266309,"threshold_uncertainty_score":0.018063664},"labels":[],"label_agreement":null},{"id":"W2621123808","doi":"10.1002/sia.6243","title":"A spectroscopic study of leak failures in cross‐linked polyethylene tubing used in domestic water supply systems","year":2017,"lang":"en","type":"article","venue":"Surface and Interface Analysis","topic":"Water Systems and 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":"Western University","funders":"","keywords":"Polyethylene; Materials science; Leak; Polymer; Cracking; Aqueous solution; Chemical engineering; Composite material; Forensic engineering; Environmental science; Chemistry; Environmental engineering; Organic chemistry; Engineering","score_opus":0.012006833063362643,"score_gpt":0.27197206675403546,"score_spread":0.2599652336906728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621123808","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":[0.99816763,0.00012212273,0.0013271838,0.000016588723,0.000003598514,0.000008816986,0.00003989413,0.000028966968,0.00028521754],"genre_scores_gemma":[0.999067,0.000051620165,0.0006443823,0.000004773728,0.000002301155,0.000003455352,0.000029039362,0.0000033640838,0.00019410129],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982053,0.000027971279,0.000008529708,0.000037854665,0.00008054618,0.000024485651],"domain_scores_gemma":[0.99958116,0.00007953046,0.00012875833,0.000036571855,0.0001446286,0.000029309955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027121487,0.0002186503,0.00027064193,0.00088270946,0.0004568288,0.00016343962,0.0002863232,0.0005127743,0.0006501947],"category_scores_gemma":[0.0005082422,0.00013781998,0.000191704,0.0005245101,0.000303013,0.0003825916,0.00022624919,0.0002742786,0.00013617676],"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":[0.00023359482,0.00016865204,0.026641428,0.00013782029,0.000020344649,0.00039917996,0.0004409271,0.0009924878,0.95529,0.00007250917,0.00016236039,0.015440675],"study_design_scores_gemma":[0.000017895487,0.0016983786,0.26711392,0.000018408993,0.000052248153,0.0013314647,0.0010979526,0.016952256,0.71005267,0.00013802598,0.0014831935,0.000043582502],"about_ca_topic_score_codex":0.0013495565,"about_ca_topic_score_gemma":0.0010554659,"teacher_disagreement_score":0.0013495565,"about_ca_system_score_codex":0.00020548714,"about_ca_system_score_gemma":0.0001136864,"threshold_uncertainty_score":0.002683401},"labels":[],"label_agreement":null},{"id":"W2621621247","doi":"10.3329/jme.v46i1.32520","title":"Non-Revenue Water (NRW) is a challenge for Global Water Supply System Management: A case study of Dhaka Water Supply System Management","year":2017,"lang":"en","type":"article","venue":"Journal of Mechanical Engineering","topic":"Water Systems and 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":"York University","funders":"","keywords":"Sewerage; Non-revenue water; Water supply; Business; Revenue; Water industry; Government (linguistics); Water resource management; Sanitation; Potable water; Environmental planning; Finance; Natural resource economics; Environmental engineering; Water resources; Water conservation; Engineering; Environmental science; Economics","score_opus":0.010757941803977725,"score_gpt":0.215936016081646,"score_spread":0.20517807427766827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621621247","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":[0.9865789,0.00017216383,0.0016054726,0.00083420513,0.000009137471,0.00009885743,0.00014538228,0.000014265444,0.010541622],"genre_scores_gemma":[0.9938233,0.00030646403,0.0013586679,0.000059341965,0.0000049734244,0.00003064805,0.00010671137,0.000007859654,0.0043019988],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993387,0.00027884985,0.00003435626,0.000069973634,0.00009911456,0.00017908277],"domain_scores_gemma":[0.99897075,0.00051880133,0.00013379449,0.00006447551,0.0001440585,0.00016801461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006542821,0.00036060362,0.00023479798,0.00068266975,0.002520888,0.0018781242,0.00070667523,0.0017263301,0.0030325078],"category_scores_gemma":[0.0010993844,0.00019945166,0.00037160874,0.002049724,0.0012725209,0.001324671,0.0012381644,0.0007727017,0.00030105028],"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":[0.00086532364,0.00430146,0.32535896,0.0018787028,0.00030383305,0.12776308,0.061845195,0.1834411,0.024296321,0.08972206,0.023721822,0.15650217],"study_design_scores_gemma":[0.00019021968,0.0017501296,0.20539802,0.0005111851,0.00024003771,0.008921871,0.4844394,0.13778836,0.012354175,0.013639796,0.13454463,0.00022212057],"about_ca_topic_score_codex":0.036416516,"about_ca_topic_score_gemma":0.09851647,"teacher_disagreement_score":0.036416516,"about_ca_system_score_codex":0.0027456225,"about_ca_system_score_gemma":0.0015942514,"threshold_uncertainty_score":0.07240915},"labels":[],"label_agreement":null},{"id":"W2624446421","doi":"","title":"Pipeline Network Optimization - Application of Genetic Algorithm Methodologies","year":2005,"lang":"en","type":"article","venue":"PSIG Annual Meeting","topic":"Water Systems and 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":"TransCanada (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Genetic algorithm; Algorithm; Mathematical optimization; Mathematics; Machine learning; Programming language","score_opus":0.01332804257481749,"score_gpt":0.23957649460900723,"score_spread":0.22624845203418975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624446421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013112918,0.0004485586,0.9781892,0.00027519205,0.00007167279,0.000077149,0.000046050733,0.00021893192,0.0075603696],"genre_scores_gemma":[0.3504766,0.00083108747,0.6422965,0.00013037282,0.0001036375,0.00038342,0.00012422241,0.00020983884,0.0054443656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996013,0.0002344391,0.000011406852,0.000037837453,0.00009052623,0.000024461726],"domain_scores_gemma":[0.9989293,0.00080792047,0.00005346556,0.00003850014,0.00015230237,0.000018450753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014412482,0.00089690724,0.0009589526,0.0010960653,0.00054657174,0.0008871821,0.0010179703,0.0017164438,0.002353675],"category_scores_gemma":[0.0039675315,0.00056609686,0.00071117515,0.0013139853,0.00058814837,0.0007924062,0.00070557604,0.0010444277,0.000332078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013105384,0.000021776217,0.00010664061,0.000024535646,0.000019029565,0.000013031616,0.000011707662,0.9746377,0.00017719371,0.0051476494,0.00034523528,0.01948232],"study_design_scores_gemma":[0.000005579164,0.0000059184254,0.000022841263,0.0000032963121,0.0000032714056,0.0000024779476,0.000002195725,0.99791294,0.0000846949,0.0017530491,0.00020244469,0.0000012608195],"about_ca_topic_score_codex":0.009779811,"about_ca_topic_score_gemma":0.008062555,"teacher_disagreement_score":0.009779811,"about_ca_system_score_codex":0.0009585123,"about_ca_system_score_gemma":0.0013199584,"threshold_uncertainty_score":0.019445777},"labels":[],"label_agreement":null},{"id":"W2624891243","doi":"10.1139/cjce-2016-0615","title":"Diagnosis of partial blockage in water pipeline using support vector machine with fault-characteristic peaks in frequency domain","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Frequency domain; Fault (geology); Classifier (UML); Pipeline (software); Time domain; Computer science; Pattern recognition (psychology); Engineering; Data mining; Artificial intelligence; Geology","score_opus":0.008018455956976617,"score_gpt":0.18477963056411167,"score_spread":0.17676117460713506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624891243","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":[0.5413017,0.0002526964,0.4566576,0.00011351693,0.000028161474,0.000040767343,0.0000877616,0.0009516766,0.00056617043],"genre_scores_gemma":[0.9818173,0.000030130555,0.017884748,0.0000064970036,0.0000035153344,0.000009976994,0.000042250504,0.0000034263683,0.00020218661],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997873,0.000033139764,0.000018719826,0.00005029988,0.000078292986,0.000032300388],"domain_scores_gemma":[0.9994143,0.00028032015,0.000097916905,0.000037798098,0.00014779909,0.000021763515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038688685,0.00042810294,0.00038321872,0.0005753628,0.0001878454,0.00031062975,0.00028846643,0.0004619994,0.00039491768],"category_scores_gemma":[0.0012831033,0.00017650312,0.0002719295,0.00029440536,0.00019484565,0.00041588725,0.00019107005,0.00033265597,0.00009528743],"study_design_candidate":"simulation_or_modeling","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":[0.0007541343,0.00022694672,0.036670435,0.00022212692,0.00009246824,0.00046708054,0.00024072018,0.35256726,0.112105645,0.0008055388,0.0009517068,0.49489594],"study_design_scores_gemma":[0.0000059418912,0.000121271274,0.0047682715,0.0000034971172,0.0000076177093,0.00004820702,0.000021803375,0.98746824,0.0072444547,0.00020979093,0.00009442528,0.0000064534793],"about_ca_topic_score_codex":0.0030869863,"about_ca_topic_score_gemma":0.0022721468,"teacher_disagreement_score":0.996913,"about_ca_system_score_codex":0.0002558571,"about_ca_system_score_gemma":0.00029173863,"threshold_uncertainty_score":0.0061380267},"labels":[],"label_agreement":null},{"id":"W2625718541","doi":"","title":"Statistical Modeling Techniques In the Design And Operation of Pipeline Systems","year":2002,"lang":"en","type":"article","venue":"PSIG Annual Meeting","topic":"Water Systems and 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":"TransCanada (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Statistical model; Artificial intelligence","score_opus":0.022468861720671474,"score_gpt":0.21566563244637463,"score_spread":0.19319677072570315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625718541","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":[0.0022168206,0.0005451052,0.9964528,0.00014473806,0.000026858856,0.000014688652,0.00001986427,0.00013993646,0.00043925797],"genre_scores_gemma":[0.4089445,0.004863638,0.58088267,0.00022304336,0.00041817004,0.00053971296,0.0003200963,0.00039433202,0.0034138327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970624,0.0017892459,0.00013327721,0.00018501341,0.00072169106,0.00010835814],"domain_scores_gemma":[0.99460876,0.00425345,0.00034563077,0.00020022668,0.0005365892,0.000055240907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004222592,0.001088893,0.0012985682,0.0011385558,0.0005296049,0.0011570774,0.0012393161,0.0008688705,0.0012415053],"category_scores_gemma":[0.00991792,0.0014379689,0.0012838641,0.0015480261,0.0008962648,0.0015251015,0.00085597206,0.0019361537,0.0004116489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004340644,0.00003036535,0.0003259683,0.000091188675,0.00007710844,0.000020281403,0.00003575498,0.91194606,0.0007617926,0.04189428,0.00068312173,0.044090603],"study_design_scores_gemma":[0.000010855875,0.000028063392,0.00012087442,0.000010515204,0.000015205058,0.000008102762,0.000005636011,0.9755701,0.00039347782,0.022550894,0.001277912,0.000008300628],"about_ca_topic_score_codex":0.0051792017,"about_ca_topic_score_gemma":0.0052587613,"teacher_disagreement_score":0.0051792017,"about_ca_system_score_codex":0.0010108491,"about_ca_system_score_gemma":0.0026586433,"threshold_uncertainty_score":0.022331417},"labels":[],"label_agreement":null},{"id":"W2641220268","doi":"10.14796/jwmm.r223-02","title":"A Genetic Algorithm for the Minimum Cost Design of a Stormwater System","year":2005,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"University of Guelph","funders":"","keywords":"Genetic algorithm; Minification; Stormwater; Stormwater management; Computer science; Mathematical optimization; Algorithm; Mathematics; Machine learning; Surface runoff; Biology; Ecology","score_opus":0.019965534360242593,"score_gpt":0.19910205837112488,"score_spread":0.1791365240108823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2641220268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004509862,0.00010089341,0.9928812,0.00006305282,0.000015357631,0.000090358706,0.00003353875,0.00016567287,0.0021401923],"genre_scores_gemma":[0.16769825,0.0002585977,0.82774675,0.000089463116,0.000023778068,0.0010331973,0.00017954234,0.000108636326,0.0028617824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994424,0.00021232961,0.0000195804,0.000061414044,0.00020731332,0.00005701899],"domain_scores_gemma":[0.99940276,0.00038242407,0.000053114985,0.00001973144,0.00012310622,0.000018904175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014172703,0.0013110065,0.0011623817,0.0012295487,0.00061620586,0.00077720085,0.0011067874,0.0014797604,0.002944061],"category_scores_gemma":[0.002416421,0.00069276115,0.00083372754,0.0010482421,0.00076950557,0.0005719259,0.0007105695,0.0011030192,0.00043301703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010710012,0.0000130287835,0.00009469341,0.000023913834,0.000014558853,0.00001642729,0.000016065433,0.9777025,0.00037821956,0.004929148,0.00030458427,0.016496219],"study_design_scores_gemma":[0.000018517047,0.000032337754,0.000049922124,0.0000076161255,0.000008149369,0.000008199784,0.000004840702,0.9960884,0.00019784903,0.002848059,0.00073212,0.000003991373],"about_ca_topic_score_codex":0.007562797,"about_ca_topic_score_gemma":0.006659165,"teacher_disagreement_score":0.007562797,"about_ca_system_score_codex":0.0012016286,"about_ca_system_score_gemma":0.0022715912,"threshold_uncertainty_score":0.015037537},"labels":[],"label_agreement":null},{"id":"W2736915452","doi":"10.1115/iogpc2017-2452","title":"HSE and City Gas Distribution","year":2017,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Statutory law; Agency (philosophy); Business; Damages; Computer security; Engineering; Computer science; Political science; Law","score_opus":0.009859210673806124,"score_gpt":0.18972033027130314,"score_spread":0.179861119597497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736915452","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":[0.027459344,0.01247661,0.014873493,0.042385086,0.0015105634,0.000074167125,0.0007138524,0.00039210246,0.9001148],"genre_scores_gemma":[0.5305661,0.014764399,0.0029015974,0.0058741807,0.0006190827,0.000041582574,0.00045780258,0.00019309082,0.44458216],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990402,0.0001738869,0.000028863316,0.00015631005,0.00037632784,0.00022446354],"domain_scores_gemma":[0.99936956,0.00011388856,0.00006882925,0.00009233422,0.00026571986,0.00008974707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005532849,0.00025168015,0.00015627847,0.00082755106,0.0012683977,0.005039108,0.0005955892,0.0016850107,0.04324391],"category_scores_gemma":[0.001546217,0.00016119899,0.00029068752,0.0021204634,0.002853796,0.0044553583,0.0027066052,0.0024243146,0.005084324],"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":[0.00004895801,0.000034752702,0.0020828429,0.00015169255,0.000010270509,0.00035670624,0.00040268767,0.0021589168,0.0005066164,0.8691803,0.056050587,0.06901565],"study_design_scores_gemma":[0.0000075965563,0.000029617944,0.0025461481,0.00012732745,0.000004898325,0.0003722191,0.0011685316,0.001231917,0.001031535,0.06649872,0.926959,0.000022433473],"about_ca_topic_score_codex":0.022930771,"about_ca_topic_score_gemma":0.014048408,"teacher_disagreement_score":0.04324391,"about_ca_system_score_codex":0.0040515396,"about_ca_system_score_gemma":0.0026126949,"threshold_uncertainty_score":0.1446653},"labels":[],"label_agreement":null},{"id":"W2739813992","doi":"10.1061/9780784480885.007","title":"Water Research Foundation Project #4618: Status and Application of Cathodic Protection in the Water Utility Industry","year":2017,"lang":"en","type":"article","venue":"Pipelines 2017","topic":"Water Systems and 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":"Cathodic protection; Galvanic anode; Pipeline transport; Corrosion; Environmental science; Engineering; Waste management; Forensic engineering; Environmental engineering; Anode; Materials science","score_opus":0.09951096447370576,"score_gpt":0.3460387535579827,"score_spread":0.24652778908427692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739813992","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":[0.40373638,0.049609117,0.03016713,0.06823381,0.0008903879,0.0040265247,0.011210674,0.0015711912,0.43055472],"genre_scores_gemma":[0.6428831,0.08462513,0.08684458,0.0038528217,0.00030323034,0.0013440868,0.017192949,0.00060269533,0.16235134],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9918779,0.0013848819,0.0002519389,0.0007290017,0.0043644602,0.0013917056],"domain_scores_gemma":[0.9896383,0.00085025345,0.0007515861,0.000440561,0.006962051,0.0013572435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010201517,0.0006291632,0.00041063206,0.0022727705,0.0016976715,0.0031590895,0.0019568913,0.0015703636,0.008056338],"category_scores_gemma":[0.0044957795,0.0003931181,0.00047532344,0.003550194,0.0014151218,0.0023243343,0.001876882,0.0012585818,0.001968983],"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":[0.00056299753,0.0027627125,0.015554651,0.00228906,0.00005366082,0.0004539944,0.0017471982,0.0026972066,0.03157644,0.029659152,0.07660775,0.8360352],"study_design_scores_gemma":[0.0001131038,0.0033066815,0.034848288,0.0010766089,0.000056620574,0.0004192361,0.0023094178,0.0017766979,0.041351423,0.002034776,0.9126277,0.000079593345],"about_ca_topic_score_codex":0.10327218,"about_ca_topic_score_gemma":0.08575815,"teacher_disagreement_score":0.10327218,"about_ca_system_score_codex":0.0072713783,"about_ca_system_score_gemma":0.03256238,"threshold_uncertainty_score":0.20534211},"labels":[],"label_agreement":null},{"id":"W2741606243","doi":"10.1139/cjce-2016-0592","title":"Defect based deterioration model for sewer pipelines using Bayesian belief networks","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Microsoft","keywords":"Bayesian network; Dynamic Bayesian network; Conditional probability; Pipeline transport; Pipeline (software); Logistic regression; Probabilistic logic; Mean squared error; Statistics; Bayesian probability; Computer science; Deep belief network; Dimension (graph theory); Data mining; Mathematics; Artificial intelligence; Artificial neural network; Engineering","score_opus":0.016980770247584875,"score_gpt":0.20210360815485923,"score_spread":0.18512283790727435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741606243","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":[0.11507364,0.00055705005,0.8781621,0.0005556785,0.0000613567,0.00012225557,0.0012467112,0.0006901464,0.0035310686],"genre_scores_gemma":[0.953815,0.0003463694,0.039361857,0.00007976172,0.000040253937,0.00021641451,0.0011184439,0.00004157826,0.004980302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992982,0.00014830515,0.00004447241,0.00025400938,0.00015311547,0.00010198746],"domain_scores_gemma":[0.9982938,0.0009573817,0.00025838902,0.00005294995,0.00037923435,0.000058234225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017299477,0.0010889792,0.0011770362,0.0015427573,0.00041969132,0.0012685735,0.0026406355,0.00149971,0.0021259584],"category_scores_gemma":[0.0038993964,0.00085173314,0.0011717946,0.0010894212,0.00068612926,0.0017783864,0.0007715123,0.0017939225,0.0005171416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003840687,0.000024697107,0.001715998,0.00001903722,0.000023124927,0.000031020034,0.000027223461,0.98929393,0.00022177534,0.0018407822,0.0002979777,0.0064660865],"study_design_scores_gemma":[0.000002155236,0.000006270064,0.00025567933,0.000002188675,0.0000049375594,0.0000046591827,0.0000026500884,0.9987772,0.000040811236,0.00084478536,0.00005566527,0.000002898976],"about_ca_topic_score_codex":0.041039824,"about_ca_topic_score_gemma":0.031010881,"teacher_disagreement_score":0.041039824,"about_ca_system_score_codex":0.00203076,"about_ca_system_score_gemma":0.0011472185,"threshold_uncertainty_score":0.08160192},"labels":[],"label_agreement":null},{"id":"W2742836947","doi":"10.1061/9780784480885.043","title":"Seymour-Capilano Filtration Project: Transient Analysis Field Testing","year":2017,"lang":"en","type":"article","venue":"Pipelines 2017","topic":"Water Systems and 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":"Stantec (Canada)","funders":"","keywords":"Transient (computer programming); Environmental science; Plan (archaeology); Water hammer; Field (mathematics); Raw data; Engineering; Computer science; Geography; Archaeology; Mechanical engineering","score_opus":0.047334081526395705,"score_gpt":0.2676774990990443,"score_spread":0.2203434175726486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742836947","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":[0.8393517,0.0004930949,0.123414494,0.0006946249,0.00009167999,0.0012846366,0.0037090639,0.0031011219,0.027859569],"genre_scores_gemma":[0.9580541,0.000325297,0.030947592,0.000062636864,0.000009409541,0.0003908765,0.0022702776,0.00015216702,0.007787538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991953,0.00012137581,0.00002900567,0.000112605536,0.00045543653,0.00008621226],"domain_scores_gemma":[0.99885833,0.00025506053,0.000052019324,0.00008904835,0.0006775873,0.000067942594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016113259,0.0005946128,0.0006846984,0.0006380347,0.00096855714,0.001135409,0.00093027356,0.0008676697,0.0022622503],"category_scores_gemma":[0.0017149346,0.00026732424,0.00040914057,0.0009860514,0.00045429514,0.0006877115,0.0003989432,0.0007257783,0.00048574558],"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":[0.0015763262,0.0039991103,0.02519719,0.0010081283,0.00015228911,0.0010370049,0.0009754765,0.5229032,0.11832055,0.00716545,0.033332095,0.2843332],"study_design_scores_gemma":[0.00035731104,0.005317401,0.039788596,0.000114487906,0.00009861402,0.00023203125,0.00092828344,0.809206,0.11053806,0.0017840242,0.031488556,0.00014660959],"about_ca_topic_score_codex":0.10438839,"about_ca_topic_score_gemma":0.10416595,"teacher_disagreement_score":0.10438839,"about_ca_system_score_codex":0.00300534,"about_ca_system_score_gemma":0.004144053,"threshold_uncertainty_score":0.20756155},"labels":[],"label_agreement":null},{"id":"W2743192450","doi":"10.1061/9780784480892.034","title":"Performance Modeling of Wastewater Collection Networks Using Multi-Proactive Renewal Analysis","year":2017,"lang":"en","type":"article","venue":"Pipelines 2017","topic":"Water Systems and 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":"Research Canada; University of Waterloo","funders":"","keywords":"Sanitary sewer; Wastewater; Asset management; Work (physics); Computer science; Asset (computer security); Data collection; Plan (archaeology); Environmental economics; Business; Environmental science; Engineering; Computer security; Finance; Environmental engineering; Economics","score_opus":0.050676754868131,"score_gpt":0.2562902813648808,"score_spread":0.2056135264967498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743192450","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":[0.6427021,0.00066567946,0.30995843,0.0011046341,0.00006799624,0.0002009048,0.0010116933,0.0006508807,0.043637697],"genre_scores_gemma":[0.98934424,0.00015497694,0.0044314847,0.000039182283,0.00000867042,0.00007650149,0.0001191448,0.000032030348,0.00579363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999577,0.00011697242,0.000016641272,0.0000825579,0.00006666966,0.00014008311],"domain_scores_gemma":[0.9992817,0.00040369062,0.0001244365,0.000024003486,0.00011437387,0.000051755396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008501679,0.0010042225,0.0010184519,0.00084897625,0.0007553516,0.0017983922,0.0012564941,0.0017106282,0.002577597],"category_scores_gemma":[0.00177775,0.0006731536,0.0012193525,0.00070920226,0.00083794235,0.0010538745,0.0010515547,0.0008153679,0.0002808004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008788772,0.000007851771,0.00024248153,0.0000044472476,0.0000037904001,0.000015347752,0.000008474267,0.9986181,0.00015591981,0.00040638447,0.00003663344,0.00049169053],"study_design_scores_gemma":[0.0000013108049,0.000005674205,0.00009218531,9.733886e-7,0.0000020808902,0.0000016204026,0.0000064924493,0.9996904,0.000040324394,0.00012104029,0.00003612312,0.0000018072777],"about_ca_topic_score_codex":0.095760375,"about_ca_topic_score_gemma":0.03810024,"teacher_disagreement_score":0.095760375,"about_ca_system_score_codex":0.003329003,"about_ca_system_score_gemma":0.0022318857,"threshold_uncertainty_score":0.19040596},"labels":[],"label_agreement":null},{"id":"W2743707033","doi":"10.1061/9780784480885.013","title":"Collection and Compilation of Water Pipeline Field Performance Data","year":2017,"lang":"en","type":"article","venue":"Pipelines 2017","topic":"Water Systems and 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":"Computer science; Pipeline (software); Field (mathematics); Database; Programming language","score_opus":0.041328090637745646,"score_gpt":0.2524838837480644,"score_spread":0.21115579311031876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743707033","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13978043,0.00025957264,0.17319396,0.0006335168,0.00018970665,0.00866292,0.62793654,0.009864215,0.039479163],"genre_scores_gemma":[0.18830425,0.00058042957,0.20442083,0.00021987142,0.00010647732,0.009278579,0.5864724,0.0014979335,0.0091192],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99440163,0.0006459201,0.00083226076,0.00079579966,0.003057618,0.0002667559],"domain_scores_gemma":[0.9739498,0.0025438536,0.0022026394,0.004314022,0.016314942,0.0006747706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045473613,0.000872869,0.00090172805,0.009116467,0.00086385227,0.001833861,0.001686525,0.00053921796,0.0062508755],"category_scores_gemma":[0.013507473,0.0006220144,0.0005976571,0.009468014,0.0005221932,0.002269242,0.0013806295,0.0010329797,0.0044722822],"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":[0.00040853917,0.0017532678,0.1720472,0.0019379912,0.00016352237,0.0006774209,0.0027607721,0.042320218,0.022586985,0.0059651965,0.18628766,0.5630913],"study_design_scores_gemma":[0.00019639565,0.000989236,0.47425407,0.00076669187,0.00015045982,0.00039124742,0.004797695,0.04225756,0.039784905,0.0055904496,0.4304598,0.00036153774],"about_ca_topic_score_codex":0.025236422,"about_ca_topic_score_gemma":0.021992646,"teacher_disagreement_score":0.025236422,"about_ca_system_score_codex":0.001979565,"about_ca_system_score_gemma":0.0064931186,"threshold_uncertainty_score":0.050179064},"labels":[],"label_agreement":null},{"id":"W2746584496","doi":"10.1061/(asce)wr.1943-5452.0000857","title":"Energy Metrics to Evaluate the Energy Use and Performance of Water Main Assets","year":2017,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Queen's University","funders":"Engineering and Physical Sciences Research Council","keywords":"Leakage (economics); Energy (signal processing); Efficient energy use; Electricity; Mains electricity; Hydraulic head; Energy accounting; Benchmark (surveying); Environmental science; Computer science; Reliability engineering; Engineering; Geology; Geotechnical engineering; Electrical engineering","score_opus":0.020190012483162812,"score_gpt":0.21986552315599966,"score_spread":0.19967551067283684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746584496","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":[0.59566045,0.0020757513,0.36984274,0.00037791568,0.0001664007,0.00050254073,0.008052988,0.0018791377,0.021442056],"genre_scores_gemma":[0.9641041,0.000312027,0.03114608,0.000040527146,0.000022799715,0.00014410276,0.0026474935,0.00010908945,0.001473767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987087,0.00030739192,0.00012982878,0.00013577992,0.00063970423,0.000078671044],"domain_scores_gemma":[0.9964709,0.0018160853,0.00049661344,0.0003943831,0.0007520192,0.00006988011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014590451,0.0012757812,0.0007605648,0.0029947488,0.00031218037,0.0010127235,0.00047955292,0.0005781711,0.0020598841],"category_scores_gemma":[0.006458589,0.0001922572,0.0004808852,0.0031346572,0.0004112594,0.001883704,0.0006382485,0.00042985336,0.0003832947],"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":[0.0003491386,0.00046126443,0.09973238,0.0006323811,0.00050104916,0.00016852547,0.00019100883,0.6796402,0.02485514,0.008936274,0.005202488,0.17933013],"study_design_scores_gemma":[0.00003384569,0.0013067169,0.10045348,0.000072589944,0.00010169673,0.0003112479,0.00044071677,0.845971,0.030006368,0.007134432,0.014047433,0.00012051405],"about_ca_topic_score_codex":0.0033073886,"about_ca_topic_score_gemma":0.0047848383,"teacher_disagreement_score":0.0033073886,"about_ca_system_score_codex":0.00076790835,"about_ca_system_score_gemma":0.0003667437,"threshold_uncertainty_score":0.007716298},"labels":[],"label_agreement":null},{"id":"W2749859467","doi":"10.1109/jsen.2017.2740220","title":"Pipeline Leak Detection by Using Time-Domain Statistical Features","year":2017,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Leak; Pipeline transport; Pipeline (software); Computer science; Waveform; Leak detection; Feature extraction; Frequency domain; Time domain; SIGNAL (programming language); Feature (linguistics); Field (mathematics); Pattern recognition (psychology); Engineering; Data mining; Real-time computing; Artificial intelligence; Computer vision; Radar; Mathematics","score_opus":0.009773542201093725,"score_gpt":0.2282558580049333,"score_spread":0.2184823158038396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749859467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06253225,0.00016668887,0.9355975,0.00005844074,0.000021063943,0.000029583496,0.00012931736,0.00094895554,0.00051625393],"genre_scores_gemma":[0.828845,0.00031234487,0.16887534,0.000035357196,0.00003387025,0.00006004364,0.00053896505,0.00009954859,0.0011995422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971944,0.00003090359,0.00001962049,0.00006442582,0.0001392604,0.000026211184],"domain_scores_gemma":[0.9994648,0.0001331854,0.00015364247,0.00004119082,0.00019138938,0.000015753645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026638288,0.0006618942,0.0004081647,0.0012655449,0.00014534513,0.00044286138,0.00033933026,0.00030502974,0.0005226954],"category_scores_gemma":[0.0014126907,0.00020709631,0.0004690809,0.0010697652,0.000224654,0.000990674,0.00034695907,0.00037708643,0.00027425875],"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":[0.00039358268,0.00019785824,0.008712753,0.00019288246,0.00009167558,0.00021172907,0.00012251145,0.13722786,0.17218238,0.0022240763,0.001997724,0.67644495],"study_design_scores_gemma":[0.0000112340385,0.00015977092,0.007920708,0.000008280332,0.00004103734,0.0001305392,0.000042413074,0.9535684,0.034980502,0.001159223,0.0019437027,0.000034066732],"about_ca_topic_score_codex":0.0019522188,"about_ca_topic_score_gemma":0.0015029465,"teacher_disagreement_score":0.0019522188,"about_ca_system_score_codex":0.00023628694,"about_ca_system_score_gemma":0.00046599287,"threshold_uncertainty_score":0.0038817525},"labels":[],"label_agreement":null},{"id":"W2750428330","doi":"10.3390/en10081226","title":"A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines","year":2017,"lang":"en","type":"article","venue":"Energies","topic":"Water Systems and 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":"Pipeline transport; Leak; Pipeline (software); Leak detection; Computer science; Sensitivity (control systems); Reliability engineering; Transient (computer programming); Engineering; Real-time computing; Mechanical engineering","score_opus":0.011918952284187873,"score_gpt":0.2074552469541271,"score_spread":0.19553629466993921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750428330","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":[0.82846165,0.00031688606,0.1658674,0.00025103815,0.00002983579,0.0001798572,0.00032112497,0.0008356614,0.003736623],"genre_scores_gemma":[0.9973013,0.000029011906,0.0023346508,0.00001099944,0.0000012560143,0.000021059843,0.000042769865,0.00001112983,0.00024787468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989858,0.0004131395,0.00005209428,0.00015198757,0.00026108394,0.00013588666],"domain_scores_gemma":[0.9947643,0.0039048865,0.00031655564,0.00034511235,0.0006283188,0.000040898183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001958669,0.0007587712,0.0007022584,0.0008666852,0.00047678075,0.00085321884,0.000502846,0.00093099574,0.0012531196],"category_scores_gemma":[0.0076041743,0.0003958383,0.0008835134,0.00035003974,0.0005449218,0.00079625216,0.00072816387,0.000676426,0.000101673744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024811042,0.000050245424,0.0017617429,0.00008593852,0.000041501822,0.000076808334,0.0000465636,0.9859049,0.0065725166,0.0004890735,0.00010316498,0.0046195933],"study_design_scores_gemma":[0.00000842981,0.0001513394,0.0014368866,0.000006703541,0.00002506602,0.000021258835,0.00001904249,0.991703,0.0063219545,0.00018903466,0.00010335234,0.000014018519],"about_ca_topic_score_codex":0.018511187,"about_ca_topic_score_gemma":0.005075323,"teacher_disagreement_score":0.018511187,"about_ca_system_score_codex":0.0015737414,"about_ca_system_score_gemma":0.0007799745,"threshold_uncertainty_score":0.03680688},"labels":[],"label_agreement":null},{"id":"W2750971168","doi":"10.1139/cjce-2017-0011","title":"Organizational competency in urban water infrastructure asset management","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"Business; Asset management; Work (physics); Context (archaeology); Asset (computer security); Process management; Knowledge management; Benchmark (surveying); Computer science; Finance; Engineering","score_opus":0.00317888857465362,"score_gpt":0.1504714595616306,"score_spread":0.14729257098697698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750971168","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":[0.7889594,0.0007456556,0.062058985,0.006744958,0.000040569867,0.00027622312,0.00004714902,0.000044077333,0.141083],"genre_scores_gemma":[0.9942478,0.00009605089,0.004655956,0.00007900591,0.000004867109,0.00003854817,0.000013960009,0.0000029651173,0.0008608719],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99386555,0.0033045055,0.0002773372,0.00025937372,0.0011587909,0.001134419],"domain_scores_gemma":[0.99075365,0.0041194856,0.0015879775,0.0005105524,0.0016446534,0.0013837324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061741974,0.00022720014,0.00018224891,0.0021351948,0.0021356652,0.002656539,0.0007338927,0.0006831354,0.0015231314],"category_scores_gemma":[0.015517212,0.00020346363,0.00019624103,0.0010279753,0.0044211918,0.0023925344,0.0036324335,0.0009091677,0.00015814461],"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":[0.000043551965,0.00063018524,0.14834443,0.00043015313,0.000054116696,0.0005745163,0.047547657,0.017127791,0.0014035023,0.58920985,0.0026998934,0.19193444],"study_design_scores_gemma":[0.00006353243,0.0005046342,0.2661651,0.0014796075,0.000048785365,0.00072823936,0.15361997,0.048385162,0.0041462895,0.44556293,0.07913493,0.00016070265],"about_ca_topic_score_codex":0.01755085,"about_ca_topic_score_gemma":0.018982848,"teacher_disagreement_score":0.01755085,"about_ca_system_score_codex":0.004902688,"about_ca_system_score_gemma":0.01087912,"threshold_uncertainty_score":0.035571694},"labels":[],"label_agreement":null},{"id":"W2754989654","doi":"10.1139/cjce-2017-0084","title":"Copula parameter estimation using Bayesian inference for pipe data analysis","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Inference; Bayesian probability; Mains electricity; Data mining; Engineering; Computer science; Statistics; Econometrics; Mathematics; Artificial intelligence","score_opus":0.03424363131787964,"score_gpt":0.252433339039648,"score_spread":0.21818970772176835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754989654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021804937,0.0002209632,0.99695337,0.00005927631,0.000010789718,0.000033908113,0.00011454011,0.00017412593,0.0002525665],"genre_scores_gemma":[0.26073968,0.002184297,0.7285478,0.00025208073,0.00025462883,0.00086356024,0.0029768276,0.0005900772,0.0035910406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960861,0.0022199436,0.0002050986,0.00063584466,0.00062625756,0.00022674259],"domain_scores_gemma":[0.9748379,0.021053312,0.0012010251,0.0012835244,0.0013689602,0.00025525028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010402579,0.0018516912,0.0028412044,0.003934923,0.0008613183,0.0018804015,0.003009372,0.0018781009,0.0044071875],"category_scores_gemma":[0.04630382,0.002220765,0.0029028184,0.0033137295,0.0013918424,0.0031059682,0.0022716043,0.004175755,0.001463995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095917574,0.000117255004,0.0037612007,0.00021444322,0.00037004848,0.00016274168,0.00015171277,0.8665085,0.0009887865,0.046540003,0.002860405,0.078229025],"study_design_scores_gemma":[0.000007023848,0.000010921577,0.00040232512,0.000020222884,0.000016567163,0.000022047825,0.000010589897,0.97920996,0.000220117,0.01944312,0.00062030635,0.000016816282],"about_ca_topic_score_codex":0.015602706,"about_ca_topic_score_gemma":0.01264758,"teacher_disagreement_score":0.015602706,"about_ca_system_score_codex":0.0016225057,"about_ca_system_score_gemma":0.0024362437,"threshold_uncertainty_score":0.05501473},"labels":[],"label_agreement":null},{"id":"W2756314504","doi":"","title":"Optimisation du nombre de sites d'entreposage de poteaux chez Hydro-Québec","year":2005,"lang":"fr","type":"article","venue":"Les Cahiers du GERAD","topic":"Water Systems and 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":"Geography","score_opus":0.004569943567113249,"score_gpt":0.17114377908917977,"score_spread":0.16657383552206653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756314504","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":[0.99362767,0.00014063454,0.0042377855,0.000067293586,0.000009511522,0.00003309094,0.0004216866,0.00007358971,0.0013887864],"genre_scores_gemma":[0.9908303,0.000061083796,0.004127463,0.000020200454,0.0000028970896,0.000029017447,0.00054653746,0.000022378894,0.004360083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997639,0.000045264627,0.0000071044483,0.00006814117,0.000038688835,0.00007684763],"domain_scores_gemma":[0.99924517,0.00038519016,0.000061451945,0.000027505113,0.00016824502,0.00011247956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006183037,0.00080682867,0.0008556336,0.0008477257,0.0008232512,0.0017796592,0.00071056606,0.001062695,0.003139669],"category_scores_gemma":[0.0012396578,0.00044320893,0.00064062036,0.0007143397,0.0005400227,0.0005165844,0.00035459673,0.0005493975,0.0002485472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068682525,0.0001712721,0.05001163,0.00011180946,0.00018058,0.00022564945,0.00014499138,0.91062456,0.01212218,0.0005765497,0.0011340963,0.02400976],"study_design_scores_gemma":[0.00014616168,0.0004137065,0.15018643,0.000028077895,0.00009425547,0.000048992497,0.0006636246,0.8399991,0.00629051,0.0003033949,0.0017717174,0.000054029348],"about_ca_topic_score_codex":0.6536517,"about_ca_topic_score_gemma":0.7113967,"teacher_disagreement_score":0.6536517,"about_ca_system_score_codex":0.0043166713,"about_ca_system_score_gemma":0.0024312688,"threshold_uncertainty_score":0.696776},"labels":[],"label_agreement":null},{"id":"W2757736315","doi":"10.11159/icesdp18.1","title":"New Approaches in Urban Water Systems Engineering","year":2018,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Water Systems and 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":"Environmental science; Computer science; Environmental planning","score_opus":0.007025200260587029,"score_gpt":0.1548691884507959,"score_spread":0.14784398819020886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757736315","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":[0.005209567,0.039041076,0.84124726,0.02043447,0.0033374776,0.000104724255,0.00014987514,0.0001748432,0.090300776],"genre_scores_gemma":[0.22152126,0.062163085,0.62502986,0.004233723,0.007889265,0.00087723107,0.00034328754,0.00031517315,0.0776271],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986077,0.0006221983,0.000078939716,0.00016670201,0.00045762135,0.00006685014],"domain_scores_gemma":[0.99857116,0.0008256325,0.00006987763,0.00013548533,0.00032291742,0.000074891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002408521,0.0011510393,0.0009923427,0.0016837567,0.00086940476,0.0022454902,0.001388348,0.001399515,0.00624882],"category_scores_gemma":[0.003334019,0.00042798356,0.0012817761,0.0016066373,0.0031792677,0.003693223,0.0029707304,0.0036168688,0.001217854],"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":[0.000013187582,0.00005457975,0.00020674305,0.0003202111,0.000041896143,0.000055988756,0.00019325876,0.04763554,0.0003885882,0.8730942,0.010717959,0.067277916],"study_design_scores_gemma":[0.0000110533965,0.00002065716,0.0000780296,0.00011987832,0.000008226039,0.000028762455,0.00010964822,0.05793034,0.00012525503,0.87327456,0.0682793,0.000014372802],"about_ca_topic_score_codex":0.002151155,"about_ca_topic_score_gemma":0.003421073,"teacher_disagreement_score":0.00624882,"about_ca_system_score_codex":0.002027781,"about_ca_system_score_gemma":0.0012199809,"threshold_uncertainty_score":0.020904422},"labels":[],"label_agreement":null},{"id":"W2765723977","doi":"10.1016/j.petrol.2017.10.059","title":"Application of phased antenna arrays for pipeline leak detection","year":2017,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"D-Wave Systems (Canada)","funders":"Tomsk Polytechnic University","keywords":"Phased array; Pipeline (software); Antenna (radio); Acoustics; Antenna array; Electronic engineering; Pipeline transport; Sensor array; Transducer; Leak; Signal processing; Phased-array optics; SIGNAL (programming language); Underwater; Engineering; Computer science; Electrical engineering; Digital signal processing; Physics; Geology","score_opus":0.007941408565402958,"score_gpt":0.21434670906399952,"score_spread":0.20640530049859657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765723977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13599072,0.0013365358,0.8473034,0.0007268165,0.0002686819,0.00005398694,0.00013499447,0.001415385,0.012769525],"genre_scores_gemma":[0.80348957,0.00075864303,0.19179651,0.0002842712,0.00011105266,0.000029971163,0.000084233565,0.000055006516,0.0033907846],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996425,0.0001357882,0.000011675322,0.00006654933,0.00011266582,0.000030830215],"domain_scores_gemma":[0.9995004,0.00023192447,0.000053663418,0.00005013686,0.00014929782,0.000014608397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024889273,0.0005038653,0.0003680596,0.00034484695,0.00014114569,0.000490425,0.00030716942,0.0006649962,0.0014412709],"category_scores_gemma":[0.0009142987,0.00028588867,0.00024063484,0.0005545764,0.00019454677,0.0004951825,0.00045460986,0.00030330344,0.00056824024],"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":[0.0004846532,0.00010207487,0.0023271285,0.00019282548,0.00006842821,0.00014993308,0.00009828558,0.026737247,0.70536846,0.0033498362,0.0016217338,0.25949934],"study_design_scores_gemma":[0.0000718626,0.000668239,0.0028714973,0.00003397412,0.000107197775,0.00067302486,0.0000901942,0.530094,0.45121983,0.003481846,0.010633152,0.00005508069],"about_ca_topic_score_codex":0.00016220118,"about_ca_topic_score_gemma":0.00026269528,"teacher_disagreement_score":0.0014412709,"about_ca_system_score_codex":0.00018829497,"about_ca_system_score_gemma":0.0002107944,"threshold_uncertainty_score":0.004821539},"labels":[],"label_agreement":null},{"id":"W2768892291","doi":"10.1139/cjce-2015-0293","title":"Identification of most significant factors for modeling deterioration of sewer pipes","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Identification (biology); Engineering; Sanitary sewer; Structural engineering; Civil engineering; Forensic engineering; Environmental science; Environmental engineering","score_opus":0.01536393509110479,"score_gpt":0.19387831001126427,"score_spread":0.17851437492015948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768892291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33615008,0.00025751916,0.6610998,0.00007918078,0.00002877617,0.00026774488,0.0005023866,0.0007744341,0.00084009167],"genre_scores_gemma":[0.88609123,0.00015839968,0.11203241,0.000011353089,0.00001735323,0.00016762922,0.0005443663,0.000055510158,0.00092178443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994605,0.0001873427,0.000027740038,0.00010879801,0.0001319879,0.000083528736],"domain_scores_gemma":[0.99856746,0.0009264944,0.00018172059,0.00005875718,0.00020168169,0.000063870466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016632266,0.0014748268,0.0008819943,0.001663077,0.00047127137,0.0007311469,0.000973797,0.0007157217,0.0012503559],"category_scores_gemma":[0.0043185204,0.00041950773,0.0014908137,0.0007939198,0.00041542348,0.00055532507,0.00057590735,0.0006588729,0.000194386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023509059,0.0002442069,0.08176363,0.0001705304,0.00020137822,0.00024325021,0.00016889718,0.8303147,0.009298306,0.0024761974,0.0007353247,0.07414842],"study_design_scores_gemma":[0.000011015247,0.00004945998,0.011456049,0.0000056459885,0.00003121241,0.000018309935,0.000031395502,0.9863093,0.0012357669,0.00054967694,0.00028624898,0.000016024827],"about_ca_topic_score_codex":0.037636906,"about_ca_topic_score_gemma":0.038345695,"teacher_disagreement_score":0.037636906,"about_ca_system_score_codex":0.0006097697,"about_ca_system_score_gemma":0.0031405808,"threshold_uncertainty_score":0.07483566},"labels":[],"label_agreement":null},{"id":"W2769382088","doi":"10.4028/www.scientific.net/amm.873.176","title":"Performance Index for Rehabilitating Water Distribution Networks","year":2017,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"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":"Analytic hierarchy process; Reliability (semiconductor); Index (typography); Function (biology); Rehabilitation; Process (computing); Criticality; Distribution (mathematics); Computer science; Environmental resource management; Reliability engineering; Operations research; Engineering; Civil engineering; Business; Environmental science; Mathematics","score_opus":0.006602533013554039,"score_gpt":0.1854801543081716,"score_spread":0.17887762129461757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769382088","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":[0.6726402,0.000752831,0.24445684,0.00045709347,0.000067031426,0.00071443984,0.0025408168,0.0007531707,0.07761761],"genre_scores_gemma":[0.9716921,0.00013989053,0.02545363,0.000011119608,0.000010860365,0.000103976854,0.0008892285,0.000015014327,0.0016841392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978077,0.00037421973,0.00014512698,0.0001437953,0.0013319307,0.00019720785],"domain_scores_gemma":[0.99449366,0.0018100953,0.000980037,0.00022987687,0.0022693702,0.00021689091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027676788,0.0006002483,0.00033774378,0.005096418,0.0006327409,0.0015533603,0.0005183166,0.00036724962,0.0018717482],"category_scores_gemma":[0.011377709,0.00015263686,0.00042082428,0.0047806683,0.00041799204,0.0014398565,0.00081958174,0.00032688238,0.00031546666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002545749,0.00040391868,0.26121578,0.00040246212,0.00023559603,0.00025076856,0.0009543769,0.39688915,0.0064710677,0.023841536,0.007834031,0.30124673],"study_design_scores_gemma":[0.000017164137,0.00066428905,0.19596098,0.00009989173,0.00009915938,0.0002450705,0.0019355774,0.7674635,0.007434975,0.011480343,0.014508082,0.00009095601],"about_ca_topic_score_codex":0.020201419,"about_ca_topic_score_gemma":0.025760746,"teacher_disagreement_score":0.020201419,"about_ca_system_score_codex":0.003308373,"about_ca_system_score_gemma":0.0019780856,"threshold_uncertainty_score":0.04016763},"labels":[],"label_agreement":null},{"id":"W2770171409","doi":"10.3390/e19110613","title":"Entropy Applications to Water Monitoring Network Design: A Review","year":2017,"lang":"en","type":"review","venue":"Entropy","topic":"Water Systems and Optimization","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Network planning and design; Entropy (arrow of time); Streamflow; Environmental science; Network monitoring; Water resources; Computer network","score_opus":0.08667075756971959,"score_gpt":0.3246274948732067,"score_spread":0.2379567373034871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770171409","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":[0.00037034645,0.993006,0.0033270286,0.00022114281,0.00017550417,0.000010813586,0.000033950044,0.000019053241,0.002836147],"genre_scores_gemma":[0.0027980232,0.99420834,0.0019881972,0.000098615885,0.0001759964,0.000014223111,0.00004395578,0.000007358102,0.00066530786],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99966836,0.00005840524,0.00005125828,0.000070535476,0.00013040344,0.000021064701],"domain_scores_gemma":[0.9989115,0.00073242496,0.000103700884,0.000034995264,0.00018683328,0.000030493706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007969995,0.0010976504,0.0013956119,0.0030743233,0.00034706845,0.0010558843,0.00096059305,0.001011551,0.0047008237],"category_scores_gemma":[0.0019147779,0.0005781929,0.0008902,0.0043662232,0.00059147406,0.0017213788,0.0008292901,0.0010681765,0.0015821696],"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":[0.00003428738,0.00008014759,0.00032486254,0.028176421,0.00012321411,0.00017311153,0.00009889887,0.0049208244,0.0021472,0.02203189,0.01377832,0.9281109],"study_design_scores_gemma":[0.000013949465,0.00017056079,0.0010071081,0.006705431,0.00023898655,0.0011083251,0.00009076056,0.0021423325,0.0025096037,0.012594568,0.97334564,0.0000728321],"about_ca_topic_score_codex":0.0013665402,"about_ca_topic_score_gemma":0.0015619784,"teacher_disagreement_score":0.0047008237,"about_ca_system_score_codex":0.00060526986,"about_ca_system_score_gemma":0.001147366,"threshold_uncertainty_score":0.015725791},"labels":[],"label_agreement":null},{"id":"W2771213212","doi":"10.1186/s40713-017-0007-9","title":"Collective thinking approach for improving leak detection systems","year":2016,"lang":"en","type":"article","venue":"Smart Water","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Leak; Computer science; Leak detection; Noise (video); Identification (biology); Pipeline (software); Real-time computing; Radio-frequency identification; Process (computing); Pipeline transport; Data mining; Artificial intelligence; Computer security; Engineering","score_opus":0.008507151823114685,"score_gpt":0.16524243658407212,"score_spread":0.15673528476095744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771213212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082736276,0.00021461071,0.90923405,0.00048172422,0.000060639817,0.00012627844,0.00003444933,0.0013573145,0.0057546105],"genre_scores_gemma":[0.8508182,0.000105241306,0.14672507,0.00013144266,0.000036356378,0.00013160596,0.000055122593,0.000039628343,0.0019574228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984818,0.00039821648,0.0001121138,0.0003792114,0.00044737832,0.00018117447],"domain_scores_gemma":[0.9981116,0.0006862691,0.00025440118,0.00018276046,0.00065830076,0.00010661136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002290391,0.0009988258,0.00087710045,0.00119061,0.00089870236,0.0013663521,0.0012496059,0.0010025081,0.0025297452],"category_scores_gemma":[0.0031223346,0.00034948823,0.0008856061,0.0006688614,0.00074570987,0.0019668562,0.0016023116,0.0010774102,0.0004323025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003824056,0.000596538,0.0067792386,0.00027850835,0.00019783527,0.00028390798,0.0009347794,0.4725411,0.031087535,0.018995423,0.002333832,0.46558905],"study_design_scores_gemma":[0.000015523434,0.0001485837,0.00052877713,0.000015350191,0.000045881254,0.000027585871,0.00009797368,0.98707086,0.0044288877,0.0064846193,0.0011208323,0.000015178041],"about_ca_topic_score_codex":0.0036800732,"about_ca_topic_score_gemma":0.0028563777,"teacher_disagreement_score":0.0036800732,"about_ca_system_score_codex":0.0012725125,"about_ca_system_score_gemma":0.0012870615,"threshold_uncertainty_score":0.0121129155},"labels":[],"label_agreement":null},{"id":"W2777472498","doi":"10.1061/(asce)wr.1943-5452.0000875","title":"PADDS Algorithm Assessment for Biobjective Water Distribution System Benchmark Design Problems","year":2017,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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 Waterloo","funders":"Directorate for Mathematical and Physical Sciences","keywords":"Benchmark (surveying); Selection (genetic algorithm); Pareto principle; Mathematical optimization; Multi-objective optimization; Computer science; Algorithm; Evolutionary algorithm; Mathematics; Machine learning","score_opus":0.014472613768734143,"score_gpt":0.23007201279399736,"score_spread":0.2155993990252632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2777472498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34691846,0.003698566,0.6177375,0.0010382688,0.00030090113,0.0006637104,0.0011048751,0.001421841,0.027115883],"genre_scores_gemma":[0.60742724,0.0008225785,0.38562804,0.00018056593,0.000036901813,0.00076818315,0.0014434217,0.00014682651,0.0035461385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985815,0.0007002689,0.000088399065,0.0001282775,0.00040126787,0.00010020175],"domain_scores_gemma":[0.9949752,0.0036407518,0.00021971445,0.00020864294,0.0008415611,0.00011418307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004175658,0.0016497492,0.00090476515,0.0026304077,0.0005106472,0.0013918602,0.0015242363,0.0015211826,0.002317847],"category_scores_gemma":[0.008707325,0.00038491795,0.0010155737,0.0015890509,0.00063504657,0.00078625797,0.0010271559,0.0010532998,0.00021236208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059643415,0.00006281758,0.0009113771,0.0001299393,0.000034292756,0.000027373571,0.000019588939,0.9645493,0.00031529737,0.0021785717,0.00075172907,0.030960167],"study_design_scores_gemma":[0.000022696739,0.000074647694,0.0002238595,0.000018260105,0.000008971116,0.000011062245,0.00002197013,0.9970552,0.0007393859,0.0010262851,0.0007925095,0.0000051145526],"about_ca_topic_score_codex":0.005566738,"about_ca_topic_score_gemma":0.005568705,"teacher_disagreement_score":0.005566738,"about_ca_system_score_codex":0.0016826103,"about_ca_system_score_gemma":0.0020263668,"threshold_uncertainty_score":0.022083282},"labels":[],"label_agreement":null},{"id":"W2780021604","doi":"10.14288/1.0357367","title":"Prediction of water distribution system pipes and isolation valves failure using Bayesian models with the consideration of soil corrosion and climate change","year":2017,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Water Systems and 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":"Corrosion; Bayesian probability; Climate change; Environmental science; Geotechnical engineering; Forensic engineering; Engineering; Geology; Mathematics; Statistics; Materials science; Metallurgy","score_opus":0.012499557369573963,"score_gpt":0.1494480717323091,"score_spread":0.13694851436273514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2780021604","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":[0.589774,0.00040395468,0.4051397,0.00039297435,0.0000355362,0.00009942412,0.0005893764,0.00027709108,0.0032880348],"genre_scores_gemma":[0.9864546,0.00016826883,0.011657588,0.000025508898,0.000012946402,0.0000501476,0.00028299735,0.000008826626,0.0013391697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997013,0.000084153246,0.00001752409,0.000077435536,0.000066883025,0.00005273926],"domain_scores_gemma":[0.9989507,0.0006286076,0.00018115761,0.000021702817,0.00016139128,0.00005641213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010314166,0.0007589076,0.00061618,0.0007172364,0.00023551635,0.00060442125,0.00073413056,0.00087149686,0.0006571606],"category_scores_gemma":[0.002409503,0.0006473853,0.000637327,0.00040781856,0.0003248862,0.00076931494,0.00043173484,0.00058849517,0.00012536476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014219994,0.00002000548,0.0020532797,0.000007946452,0.000012639109,0.000011517833,0.000010139337,0.99379045,0.00022771745,0.00047670415,0.000068991365,0.0033063174],"study_design_scores_gemma":[9.825985e-7,0.0000050749363,0.0004228666,9.355647e-7,0.0000021596777,0.0000013504457,0.000002050452,0.9992754,0.000030361494,0.00023655989,0.000020524854,0.0000016567512],"about_ca_topic_score_codex":0.037756525,"about_ca_topic_score_gemma":0.02858501,"teacher_disagreement_score":0.037756525,"about_ca_system_score_codex":0.00094587856,"about_ca_system_score_gemma":0.0009180484,"threshold_uncertainty_score":0.07507354},"labels":[],"label_agreement":null},{"id":"W2782346946","doi":"10.1061/(asce)wr.1943-5452.0000890","title":"Benchmark Performance Indicators for Utility Water and Wastewater Pipelines Infrastructure","year":2018,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":true,"ca_institutions":"University of Waterloo; Research Canada","funders":"","keywords":"Benchmarking; Benchmark (surveying); Performance indicator; Environmental economics; Sustainability; Network performance; Computer science; Environmental resource management; Environmental science; Operations research; Engineering; Business; Telecommunications; Economics","score_opus":0.006303926304801484,"score_gpt":0.19717310567867424,"score_spread":0.19086917937387277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782346946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40987283,0.0022624265,0.50023794,0.0011615338,0.0003179249,0.0007943651,0.0058175335,0.0031852745,0.076350056],"genre_scores_gemma":[0.93213,0.00047759488,0.061470643,0.00004947573,0.00003191452,0.00023976418,0.00393818,0.000086693515,0.0015758234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9901509,0.0032149537,0.00077760493,0.00058686844,0.0045683505,0.0007013581],"domain_scores_gemma":[0.98491955,0.003532742,0.0037780816,0.0011287403,0.0061274534,0.0005133929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011335948,0.0013041613,0.0008630974,0.0076796087,0.00084973214,0.0029010288,0.0010046312,0.0007545283,0.0013118652],"category_scores_gemma":[0.031655245,0.0002442922,0.0008319395,0.0108036045,0.0006826795,0.0032281394,0.0018911536,0.0007841102,0.00030171347],"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":[0.0002691128,0.00037686506,0.0856245,0.0003959914,0.0001682567,0.00012753042,0.00033906943,0.6941537,0.0028150917,0.048247796,0.009527075,0.15795507],"study_design_scores_gemma":[0.000030498182,0.00074232684,0.0652378,0.00023344676,0.00006096075,0.00013984482,0.0009592682,0.88067675,0.009470535,0.021892322,0.020421088,0.0001351701],"about_ca_topic_score_codex":0.017746296,"about_ca_topic_score_gemma":0.01169543,"teacher_disagreement_score":0.017746296,"about_ca_system_score_codex":0.0058485516,"about_ca_system_score_gemma":0.0038546072,"threshold_uncertainty_score":0.059950948},"labels":[],"label_agreement":null},{"id":"W2782593635","doi":"10.1139/cjce-2017-0515","title":"Tailrace surge shaft optimization procedure for minimum downsurge","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"Surge; Surge tank; Tailwater; Hydropower; Marine engineering; Turbine; Transient (computer programming); Engineering; Water hammer; Storm surge; Environmental science; Geotechnical engineering; Geology; Electrical engineering; Computer science; Mechanical engineering","score_opus":0.006554540457058802,"score_gpt":0.1744509065263787,"score_spread":0.1678963660693199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782593635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13258843,0.00030103017,0.85236686,0.00010183142,0.00004362074,0.00022645568,0.00023219278,0.00088907126,0.013250528],"genre_scores_gemma":[0.764559,0.00018325706,0.22775093,0.00003801321,0.00001439468,0.00029409525,0.00032534945,0.0002018575,0.006633211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986744,0.000024769135,0.0000071848253,0.000029792545,0.00004101897,0.000029845924],"domain_scores_gemma":[0.9998234,0.00006244733,0.00002768561,0.000016926004,0.000060854843,0.000008735257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036658466,0.0007936739,0.0006333958,0.000555178,0.0003361449,0.0004396176,0.00043231074,0.0004923482,0.006303298],"category_scores_gemma":[0.00072155043,0.00028829183,0.0005784503,0.00039270666,0.00024501927,0.0003402405,0.00040869732,0.00053101365,0.0007453465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002091913,0.00011775533,0.0011844963,0.0002587399,0.000037481335,0.00022945544,0.00012782915,0.84849244,0.036683053,0.0058754794,0.002242002,0.10454199],"study_design_scores_gemma":[0.000031257503,0.00027994116,0.0010154985,0.00001756773,0.000020858397,0.000045257355,0.00006815654,0.9805871,0.013543888,0.0012581665,0.0031188098,0.000013517736],"about_ca_topic_score_codex":0.0021365555,"about_ca_topic_score_gemma":0.0030115915,"teacher_disagreement_score":0.006303298,"about_ca_system_score_codex":0.00033866934,"about_ca_system_score_gemma":0.0009956212,"threshold_uncertainty_score":0.021086693},"labels":[],"label_agreement":null},{"id":"W2784002364","doi":"10.1139/cjce-2017-0490","title":"Utilizing siphon for effective management of air in gravity-fed water pipelines","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"National Natural Science Foundation of China","keywords":"Siphon (mollusc); Pipeline (software); Pipeline transport; Inlet; Marine engineering; Engineering; Hydraulic head; Head (geology); Civil engineering; Excellence; Petroleum engineering; Mechanical engineering; Geotechnical engineering; Geology","score_opus":0.005729295511447111,"score_gpt":0.18237626791375974,"score_spread":0.17664697240231264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784002364","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":[0.9244345,0.0005136912,0.07231705,0.000105701954,0.00004926847,0.00004901082,0.000059187154,0.00036807015,0.0021035331],"genre_scores_gemma":[0.9894571,0.00015280554,0.009696669,0.000009773545,0.0000065027166,0.000018237599,0.000016878703,0.000008980358,0.0006332225],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987304,0.000025725527,0.000009212749,0.000021594256,0.000046234687,0.000024139003],"domain_scores_gemma":[0.9998945,0.000033404922,0.000029966634,0.000009656015,0.000018318113,0.000014103964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017254129,0.0002964287,0.00027379763,0.00025898436,0.00030233344,0.00024876295,0.0002889561,0.00029732907,0.00055840996],"category_scores_gemma":[0.00025625993,0.00011477435,0.00016694223,0.00015567256,0.0003649237,0.00044923148,0.00044313798,0.00013176426,0.000105746934],"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":[0.00027201694,0.00006302562,0.0018032602,0.00027112526,0.000010426858,0.00036724375,0.00016656393,0.011275553,0.9494331,0.0008257096,0.0002290075,0.035282947],"study_design_scores_gemma":[0.00006260574,0.0020344967,0.008397535,0.000034526194,0.000049290935,0.0005366166,0.00025153087,0.126252,0.85236835,0.0010037114,0.008939709,0.00006972728],"about_ca_topic_score_codex":0.0003020384,"about_ca_topic_score_gemma":0.00064535456,"teacher_disagreement_score":0.00055840996,"about_ca_system_score_codex":0.0001443888,"about_ca_system_score_gemma":0.000290014,"threshold_uncertainty_score":0.0018680692},"labels":[],"label_agreement":null},{"id":"W2786624384","doi":"10.14288/cjur.v2i2.189228","title":"Effect of hole area and incline angle on pipe flow leakage rates","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Water Systems and 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":"Mechanics; Leakage (economics); Drop (telecommunication); Volumetric flow rate; Water flow; Pipe flow; Materials science; Environmental science; Petroleum engineering; Geology; Mechanical engineering; Engineering; Soil science; Physics","score_opus":0.01190162515740249,"score_gpt":0.24616385063713606,"score_spread":0.23426222547973358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786624384","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":[0.9975101,0.00016231411,0.0017112131,0.000011088031,0.00000847206,0.000010657247,0.000051631425,0.000036165402,0.0004983551],"genre_scores_gemma":[0.9993131,0.00008392842,0.00045577378,0.0000033760439,0.0000016342414,0.0000040756545,0.000022207745,0.000004796798,0.00011115104],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995289,0.00013247995,0.00004284786,0.00009173048,0.00009385996,0.000110151974],"domain_scores_gemma":[0.9951998,0.0034519988,0.00069660135,0.00023376767,0.00029147344,0.00012631885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072977867,0.00046898922,0.00038144644,0.0004218754,0.00016842663,0.0005239924,0.00022171278,0.00033473942,0.0015196609],"category_scores_gemma":[0.0031590478,0.00019440176,0.00028992054,0.00037815722,0.00051553734,0.00059187785,0.00036823,0.00029635467,0.00015075042],"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":[0.008009339,0.0009300948,0.13895348,0.0009554423,0.00017160075,0.0012942577,0.00052914553,0.29900077,0.45701832,0.0007144197,0.0005113477,0.09191177],"study_design_scores_gemma":[0.00018603004,0.014301673,0.30526665,0.00011406513,0.00038283225,0.0005258741,0.0014336865,0.15979697,0.514649,0.00063011347,0.0025478974,0.00016533048],"about_ca_topic_score_codex":0.0007270315,"about_ca_topic_score_gemma":0.00086296455,"teacher_disagreement_score":0.0015196609,"about_ca_system_score_codex":0.00018910662,"about_ca_system_score_gemma":0.00020217897,"threshold_uncertainty_score":0.0050837398},"labels":[],"label_agreement":null},{"id":"W2787826464","doi":"10.1109/ice.2017.8279884","title":"Optimizing control for large scale dynamic systems; general issues and case study results: Transmission operations optimizer for Toronto water system","year":2017,"lang":"en","type":"article","venue":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","topic":"Water Systems and 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":"IBI Group (Canada)","funders":"","keywords":"Computer science; Control (management); Controller (irrigation); Transmission (telecommunications); Control system; Scale (ratio); Transmission system; Water supply; Presentation (obstetrics); Operations research; Industrial engineering; Control engineering; Engineering; Telecommunications","score_opus":0.020403123187066503,"score_gpt":0.2799639843910887,"score_spread":0.2595608612040222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787826464","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":[0.8392195,0.002919791,0.115698904,0.000575873,0.000029168239,0.00038348662,0.000827727,0.00040764964,0.03993788],"genre_scores_gemma":[0.99114966,0.000411478,0.005236664,0.000013292086,0.000007403637,0.00006147998,0.00012642345,0.0000174528,0.0029760217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959975,0.00013302387,0.000018018558,0.000051442315,0.00011738711,0.00008037893],"domain_scores_gemma":[0.9994362,0.00037349988,0.00006231044,0.000030505968,0.00007296121,0.000024488727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065091235,0.0008208863,0.00056638237,0.00044404945,0.0005684453,0.0011538442,0.000547829,0.0009335521,0.0024479772],"category_scores_gemma":[0.0013428713,0.00015351555,0.00042992693,0.0010828478,0.00076274766,0.0004301237,0.00042269862,0.0005989767,0.00010342944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010095805,0.000087371984,0.0016092851,0.0002194373,0.00003269386,0.00035280103,0.000109839406,0.9785957,0.0016247557,0.006475117,0.00093607785,0.0098560555],"study_design_scores_gemma":[0.000047561007,0.00024244771,0.0036406212,0.000016550874,0.000032128428,0.00005090172,0.00029579573,0.9885747,0.002547686,0.0023651598,0.002167252,0.000019115872],"about_ca_topic_score_codex":0.18220368,"about_ca_topic_score_gemma":0.1542234,"teacher_disagreement_score":0.81779635,"about_ca_system_score_codex":0.0025465193,"about_ca_system_score_gemma":0.0012697846,"threshold_uncertainty_score":0.36228627},"labels":[],"label_agreement":null},{"id":"W2790270831","doi":"10.4095/306492","title":"Optimal use of source water protection modelling results","year":2018,"lang":"en","type":"report","venue":"","topic":"Water Systems and 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":"Water source; Environmental science; Water resource management","score_opus":0.0683460327156617,"score_gpt":0.21747547651991922,"score_spread":0.14912944380425752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790270831","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":[0.14833207,0.00041892673,0.71974015,0.0013713586,0.00020348541,0.00046287195,0.0075871437,0.0029347164,0.11894919],"genre_scores_gemma":[0.8209078,0.0002888063,0.16792107,0.00012647202,0.000025605894,0.00034459046,0.004381138,0.0006183422,0.005386132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929094,0.0002547924,0.000048658272,0.000114384195,0.00020526497,0.00008596345],"domain_scores_gemma":[0.99871755,0.00062738976,0.0000645895,0.00018422486,0.00037551596,0.0000307131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012628579,0.00093553425,0.000741514,0.0013134705,0.000501586,0.0017872594,0.001150819,0.000997907,0.008006773],"category_scores_gemma":[0.0052981745,0.0005152077,0.0013209356,0.0009852157,0.00028925046,0.0013673205,0.0007637889,0.00080520345,0.0012009409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003239196,0.00003491847,0.00079616916,0.000052597803,0.000015074812,0.000033605018,0.00002582261,0.977499,0.0005513212,0.00481805,0.0013725022,0.01476862],"study_design_scores_gemma":[0.000009716871,0.000012439368,0.00018065315,0.000017190723,0.000008493395,0.0000061901887,0.000021504824,0.9938916,0.00078196905,0.0031027964,0.0019619947,0.0000055835126],"about_ca_topic_score_codex":0.017331675,"about_ca_topic_score_gemma":0.013597926,"teacher_disagreement_score":0.017331675,"about_ca_system_score_codex":0.0011827659,"about_ca_system_score_gemma":0.0023520342,"threshold_uncertainty_score":0.034461558},"labels":[],"label_agreement":null},{"id":"W2790613613","doi":"10.3390/w10040368","title":"Comparing Topological Partitioning Methods for District Metered Areas in the Water Distribution Network","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":27,"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 Key Research and Development Program of China; Engineering and Physical Sciences Research Council; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Graph partition; Computer science; Greedy algorithm; Modularity (biology); Graph theory; Mathematics; Graph; Topology (electrical circuits); Mathematical optimization; Theoretical computer science; Combinatorics","score_opus":0.04052555642992547,"score_gpt":0.28486399166371845,"score_spread":0.24433843523379298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790613613","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":[0.36218295,0.0024368311,0.6246185,0.00033699695,0.00011743749,0.00038933987,0.00032984882,0.0008137121,0.008774382],"genre_scores_gemma":[0.7967703,0.00089946715,0.20061556,0.000041301722,0.00003279097,0.00017903067,0.0004809276,0.00011167291,0.0008690011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986811,0.0006316825,0.00007264214,0.0001250434,0.00038070072,0.00010873007],"domain_scores_gemma":[0.9936405,0.0045797336,0.00038438872,0.00042783588,0.0008364103,0.00013123822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024135706,0.0009296595,0.00069928286,0.0032546485,0.0006075351,0.00089426665,0.00081366615,0.0005761453,0.0010061163],"category_scores_gemma":[0.009132383,0.00028690003,0.0006279734,0.0022100443,0.00049492466,0.00189152,0.0008334221,0.00042015026,0.00015953758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003032537,0.00008808403,0.0045049954,0.00031835106,0.00012845152,0.000049391536,0.00018703796,0.8359657,0.0024626143,0.008913659,0.00092924485,0.1461492],"study_design_scores_gemma":[0.000026786809,0.00015258566,0.0027411336,0.000024772473,0.000043721197,0.00007633632,0.00022953494,0.9893666,0.0023422143,0.003876928,0.0010985655,0.000020712709],"about_ca_topic_score_codex":0.00590829,"about_ca_topic_score_gemma":0.007939256,"teacher_disagreement_score":0.00590829,"about_ca_system_score_codex":0.0012477712,"about_ca_system_score_gemma":0.0010025774,"threshold_uncertainty_score":0.012764335},"labels":[],"label_agreement":null},{"id":"W2790946617","doi":"10.2166/wst.2018.085","title":"Experimental study of geysers through a vent pipe connected to flowing sewers","year":2018,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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":"Head (geology); Hydraulic head; Drainage; Geology; Surge; Flow (mathematics); Volumetric flow rate; Electrical conduit; Pressure head; Sanitary sewer; Atmospheric pressure; Environmental science; Geotechnical engineering; Hydrology (agriculture); Mechanics; Geomorphology; Environmental engineering; Engineering","score_opus":0.011230557160194292,"score_gpt":0.2422201848000361,"score_spread":0.2309896276398418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790946617","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":[0.99892527,0.000031382133,0.0007695757,0.000009080967,0.0000050928097,0.000013261977,0.00007189198,0.000041875355,0.00013251464],"genre_scores_gemma":[0.9974809,0.00007479225,0.0013409002,0.000005964617,0.0000045330607,0.000018985798,0.00009814517,0.000010803718,0.0009649037],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968636,0.000028946512,0.000019245972,0.00011592935,0.00007923561,0.00007030491],"domain_scores_gemma":[0.9995938,0.00015739228,0.000056322777,0.000050744453,0.00008319253,0.000058524154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034749485,0.0005334703,0.0006987499,0.00034827838,0.00059769186,0.000308356,0.00060445094,0.00067845744,0.0021323287],"category_scores_gemma":[0.00058488036,0.0002418191,0.00045529637,0.00027890896,0.00062049384,0.0005691173,0.00061228906,0.000615195,0.00019611082],"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":[0.0007439412,0.00030503026,0.0052294224,0.00013739156,0.000019859232,0.0006745005,0.00043017103,0.0036490844,0.983375,0.00019189494,0.00010187286,0.0051417975],"study_design_scores_gemma":[0.00010359683,0.00486265,0.044676617,0.000023792367,0.00005412668,0.00020842963,0.0006184248,0.0333975,0.914839,0.00014135223,0.0010313898,0.000043080483],"about_ca_topic_score_codex":0.0011845186,"about_ca_topic_score_gemma":0.0013851732,"teacher_disagreement_score":0.0021323287,"about_ca_system_score_codex":0.00025308036,"about_ca_system_score_gemma":0.00027043457,"threshold_uncertainty_score":0.0071333647},"labels":[],"label_agreement":null},{"id":"W2791092751","doi":"10.1080/14488353.2018.1444333","title":"Condition assessment model for sewer pipelines using fuzzy-based evidential reasoning","year":2018,"lang":"en","type":"article","venue":"Australian Journal of Civil Engineering","topic":"Water Systems and 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":"Concordia University","funders":"Qatar National Research Fund","keywords":"Evidential reasoning approach; Pipeline transport; Engineering; Project commissioning; Fuzzy logic; Computer science; Publishing; Civil engineering; Forensic engineering; Artificial intelligence; Decision support system; Environmental engineering; Political science","score_opus":0.02829865822963799,"score_gpt":0.2829821315043384,"score_spread":0.25468347327470037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791092751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033927087,0.00012208382,0.96232146,0.00015901805,0.000019071094,0.000082478364,0.000085200365,0.00016792916,0.003115659],"genre_scores_gemma":[0.9455085,0.00018871242,0.05069083,0.00003643702,0.000019289535,0.00025460526,0.00010238779,0.000016657044,0.0031825416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990396,0.00027300668,0.000053849435,0.0002471635,0.00028402987,0.0001023165],"domain_scores_gemma":[0.99822897,0.0011021622,0.00024597382,0.000038044982,0.00033967316,0.000045227083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023422674,0.0012050472,0.001198622,0.0013237081,0.0007572146,0.0018690518,0.0016937291,0.0020216298,0.0021370018],"category_scores_gemma":[0.0039615026,0.00067249185,0.0014454543,0.00068853097,0.0011377156,0.0018842905,0.00091117877,0.0015325623,0.00029314274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014183099,0.000010673749,0.00018417934,0.000017011296,0.000009933187,0.000037146947,0.000041072028,0.99447674,0.00038726177,0.0025641504,0.000044356373,0.002213349],"study_design_scores_gemma":[0.0000024868996,0.000009137435,0.00005624965,0.000002044726,0.000004322463,0.0000053513936,0.000004012621,0.99891686,0.00007912381,0.000876323,0.000040640483,0.000003425627],"about_ca_topic_score_codex":0.01936976,"about_ca_topic_score_gemma":0.010067941,"teacher_disagreement_score":0.01936976,"about_ca_system_score_codex":0.0023200822,"about_ca_system_score_gemma":0.0017770233,"threshold_uncertainty_score":0.038514018},"labels":[],"label_agreement":null},{"id":"W2791182213","doi":"10.1007/s12652-018-0733-3","title":"A framework for single and multiple anomalies localization in pipelines","year":2018,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Pipeline transport; Pipeline (software); Computer science; Natural disaster; Risk analysis (engineering); Population; Computer security; Natural gas; Petroleum engineering; Environmental science; Business; Engineering; Geology","score_opus":0.03885331528925131,"score_gpt":0.26333172755132495,"score_spread":0.22447841226207363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791182213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007903759,0.00004963871,0.99847466,0.000038221835,0.00001302797,0.000010542769,0.00002145642,0.00030799175,0.00029411854],"genre_scores_gemma":[0.19075757,0.00036626062,0.8040932,0.00007387545,0.00010238941,0.00011266392,0.0002307436,0.00027300132,0.0039902907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902284,0.00019664428,0.000053526484,0.0002691587,0.00031793595,0.00013987134],"domain_scores_gemma":[0.9992067,0.00023390178,0.00007202411,0.00014635398,0.00026546334,0.00007543039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014996657,0.0009973561,0.0012951677,0.0015679424,0.0010693368,0.0020915023,0.003449446,0.0020215511,0.0033528765],"category_scores_gemma":[0.003259477,0.000806411,0.0017028822,0.0013840945,0.0014105809,0.0025391008,0.0035214117,0.0020081582,0.0009417685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010219365,0.00008370604,0.0009792917,0.0001469852,0.000104247396,0.00028076343,0.00024404273,0.70337826,0.0076374393,0.17479764,0.0036208944,0.10862451],"study_design_scores_gemma":[0.000005585897,0.000016259306,0.00005958989,0.000006538084,0.0000109833345,0.00003153792,0.000019705476,0.9793488,0.00068196433,0.01811235,0.0016951249,0.000011539849],"about_ca_topic_score_codex":0.01908184,"about_ca_topic_score_gemma":0.01984586,"teacher_disagreement_score":0.01908184,"about_ca_system_score_codex":0.0012221215,"about_ca_system_score_gemma":0.0024763024,"threshold_uncertainty_score":0.037941515},"labels":[],"label_agreement":null},{"id":"W2792212496","doi":"10.1177/1369433218764976","title":"Optimum design of composite conical tanks under hydrostatic pressure","year":2018,"lang":"en","type":"article","venue":"Advances in Structural Engineering","topic":"Water Systems and 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":"Western University","funders":"","keywords":"Structural engineering; Composite number; Conical surface; Hydrostatic pressure; Materials science; Ultimate tensile strength; Shell (structure); Finite element method; Buckling; Economic shortage; Composite material; Engineering","score_opus":0.005770408593636995,"score_gpt":0.21842079538164977,"score_spread":0.21265038678801276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792212496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37620512,0.0003184554,0.6059352,0.0001630099,0.000041633,0.00019733924,0.00018966333,0.00028669392,0.016662847],"genre_scores_gemma":[0.921386,0.00014935328,0.07576848,0.00002866071,0.0000065079544,0.00019659585,0.00009047425,0.000041595114,0.0023323514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973816,0.00006221122,0.000009484244,0.000049365135,0.00007770212,0.00006297011],"domain_scores_gemma":[0.99962664,0.00018196608,0.00007624683,0.000011754413,0.00007327428,0.000030070154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005217932,0.00056000653,0.00080138835,0.0006100935,0.00028054003,0.00077719154,0.00065144576,0.0010518744,0.0023014422],"category_scores_gemma":[0.0010040184,0.00055419485,0.0007292201,0.00042992874,0.0005961739,0.00041274482,0.00059650105,0.00044348554,0.0002560223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097489945,0.00004459858,0.00035208484,0.000053212272,0.000015145666,0.000059424663,0.000027643231,0.9848791,0.00717,0.0015060172,0.000159089,0.0056362837],"study_design_scores_gemma":[0.000026328253,0.00014399456,0.00035465014,0.0000062105205,0.000012463633,0.000011153093,0.000030207924,0.99621505,0.0021378445,0.00065127236,0.00040403902,0.0000068720597],"about_ca_topic_score_codex":0.0022059872,"about_ca_topic_score_gemma":0.0028266932,"teacher_disagreement_score":0.0023014422,"about_ca_system_score_codex":0.0005784701,"about_ca_system_score_gemma":0.0011682771,"threshold_uncertainty_score":0.007699132},"labels":[],"label_agreement":null},{"id":"W2793376378","doi":"10.2166/aqua.2018.097","title":"Probability of network disconnection of water distribution system for maintenance prioritization","year":2018,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":"Centre For Cold Ocean Resources Engineering; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Unavailability; Reliability engineering; Robustness (evolution); Probability distribution; Topology (electrical circuits); Supervisor; Computer science; Fail-safe; Engineering; Mathematics; Statistics; Electrical engineering","score_opus":0.020233822484832242,"score_gpt":0.24729195955401864,"score_spread":0.2270581370691864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793376378","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":[0.9646833,0.00022522623,0.03182125,0.0002494811,0.000031358424,0.00006979366,0.0004919869,0.00018083668,0.0022467384],"genre_scores_gemma":[0.9986577,0.000023276494,0.0009626746,0.0000042286197,0.0000039422666,0.000010032609,0.00014724518,0.0000049614696,0.00018598363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989303,0.0002556622,0.000070666734,0.00018929715,0.00041424445,0.00013986972],"domain_scores_gemma":[0.9809638,0.0135410875,0.002866927,0.00052748836,0.001563478,0.0005371899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020405885,0.0002477225,0.00033443034,0.0015297557,0.00039626475,0.00064088544,0.00075537607,0.00048681168,0.002939116],"category_scores_gemma":[0.014413618,0.00016135552,0.00035432554,0.0007916156,0.00055898697,0.00086658687,0.00053921307,0.00057335536,0.00011006511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011174985,0.00013094174,0.21021761,0.00026574032,0.00017525686,0.0011694899,0.00022407058,0.73596966,0.005987597,0.010749016,0.0022615958,0.03173153],"study_design_scores_gemma":[0.00002444036,0.0002828937,0.08283532,0.000033264478,0.00008250864,0.00058457284,0.00025703973,0.90770435,0.0040570158,0.0033475982,0.0007595079,0.000031448944],"about_ca_topic_score_codex":0.0035672009,"about_ca_topic_score_gemma":0.003053292,"teacher_disagreement_score":0.0035672009,"about_ca_system_score_codex":0.00090677565,"about_ca_system_score_gemma":0.0003279818,"threshold_uncertainty_score":0.010791838},"labels":[],"label_agreement":null},{"id":"W2794063233","doi":"10.1002/j.1551-8833.2003.tb10364.x","title":"Applying GIS to a water main corrosion study","year":2003,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"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 Research Council Canada","keywords":"Geographic information system; Corrosion; Computer science; Environmental science; Construction engineering; Civil engineering; Water resource management; Remote sensing; Engineering; Geography; Materials science; Metallurgy","score_opus":0.004586249019007596,"score_gpt":0.19181640907012987,"score_spread":0.18723016005112228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794063233","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":[0.8748274,0.00028347457,0.09043442,0.00064866967,0.000061640836,0.00041689724,0.008104603,0.0017665095,0.023456322],"genre_scores_gemma":[0.89328593,0.00042380518,0.1004723,0.000035306643,0.000017804143,0.0001456954,0.002768651,0.00013830834,0.0027122302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996588,0.00017858134,0.000022880045,0.000034388184,0.000083162166,0.000022107812],"domain_scores_gemma":[0.9991522,0.0005134262,0.000049793758,0.000075632786,0.00017352139,0.00003537499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051171664,0.00046861116,0.0004258945,0.0029797095,0.0005093662,0.00085657334,0.00032565216,0.00036752856,0.0035175185],"category_scores_gemma":[0.001769651,0.00022940127,0.0005404472,0.0041455603,0.00029582338,0.0006232644,0.00045288663,0.00032809813,0.0003744674],"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":[0.0007230196,0.0009605283,0.19429377,0.00078519643,0.00044680343,0.0038386558,0.0026264056,0.47588825,0.019679489,0.023760747,0.015380854,0.2616163],"study_design_scores_gemma":[0.00016538132,0.0008546938,0.09841341,0.00009984682,0.00023550884,0.0009349674,0.009664841,0.816654,0.014109162,0.01763076,0.041133724,0.00010370778],"about_ca_topic_score_codex":0.033913817,"about_ca_topic_score_gemma":0.03758623,"teacher_disagreement_score":0.033913817,"about_ca_system_score_codex":0.00073708955,"about_ca_system_score_gemma":0.00071386615,"threshold_uncertainty_score":0.06743282},"labels":[],"label_agreement":null},{"id":"W2794310693","doi":"10.1016/j.ins.2018.02.055","title":"BRBcast: A new approach to belief rule-based system parameter learning via extended causal strength logic","year":2018,"lang":"en","type":"article","venue":"Information Sciences","topic":"Water Systems and Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Machine learning; Mathematics","score_opus":0.02331628017888007,"score_gpt":0.23268114671800008,"score_spread":0.20936486653912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794310693","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":[0.00080987636,0.000069489186,0.9978942,0.000067396715,0.000025477997,0.000020565525,0.00003711827,0.0002792342,0.0007966204],"genre_scores_gemma":[0.25565118,0.00043114065,0.73750126,0.0004704958,0.00020000686,0.00030395578,0.0004001028,0.00044094308,0.0046008667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973502,0.0008309852,0.00014632285,0.00030354183,0.0012250645,0.00014381166],"domain_scores_gemma":[0.9956398,0.0024868224,0.00025190393,0.000517248,0.0009647811,0.00013943492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035007328,0.0011008332,0.002311204,0.0013661315,0.00060974323,0.0024268853,0.0042812503,0.0016966213,0.0061499164],"category_scores_gemma":[0.010629298,0.0009534777,0.0014025846,0.0013483063,0.0015383997,0.0036431937,0.003819063,0.0044578607,0.0014370175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017972606,0.00019221626,0.00055284763,0.00032212722,0.00023928555,0.00013062313,0.00014069026,0.5932438,0.0026503936,0.15784925,0.0045177415,0.23998137],"study_design_scores_gemma":[0.000008710643,0.000015516778,0.000024868517,0.000008608184,0.000012162141,0.000009558763,0.0000037986038,0.9706623,0.0003587936,0.028178072,0.00071099726,0.000006681058],"about_ca_topic_score_codex":0.005370726,"about_ca_topic_score_gemma":0.006414716,"teacher_disagreement_score":0.0061499164,"about_ca_system_score_codex":0.0010855617,"about_ca_system_score_gemma":0.001983236,"threshold_uncertainty_score":0.020573497},"labels":[],"label_agreement":null},{"id":"W2794439414","doi":"10.1007/s11269-018-1961-1","title":"Method for Extended Period Simulation of Water Distribution Networks with Pressure Driven Demands","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Snapshot (computer storage); Throttle; Flow control valve; Benchmark (surveying); Programmer; Simulation; Real-time computing; Control engineering; Automotive engineering; Engineering; Embedded system; Operating system","score_opus":0.005942184055577788,"score_gpt":0.21294689929746433,"score_spread":0.20700471524188654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794439414","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":[0.043745074,0.0003097556,0.94697267,0.0003165302,0.0001393225,0.00013380637,0.00043036466,0.0006208422,0.0073316004],"genre_scores_gemma":[0.73925865,0.00037892614,0.25071025,0.00020566926,0.000094818075,0.0007313283,0.00048857555,0.00043537834,0.007696425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997212,0.00013420821,0.000012727882,0.0000334548,0.000049883925,0.000048400736],"domain_scores_gemma":[0.9982268,0.0013069102,0.000104924584,0.00008727088,0.00017974968,0.000094221585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001108275,0.0006810338,0.0011849914,0.00059734134,0.000625412,0.00066968356,0.0018038737,0.0016295353,0.006502538],"category_scores_gemma":[0.0031630218,0.00066868804,0.0010184554,0.00058533263,0.0005642881,0.0008536405,0.0010542067,0.001491638,0.00046548052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003389502,0.000023221623,0.0002088625,0.000025674055,0.000022698021,0.000032884564,0.000014668393,0.9933055,0.00028041613,0.0028143567,0.00023731592,0.0030006138],"study_design_scores_gemma":[0.000006507771,0.000004168725,0.000020531552,0.0000017439905,0.0000022268487,0.000002732024,0.0000021431597,0.9993031,0.000036813133,0.00050118624,0.00011725149,0.000001558483],"about_ca_topic_score_codex":0.014989656,"about_ca_topic_score_gemma":0.009990223,"teacher_disagreement_score":0.014989656,"about_ca_system_score_codex":0.000703675,"about_ca_system_score_gemma":0.0013850479,"threshold_uncertainty_score":0.029804766},"labels":[],"label_agreement":null},{"id":"W2795869923","doi":"10.3390/w10040428","title":"Identification of Factors That Influence Energy Performance in Water Distribution System Mains","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"Jacobs (Canada); Queen's University","funders":"Engineering and Physical Sciences Research Council","keywords":"Leakage (economics); Mains electricity; Environmental science; Energy (signal processing); Electricity; Asset management; Pipe network analysis; Principal component analysis; Marine engineering; Engineering; Computer science; Statistics; Mathematics; Mechanics","score_opus":0.005302357024908943,"score_gpt":0.16561659138655435,"score_spread":0.1603142343616454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795869923","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":[0.99297756,0.00003805279,0.0057277237,0.000039698283,0.0000026561668,0.000016323154,0.00020268794,0.000045841673,0.00094948115],"genre_scores_gemma":[0.99892247,0.000019229434,0.0006991378,0.0000022415536,0.0000018710278,0.0000037997431,0.00013563037,0.000009106344,0.00020648408],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99968827,0.00007687421,0.000021301033,0.00006179014,0.0001012162,0.000050527826],"domain_scores_gemma":[0.99871373,0.00071372354,0.00020070423,0.000071704635,0.00024799432,0.000052141462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052023085,0.00038337088,0.00035320348,0.0010452808,0.0002991531,0.0008542117,0.00014116669,0.00025749797,0.0009891028],"category_scores_gemma":[0.0029114087,0.00021231167,0.0002964338,0.0011377383,0.00037439747,0.0007919355,0.000392658,0.00027723738,0.00020225724],"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":[0.00046769256,0.00017564795,0.7143966,0.00012988338,0.00021871792,0.0003578549,0.0005893389,0.16046563,0.03488525,0.00088546047,0.0010079279,0.08641993],"study_design_scores_gemma":[0.0000051349552,0.00015870258,0.8404279,0.000011993643,0.000037892318,0.000086263695,0.00068491604,0.15150605,0.005566329,0.00068590115,0.00080127246,0.000027751234],"about_ca_topic_score_codex":0.006752597,"about_ca_topic_score_gemma":0.01033156,"teacher_disagreement_score":0.006752597,"about_ca_system_score_codex":0.00041177703,"about_ca_system_score_gemma":0.00039015766,"threshold_uncertainty_score":0.013426602},"labels":[],"label_agreement":null},{"id":"W2800216870","doi":"10.2166/wst.2018.213","title":"Modulation of transient pressure by an air pocket in a horizontal pipe with an end orifice","year":2018,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Body orifice; Water hammer; Mechanics; Cabin pressurization; Pipe flow; Orifice plate; Flow (mathematics); Inlet; Transient (computer programming); Materials science; Geotechnical engineering; Geology; Engineering; Mechanical engineering; Composite material; Turbulence; Physics","score_opus":0.0042766232977394,"score_gpt":0.18845304359514095,"score_spread":0.18417642029740156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800216870","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":[0.9795696,0.00009940479,0.018618157,0.00004554778,0.000008705589,0.000015520603,0.000028784543,0.000093963805,0.0015203328],"genre_scores_gemma":[0.9981622,0.000028725415,0.0015758914,0.0000026558241,0.0000017687504,0.0000052683654,0.0000064838223,0.000004175095,0.00021282691],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999201,0.000013622151,0.0000038987305,0.00001524128,0.000020471201,0.000026617006],"domain_scores_gemma":[0.9997546,0.00013675503,0.000059239985,0.000017037002,0.000016891108,0.000015493975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016449056,0.00020384054,0.00023968183,0.0001780469,0.00020908196,0.00032554538,0.00021528106,0.00040719798,0.0008117561],"category_scores_gemma":[0.0004888538,0.00016424715,0.000260118,0.0001298248,0.0006234566,0.00031566524,0.00030494484,0.00026121474,0.0000536972],"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":[0.0005462357,0.00026776007,0.018692872,0.0003341302,0.000041529172,0.0014495999,0.0003594995,0.46690956,0.48367536,0.0033952196,0.0002955665,0.02403273],"study_design_scores_gemma":[0.000041123305,0.0003566933,0.013503967,0.00000817548,0.00001948866,0.0001245435,0.00010031545,0.94650894,0.038449626,0.00040869118,0.00046038686,0.00001800654],"about_ca_topic_score_codex":0.0013903606,"about_ca_topic_score_gemma":0.0007092604,"teacher_disagreement_score":0.0013903606,"about_ca_system_score_codex":0.00017318923,"about_ca_system_score_gemma":0.00024836836,"threshold_uncertainty_score":0.0027644634},"labels":[],"label_agreement":null},{"id":"W2803893193","doi":"10.1371/journal.pone.0196887","title":"Analytical scaling relations to evaluate leakage and intrusion in intermittent water supply systems","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Water Systems and Optimization","field":"Engineering","cited_by":28,"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; Tata Center for Technology and Design, Massachusetts Institute of Technology; World Bank Group","keywords":"Flushing; Water supply; Environmental science; Water quality; Leakage (economics); Environmental engineering; Intrusion; Engineering; Geology","score_opus":0.029241208997395195,"score_gpt":0.21627661100657333,"score_spread":0.18703540200917815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803893193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27643785,0.0016454749,0.702055,0.00079517165,0.00010816433,0.0004504969,0.00086034666,0.0019041972,0.015743293],"genre_scores_gemma":[0.9526132,0.000440396,0.045129567,0.000061158695,0.000037026908,0.00017358607,0.00034207082,0.00009874426,0.0011042071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821657,0.0005419034,0.00017702069,0.0002168179,0.0006892605,0.00015832856],"domain_scores_gemma":[0.99265856,0.0042503984,0.0013283143,0.0004785535,0.0011543874,0.00012989051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026300799,0.00096817745,0.0006482157,0.0027981743,0.00053086865,0.0014782251,0.0008163354,0.0008509041,0.0018326646],"category_scores_gemma":[0.019167915,0.00031862623,0.0009782853,0.0027284499,0.0007975944,0.002521988,0.001030896,0.00082092895,0.00040796533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007215638,0.00016637749,0.026214192,0.00027442287,0.00008032066,0.00028820828,0.00057485804,0.88647467,0.0046034935,0.018974269,0.0017456984,0.060531374],"study_design_scores_gemma":[0.00000383504,0.000079189,0.004514999,0.000023965846,0.000011866851,0.00008173437,0.00017361714,0.9884317,0.0006993175,0.004950663,0.0010074009,0.000021806673],"about_ca_topic_score_codex":0.0071563893,"about_ca_topic_score_gemma":0.0027293658,"teacher_disagreement_score":0.0071563893,"about_ca_system_score_codex":0.0014254086,"about_ca_system_score_gemma":0.0008967842,"threshold_uncertainty_score":0.014229476},"labels":[],"label_agreement":null},{"id":"W2808149924","doi":"10.1007/s10661-018-6769-1","title":"Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches","year":2018,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Artificial neural network; Support vector machine; Perceptron; Residual; Multilayer perceptron; Data mining; Mean squared error; Artificial intelligence; Computer science; Machine learning; Statistics; Mathematics; Algorithm","score_opus":0.035568965943723226,"score_gpt":0.23423430950262938,"score_spread":0.19866534355890617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808149924","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":[0.9654655,0.00047566526,0.031849124,0.0004975373,0.000022870045,0.00005838946,0.00029097003,0.00020392892,0.001135906],"genre_scores_gemma":[0.98776746,0.00014696103,0.011470941,0.000024257904,0.000011368324,0.000017295883,0.00029078676,0.000007458885,0.0002635471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998882,0.0004974852,0.000106027044,0.00019229983,0.0002040196,0.00011825328],"domain_scores_gemma":[0.992949,0.0051190387,0.000701959,0.00036155054,0.00072372856,0.0001447195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003759053,0.0009686619,0.0007042412,0.0017320927,0.00041934996,0.0015287902,0.00074971607,0.00089490844,0.00037765913],"category_scores_gemma":[0.008988801,0.00031172595,0.00081496674,0.0010237108,0.00029765154,0.001605278,0.00047945284,0.00056405476,0.0000799897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087141176,0.0009816401,0.06650906,0.00019292145,0.0005062335,0.00010517404,0.000088841705,0.8327272,0.005549301,0.001276926,0.0007078611,0.090483524],"study_design_scores_gemma":[0.000007929247,0.00012210794,0.0038512226,0.000005037109,0.000040848452,0.0000124262615,0.000057907,0.992708,0.0025862905,0.0004957742,0.000107689404,0.0000048321576],"about_ca_topic_score_codex":0.014385859,"about_ca_topic_score_gemma":0.008942572,"teacher_disagreement_score":0.014385859,"about_ca_system_score_codex":0.0011945058,"about_ca_system_score_gemma":0.0012272396,"threshold_uncertainty_score":0.02860421},"labels":[],"label_agreement":null},{"id":"W2883373216","doi":"10.1002/awwa.1089","title":"Examining the Energy Performance Associated With Typical Pipe Unit Head Loss Thresholds","year":2018,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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":"Queen's University; Jacobs (Canada)","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Hydraulic head; Head (geology); Mains electricity; Energy (signal processing); Friction loss; Pipe network analysis; Environmental science; Flow (mathematics); Marine engineering; Engineering; Mechanics; Mechanical engineering; Geotechnical engineering; Geology; Mathematics; Statistics; Electrical engineering; Physics","score_opus":0.010632888772811645,"score_gpt":0.19066248475052322,"score_spread":0.18002959597771157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883373216","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":[0.9959558,0.00004487587,0.0028725006,0.000025355592,0.000002465766,0.000011145024,0.00027850393,0.000062004634,0.0007472507],"genre_scores_gemma":[0.9990324,0.000017054464,0.00039326175,0.0000037084826,0.0000013557105,0.000005734177,0.00036280023,0.000009185851,0.00017455479],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983701,0.000350338,0.00013320583,0.0002914376,0.00054714794,0.0003078315],"domain_scores_gemma":[0.99023074,0.004955577,0.0021741404,0.0007490455,0.0016267091,0.00026380297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023668455,0.00046269002,0.0004396935,0.0018599877,0.00024508682,0.00077657786,0.0004289643,0.00059963984,0.0008260643],"category_scores_gemma":[0.011433253,0.00022878354,0.00039352267,0.0015776017,0.00054711703,0.001584507,0.0006597033,0.00041051285,0.00025501102],"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":[0.0009192318,0.0002731513,0.70900327,0.00015208698,0.0001973693,0.00040585938,0.00040323782,0.2325076,0.020345682,0.0008606994,0.0008718904,0.034059927],"study_design_scores_gemma":[0.000013381183,0.001044383,0.8589187,0.00002472697,0.00004744729,0.00034655127,0.0007897737,0.11624915,0.020554677,0.0009030783,0.0010406596,0.00006741414],"about_ca_topic_score_codex":0.0037743987,"about_ca_topic_score_gemma":0.004918329,"teacher_disagreement_score":0.0037743987,"about_ca_system_score_codex":0.0008500411,"about_ca_system_score_gemma":0.00034032244,"threshold_uncertainty_score":0.012517214},"labels":[],"label_agreement":null},{"id":"W2883636325","doi":"10.3390/w10070939","title":"Developing a Statistical Model to Improve Drinking Water Quality for Water Distribution System by Minimizing Heavy Metal Releases","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Water quality; Nonlinear system; Water source; Computer science; Surface water; Mathematical optimization; Environmental science; Process engineering; Environmental engineering; Engineering; Mathematics; Chemistry","score_opus":0.023464815837789147,"score_gpt":0.2552814689067707,"score_spread":0.23181665306898158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883636325","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":[0.015021147,0.00014552644,0.9823318,0.00017606265,0.000022550852,0.00004391001,0.000060589227,0.00022210435,0.0019763513],"genre_scores_gemma":[0.85122275,0.0005873449,0.141006,0.00015199755,0.00005674768,0.00047282907,0.00029648317,0.00014843787,0.0060574207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994273,0.00014003784,0.00002952839,0.00014119069,0.0001928766,0.00006900498],"domain_scores_gemma":[0.99945635,0.0002567277,0.00009212849,0.00002206317,0.0001533683,0.000019391347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010106484,0.00092243723,0.00085779856,0.0006868195,0.0003659373,0.0009212304,0.0010981828,0.0010062357,0.0012803472],"category_scores_gemma":[0.0015792917,0.00060053123,0.001041643,0.00077887793,0.00052222505,0.0012288451,0.00072706275,0.0009209105,0.00026239562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049225036,0.000009354178,0.00021886641,0.000013568596,0.000008430897,0.0000090756885,0.000005777932,0.99453306,0.00047757267,0.0011639199,0.00008071811,0.0034747252],"study_design_scores_gemma":[9.803373e-7,0.000005897847,0.000038113896,6.436116e-7,0.0000021213466,0.0000014478998,0.0000014014907,0.99944514,0.00012321492,0.0002894015,0.000090183974,0.0000013570196],"about_ca_topic_score_codex":0.012261182,"about_ca_topic_score_gemma":0.00891124,"teacher_disagreement_score":0.012261182,"about_ca_system_score_codex":0.0010843782,"about_ca_system_score_gemma":0.0019809608,"threshold_uncertainty_score":0.024379611},"labels":[],"label_agreement":null},{"id":"W2885857506","doi":"10.1016/j.jher.2018.08.002","title":"Assessment of different MOEAs for rehabilitation evaluation of Urban Stormwater Drainage Systems – Case study: Eastern catchment of Tehran","year":2018,"lang":"en","type":"article","venue":"Journal of Hydro-environment Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Stormwater; Sorting; Drainage; Drainage basin; Environmental science; Storm Water Management Model; Civil engineering; Computer science; Water resource management; Engineering; Surface runoff; Geography; Algorithm; Ecology","score_opus":0.054958199404925114,"score_gpt":0.35482168223707966,"score_spread":0.2998634828321545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885857506","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":[0.9925155,0.00013221987,0.005666411,0.000044494707,0.000007291691,0.000068578884,0.00007685752,0.00006249355,0.0014261735],"genre_scores_gemma":[0.99661607,0.000043594555,0.0029043988,0.0000029363432,0.0000021721557,0.00001890782,0.00006882768,0.000006399737,0.00033668536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908113,0.0002872096,0.000059265887,0.00009526172,0.0002611365,0.00021584387],"domain_scores_gemma":[0.99892586,0.00042590615,0.00010915052,0.000055958604,0.00038239945,0.000100725105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023510028,0.00083440664,0.0010477449,0.0023138884,0.0006715069,0.0013643411,0.00092037715,0.0007743111,0.0007594122],"category_scores_gemma":[0.0022288614,0.00028726624,0.0010575987,0.0014238518,0.00041165733,0.0007853261,0.0005769298,0.00034598503,0.00006161716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061452616,0.00073672214,0.05136679,0.00019675387,0.00025822068,0.00031435693,0.00023463866,0.8750583,0.008092162,0.0014256963,0.0004783593,0.061223518],"study_design_scores_gemma":[0.0000702304,0.0015766934,0.06709691,0.00003030498,0.000222036,0.00008237598,0.001158156,0.92158514,0.0063520507,0.0008686344,0.00092318194,0.000034309072],"about_ca_topic_score_codex":0.02564873,"about_ca_topic_score_gemma":0.03582283,"teacher_disagreement_score":0.02564873,"about_ca_system_score_codex":0.0025351113,"about_ca_system_score_gemma":0.0017323262,"threshold_uncertainty_score":0.050998926},"labels":[],"label_agreement":null},{"id":"W2892235361","doi":"10.4236/jpee.2018.69003","title":"Optimization of Temperature Drop Formula for Annular Gathering Pipeline in High Water Cut Oil Region of Central Hebei Province","year":2018,"lang":"en","type":"article","venue":"Journal of Power and Energy Engineering","topic":"Water Systems and 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":"Ministry of Agriculture","funders":"","keywords":"Drop (telecommunication); Mechanics; Mixing (physics); Pressure drop; Pipeline transport; Thermodynamics; Pipeline (software); Diagram; Flow (mathematics); Inverse; Volumetric flow rate; Heat transfer; Petroleum engineering; Mathematics; Environmental science; Engineering; Mechanical engineering; Physics; Statistics; Geometry","score_opus":0.0028318330375923357,"score_gpt":0.1614032944951482,"score_spread":0.15857146145755588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892235361","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":[0.8524062,0.00025015694,0.13969396,0.00019260224,0.000027832215,0.000067879024,0.0001996663,0.00025024856,0.006911459],"genre_scores_gemma":[0.99112636,0.00007826252,0.0077030584,0.000006518385,0.000002438043,0.00002234073,0.00007739286,0.000022183578,0.0009613532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998659,0.000015075342,0.000007228815,0.000031525426,0.000042516625,0.000037615588],"domain_scores_gemma":[0.9999026,0.000025098294,0.000011537163,0.0000039434485,0.000049206432,0.000007605518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032040762,0.00034144198,0.0003842954,0.00050205167,0.00045293607,0.00070667296,0.00043459365,0.00039118872,0.0008848032],"category_scores_gemma":[0.0004956243,0.00024564943,0.0004427662,0.0004130912,0.00026031473,0.00038678525,0.00028541996,0.00026029485,0.00010116833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001255797,0.000079968384,0.009125573,0.00015128645,0.000018257722,0.00028417874,0.00014052715,0.92025423,0.0384111,0.0021606698,0.00097343937,0.028275164],"study_design_scores_gemma":[0.000023681845,0.000059527127,0.004722421,0.000004121855,0.000016956948,0.000026278543,0.0000999057,0.9860722,0.008037746,0.00043335333,0.0004918018,0.00001202243],"about_ca_topic_score_codex":0.037970603,"about_ca_topic_score_gemma":0.029498268,"teacher_disagreement_score":0.037970603,"about_ca_system_score_codex":0.0011319115,"about_ca_system_score_gemma":0.0013744439,"threshold_uncertainty_score":0.07549918},"labels":[],"label_agreement":null},{"id":"W2896032897","doi":"10.2166/wst.2018.436","title":"Mitigating peak pressure of storm geysering by orifice plates installed at the top of vent pipes","year":2018,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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 Alberta","funders":"","keywords":"Body orifice; Environmental science; Orifice plate; Head (geology); Nominal Pipe Size; Airflow; Outflow; Meteorology; Engineering; Geology; Materials science; Mechanical engineering","score_opus":0.0040727292810391,"score_gpt":0.18683519776320737,"score_spread":0.18276246848216826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896032897","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":[0.9721196,0.00016979598,0.026580269,0.000028828572,0.000014000889,0.000021738857,0.000033394244,0.00031423013,0.0007181535],"genre_scores_gemma":[0.99564725,0.0000908011,0.003938146,0.0000041638323,0.0000024941562,0.000007629273,0.000016845086,0.000008302556,0.00028432783],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998646,0.000018403925,0.00000595785,0.00002169522,0.000056069766,0.000033307348],"domain_scores_gemma":[0.99977297,0.00008348794,0.000058810278,0.000023649998,0.000040887546,0.000020217924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021039744,0.0003754996,0.00037349891,0.00019947828,0.00016888272,0.00033733834,0.0004653601,0.0003081791,0.00064406113],"category_scores_gemma":[0.00050013134,0.0002567723,0.0003031332,0.0001609428,0.00030727254,0.0003696642,0.00043377787,0.0003606956,0.00009063163],"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":[0.00035326736,0.00011465626,0.0031795658,0.00022672945,0.000020713458,0.00016126591,0.00004984092,0.051133372,0.9256538,0.00021300299,0.00017405036,0.018719738],"study_design_scores_gemma":[0.00007364556,0.0015198049,0.01738992,0.0000136388535,0.00007939704,0.00009928464,0.000089556364,0.22419386,0.755073,0.0001681528,0.0012642877,0.00003545918],"about_ca_topic_score_codex":0.0016030311,"about_ca_topic_score_gemma":0.0017789259,"teacher_disagreement_score":0.0016030311,"about_ca_system_score_codex":0.00017756372,"about_ca_system_score_gemma":0.0003771301,"threshold_uncertainty_score":0.0031873584},"labels":[],"label_agreement":null},{"id":"W2897099175","doi":"10.1108/ec-11-2017-0459","title":"Influence of velocity head on filling transients in a branched pipeline","year":2018,"lang":"en","type":"article","venue":"Engineering Computations","topic":"Water Systems and 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":"HydraTek (Canada); University of Toronto","funders":"","keywords":"Hydraulic head; Mechanics; Head (geology); Pipeline (software); Overpressure; Flow velocity; Flow (mathematics); Energy (signal processing); Geology; Engineering; Geotechnical engineering; Physics; Mechanical engineering; Thermodynamics","score_opus":0.008846345117607972,"score_gpt":0.21494769161325739,"score_spread":0.2061013464956494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897099175","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":[0.97085786,0.00010891303,0.027617522,0.000048560454,0.00001052057,0.000008401604,0.00002323355,0.00011296912,0.0012120852],"genre_scores_gemma":[0.99911076,0.000025730982,0.00067628577,0.0000029608934,7.992518e-7,0.0000018888876,0.0000070524,0.000004985254,0.00016953483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998988,0.000018464012,0.000005302079,0.000015685275,0.000033964337,0.000027855403],"domain_scores_gemma":[0.9995303,0.0002947582,0.000088909714,0.000019132462,0.000045139226,0.000021822949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001979823,0.00020945148,0.00022234829,0.00022483314,0.00025175,0.0003278074,0.00019418038,0.000286318,0.00071151304],"category_scores_gemma":[0.0010047312,0.0001096714,0.00019774081,0.00018020817,0.00049314107,0.00035882025,0.0003003356,0.00022951765,0.00005557223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005329263,0.00010186195,0.014563266,0.00019593777,0.000021536276,0.0011218052,0.00032858417,0.7028835,0.24905369,0.0026826088,0.00022346749,0.028290793],"study_design_scores_gemma":[0.000009390941,0.00027199896,0.006322998,0.000011144831,0.000015976175,0.00010794048,0.000110127694,0.9112685,0.08101637,0.00051495695,0.00033268053,0.00001800048],"about_ca_topic_score_codex":0.0023982818,"about_ca_topic_score_gemma":0.0013092346,"teacher_disagreement_score":0.0023982818,"about_ca_system_score_codex":0.00036837926,"about_ca_system_score_gemma":0.00036217846,"threshold_uncertainty_score":0.00476861},"labels":[],"label_agreement":null},{"id":"W2898853920","doi":"10.1002/awwa.1163","title":"Distortions From a Simplified Approach to Fatigue Analysis in PVC Pipes","year":2018,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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; Hudbay Minerals (Canada); Hydro One (Canada)","funders":"","keywords":"Transient (computer programming); Transient analysis; Work (physics); Distortion (music); Pipeline (software); Structural engineering; Devaluation; Computer science; Transient response; Engineering; Mechanical engineering","score_opus":0.007740696567920735,"score_gpt":0.2076810919648802,"score_spread":0.19994039539695946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898853920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28174534,0.0002937768,0.7076431,0.00030051902,0.000029891164,0.00015583615,0.00028660006,0.0006926128,0.008852336],"genre_scores_gemma":[0.9066182,0.00033080456,0.09066764,0.00007477869,0.000018833374,0.00014161639,0.00015426504,0.00010419954,0.0018896993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987205,0.00033565704,0.00006385613,0.000103315666,0.00070199894,0.00007471251],"domain_scores_gemma":[0.99799156,0.0009287202,0.00019794235,0.00035885448,0.00050486665,0.000017956214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012985392,0.0004779074,0.0007440804,0.0007547285,0.00047504192,0.00063661486,0.00092578266,0.00060710625,0.0013964296],"category_scores_gemma":[0.00519285,0.0004881602,0.00068985706,0.00039341894,0.0007226143,0.0011487057,0.0005304999,0.00081983284,0.00034048242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001163956,0.000063031905,0.0043292865,0.00018403177,0.000032893597,0.00029376015,0.00046550095,0.8815849,0.04624324,0.012490778,0.0009763941,0.053219885],"study_design_scores_gemma":[0.000011690046,0.00012371944,0.0042756745,0.000023095909,0.000017047876,0.0001543009,0.000076385135,0.9824065,0.0074627064,0.0032255498,0.0021891724,0.000034184064],"about_ca_topic_score_codex":0.011830239,"about_ca_topic_score_gemma":0.0135366125,"teacher_disagreement_score":0.011830239,"about_ca_system_score_codex":0.0010508333,"about_ca_system_score_gemma":0.0007930223,"threshold_uncertainty_score":0.023522735},"labels":[],"label_agreement":null},{"id":"W2899052523","doi":"10.5194/dwes-12-1-2019","title":"Analysis of water distribution network under pressure-deficient conditions through emitter setting","year":2019,"lang":"en","type":"article","venue":"Drinking water engineering and science","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Benchmark (surveying); Software; Replicate; Relation (database); Pressure head; Computer science; Pipe network analysis; Mathematical optimization; Simulation; Engineering; Reliability engineering; Mechanical engineering; Mathematics; Mechanics; Data mining; Geology","score_opus":0.004552320724292356,"score_gpt":0.18699859698681418,"score_spread":0.18244627626252183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899052523","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":[0.87628984,0.00015374816,0.115392976,0.00019141093,0.000016480892,0.000052829317,0.0004093598,0.00041394908,0.007079285],"genre_scores_gemma":[0.99563545,0.000045145513,0.003655345,0.0000064576843,0.000002066921,0.000010736076,0.000086823624,0.000012158597,0.00054583815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998504,0.000038902046,0.0000047738,0.00003130476,0.000043311542,0.000031368807],"domain_scores_gemma":[0.99958426,0.0002088779,0.00005423802,0.000022825228,0.00010202755,0.000027847704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000341638,0.00039635162,0.00028898916,0.0007893254,0.0003195769,0.0006586412,0.0005399774,0.0006233681,0.0012009712],"category_scores_gemma":[0.0012625223,0.00022970988,0.00037535987,0.00047547297,0.00043902048,0.0008107689,0.00037313055,0.00033044055,0.0000792446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038076854,0.000019878153,0.004922012,0.000036016147,0.000011446021,0.00017774847,0.000047677797,0.9854268,0.0036093635,0.0016183867,0.00017559004,0.0039170375],"study_design_scores_gemma":[0.0000018298792,0.0000105092795,0.00067373214,0.0000018520743,0.0000031927448,0.000013707651,0.000027027527,0.99808997,0.0007651923,0.0003201047,0.00009033515,0.0000025756603],"about_ca_topic_score_codex":0.010887682,"about_ca_topic_score_gemma":0.0059686373,"teacher_disagreement_score":0.010887682,"about_ca_system_score_codex":0.0008322928,"about_ca_system_score_gemma":0.00041151742,"threshold_uncertainty_score":0.021648645},"labels":[],"label_agreement":null},{"id":"W2899096501","doi":"10.1139/cjce-2018-0229","title":"Development of a clustering-based model for enhancing acoustic leak detection","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Leak detection; Leak; Cluster analysis; Environmental science; Computer science; Water pipe; Leakage (economics); Wastewater; Engineering; Environmental engineering; Artificial intelligence","score_opus":0.010371027480866159,"score_gpt":0.17792863665798067,"score_spread":0.1675576091771145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899096501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02459968,0.00024899744,0.9710477,0.0002104064,0.00004991074,0.0000709213,0.00014410276,0.00063080067,0.0029974903],"genre_scores_gemma":[0.8595828,0.000361099,0.13324685,0.00012001198,0.000047381876,0.00026295552,0.00039028548,0.00014198528,0.005846611],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996463,0.000071756134,0.000022543974,0.00010384726,0.00010006744,0.0000554792],"domain_scores_gemma":[0.99903023,0.00045578144,0.00008606496,0.000033867065,0.00036304985,0.000031027055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008660297,0.00083812315,0.0008249277,0.0006587574,0.00047493578,0.0009859345,0.0017256836,0.0014622825,0.0013922228],"category_scores_gemma":[0.002436436,0.00050872937,0.0008272396,0.0005200226,0.00049138354,0.000763956,0.00075390475,0.0012914274,0.00048305548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020326119,0.00001578125,0.00035344178,0.000021544713,0.000012720391,0.00002059296,0.000022154058,0.99051464,0.0010385022,0.0010399532,0.00023590418,0.0067044036],"study_design_scores_gemma":[8.126873e-7,0.000003864987,0.000047072564,0.0000015531022,0.0000019923825,0.0000022872555,0.000001708929,0.9995074,0.00016218112,0.00018695473,0.00008187724,0.000002223193],"about_ca_topic_score_codex":0.03844335,"about_ca_topic_score_gemma":0.020267645,"teacher_disagreement_score":0.03844335,"about_ca_system_score_codex":0.0011815813,"about_ca_system_score_gemma":0.0013424801,"threshold_uncertainty_score":0.0764392},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2899622522","doi":"10.1115/ipc2018-78791","title":"Optimization of Averaging Window Length and Alarming Hold Time in Volume Balance RTTM Leak Detection to Minimize False Alarms and Spill Volume","year":2018,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and 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":"TransCanada (Canada)","funders":"","keywords":"False positive paradox; Leak; Computer science; False alarm; Sensitivity (control systems); Pipeline (software); Thresholding; Volume (thermodynamics); Transient (computer programming); Reliability (semiconductor); Real-time computing; Set (abstract data type); Data mining; Reliability engineering; Engineering; Artificial intelligence; Power (physics)","score_opus":0.0060832246308891195,"score_gpt":0.19223422543614063,"score_spread":0.18615100080525152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899622522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16972074,0.0018753225,0.82177037,0.00025790016,0.00013564058,0.0001982661,0.00019394886,0.0028946332,0.0029531876],"genre_scores_gemma":[0.8395821,0.00040429138,0.15743318,0.00011449739,0.00006542631,0.00019643633,0.00026118616,0.00022469064,0.0017181899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944323,0.00006667435,0.000051338702,0.00016232327,0.00018299573,0.000093430586],"domain_scores_gemma":[0.9987237,0.00046054675,0.00031951012,0.00007385168,0.00030926458,0.000113034504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073826674,0.001203075,0.0008665786,0.0010589128,0.00043745784,0.0008940571,0.0010875056,0.00067933445,0.0022331392],"category_scores_gemma":[0.0033341201,0.00028101786,0.00047505443,0.00057867443,0.00035481263,0.001215139,0.00060315983,0.00064132566,0.0005648712],"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":[0.0019132523,0.00067202,0.010143522,0.00074383913,0.0001411702,0.0008234876,0.00034267345,0.29238042,0.24966402,0.004273477,0.004792064,0.43411002],"study_design_scores_gemma":[0.000108681095,0.0012760716,0.010166194,0.000064306114,0.00022210197,0.00046328493,0.00018016735,0.9018268,0.078951314,0.002745158,0.0039135646,0.000082379556],"about_ca_topic_score_codex":0.0021545803,"about_ca_topic_score_gemma":0.0020399196,"teacher_disagreement_score":0.0022331392,"about_ca_system_score_codex":0.00047534407,"about_ca_system_score_gemma":0.0010056951,"threshold_uncertainty_score":0.0074706078},"labels":[],"label_agreement":null},{"id":"W2899904669","doi":"10.1115/ipc2018-78426","title":"Pipeline Rupture Detection Using Real-Time Transient Modelling and Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and 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":"TransCanada (Canada); University of Calgary","funders":"","keywords":"Computer science; Pipeline (software); Leak; Pipeline transport; Transient (computer programming); False alarm; Upgrade; Real-time computing; Convolutional neural network; Constant false alarm rate; Reliability (semiconductor); Reliability engineering; Preprocessor; Engineering; Artificial intelligence","score_opus":0.012576288563986734,"score_gpt":0.20537802769754962,"score_spread":0.19280173913356288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899904669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12017752,0.0002956285,0.87586623,0.0002234351,0.000035013378,0.000027039196,0.00012477401,0.0016948757,0.0015555093],"genre_scores_gemma":[0.96407527,0.0001474989,0.03393762,0.0000341396,0.000011132661,0.000037480502,0.00016581833,0.000040628944,0.0015503054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998357,0.00003134003,0.000010777236,0.00004802976,0.00004367558,0.000030466808],"domain_scores_gemma":[0.99948573,0.00023961188,0.0001244491,0.000035889258,0.00009081605,0.000023529836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041594636,0.0007473279,0.00042077593,0.0006088725,0.00022569527,0.00068047136,0.00082663324,0.0008056448,0.0008176632],"category_scores_gemma":[0.0015701476,0.00052151177,0.0007078278,0.0004383572,0.00036921527,0.0008588802,0.00051089353,0.00080338126,0.00018470177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006253648,0.00002923466,0.0010991268,0.000019197934,0.000022359738,0.000026798594,0.000012810334,0.9797324,0.0023667717,0.0005473567,0.00017280153,0.015908556],"study_design_scores_gemma":[4.1053903e-7,0.0000033512508,0.00008496067,6.157656e-7,0.0000013486292,0.0000016480913,6.430708e-7,0.9995009,0.00029189183,0.000091027265,0.000021916074,0.0000012701257],"about_ca_topic_score_codex":0.021870326,"about_ca_topic_score_gemma":0.015969504,"teacher_disagreement_score":0.021870326,"about_ca_system_score_codex":0.0014479499,"about_ca_system_score_gemma":0.00089483516,"threshold_uncertainty_score":0.04348606},"labels":[],"label_agreement":null},{"id":"W2900019230","doi":"10.1115/ipc2018-78717","title":"Liquid Pipeline Location Specific Cyclic Pressure Determination","year":2018,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"","keywords":"SCADA; Pipeline (software); Pipeline transport; Pressure measurement; Pressure sensor; Marine engineering; Environmental science; Computer science; Engineering; Mechanical engineering; Electrical engineering","score_opus":0.008902655455380678,"score_gpt":0.2002387943632706,"score_spread":0.19133613890788992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900019230","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":[0.56598616,0.0002673803,0.40514043,0.00010537468,0.00004278093,0.00042645668,0.00972996,0.00591742,0.012384055],"genre_scores_gemma":[0.9573419,0.00014926118,0.037170198,0.000020914204,0.000012454212,0.00013401856,0.0025372596,0.00010223147,0.0025318295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954766,0.000021831944,0.00002390669,0.00012719404,0.00023950107,0.000039833336],"domain_scores_gemma":[0.9994301,0.000080368816,0.00012217557,0.00006772252,0.00027320586,0.000026447075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002087903,0.0004871654,0.00036418674,0.0018682345,0.00030730065,0.00064071285,0.00088075275,0.00043315784,0.0026225257],"category_scores_gemma":[0.001367596,0.00029157932,0.00030638237,0.0017985153,0.00017842521,0.0008049499,0.0004839129,0.0004366217,0.0006947],"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":[0.00022779073,0.00017756144,0.17073245,0.0004917158,0.000086372456,0.0008074585,0.00067691575,0.3060837,0.11480506,0.0023269912,0.006149905,0.3974341],"study_design_scores_gemma":[0.000011830556,0.00015862868,0.14552295,0.000029913457,0.00003208912,0.00025197887,0.00021943742,0.7996567,0.046184808,0.00070381456,0.007148544,0.00007937832],"about_ca_topic_score_codex":0.025280837,"about_ca_topic_score_gemma":0.030875098,"teacher_disagreement_score":0.025280837,"about_ca_system_score_codex":0.00084326806,"about_ca_system_score_gemma":0.0009417757,"threshold_uncertainty_score":0.05026734},"labels":[],"label_agreement":null},{"id":"W2900114947","doi":"10.1115/ipc2018-78740","title":"Flood Monitoring: Evaluating Action Response Time Relative to Warning Time","year":2018,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and 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":"BGC Engineering (Canada)","funders":"","keywords":"Flood myth; Warning system; Risk analysis (engineering); Pipeline (software); Computer science; Flooding (psychology); Pipeline transport; Environmental science; Reliability engineering; Engineering; Business; Environmental engineering; Geography","score_opus":0.02213681431283803,"score_gpt":0.2682893159283865,"score_spread":0.24615250161554847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900114947","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":[0.97506297,0.0007442251,0.019097812,0.00007971326,0.000082162245,0.00068172126,0.00074333843,0.00023757096,0.0032705332],"genre_scores_gemma":[0.9862386,0.00022343428,0.011808593,0.000039452978,0.000023831124,0.0004375382,0.0005219535,0.000028714507,0.0006778705],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964018,0.0012065438,0.0004654587,0.000528591,0.0011915993,0.00020598446],"domain_scores_gemma":[0.9610335,0.027587084,0.006521768,0.0008112119,0.0030350538,0.0010113021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061372607,0.0008477257,0.00054589857,0.0020343561,0.00027367965,0.0009370713,0.0006235924,0.0007087421,0.0018589002],"category_scores_gemma":[0.03324829,0.00024730715,0.0007380353,0.0011409319,0.00035981502,0.0013809353,0.00057050574,0.0007305148,0.00032000372],"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":[0.020765603,0.002489262,0.5863677,0.0012473165,0.0013411692,0.00031407241,0.001645523,0.0911714,0.036204662,0.0022769042,0.0018213791,0.254355],"study_design_scores_gemma":[0.0005065186,0.035538226,0.7277485,0.00024165965,0.0012584748,0.0005001424,0.0019200395,0.19776943,0.02725839,0.0020333263,0.0049627894,0.00026241314],"about_ca_topic_score_codex":0.0033535906,"about_ca_topic_score_gemma":0.0024281219,"teacher_disagreement_score":0.0061372607,"about_ca_system_score_codex":0.0006963026,"about_ca_system_score_gemma":0.0007507771,"threshold_uncertainty_score":0.03245735},"labels":[],"label_agreement":null},{"id":"W2900617911","doi":"10.1139/cjce-2018-0137","title":"Testing evolutionary algorithms for optimization of water distribution networks","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; CMA-ES; Computer science; Optimization problem; Particle swarm optimization; Evolutionary algorithm; Heuristic; Algorithm; Global optimization; Local search (optimization); Local optimum; Nonlinear system; Metaheuristic; Evolution strategy; Mathematics","score_opus":0.00938227456744106,"score_gpt":0.17136179265194054,"score_spread":0.16197951808449948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900617911","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":[0.55819654,0.002105035,0.42726535,0.00067247177,0.00013094625,0.00030573242,0.00039197225,0.000631674,0.010300185],"genre_scores_gemma":[0.8885592,0.0004764067,0.10917143,0.00008700707,0.00002976331,0.00028925404,0.00037272772,0.00006809337,0.00094600924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977482,0.0013629517,0.00012626797,0.00020453101,0.00040239783,0.00015560936],"domain_scores_gemma":[0.97721523,0.02059784,0.0006518539,0.00051793363,0.0008880948,0.00012894467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004382992,0.001563365,0.0011327362,0.0015397092,0.00046234502,0.0008915404,0.0012497444,0.0014770546,0.0013145399],"category_scores_gemma":[0.021939728,0.00041947674,0.00088102923,0.0012382634,0.0010636055,0.0010935048,0.0009374079,0.0010517316,0.00014523264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004424184,0.0000662346,0.0012525186,0.000055169075,0.000040071875,0.000013823728,0.000013248195,0.9850761,0.00022947691,0.0010527723,0.00010525976,0.01205108],"study_design_scores_gemma":[0.000014910695,0.00006881045,0.00030518777,0.0000086086475,0.0000059770186,0.000004327453,0.000010252428,0.9983032,0.0003585358,0.0008151238,0.00010234508,0.0000027544897],"about_ca_topic_score_codex":0.005927745,"about_ca_topic_score_gemma":0.002962122,"teacher_disagreement_score":0.005927745,"about_ca_system_score_codex":0.0010152212,"about_ca_system_score_gemma":0.0009970627,"threshold_uncertainty_score":0.02317977},"labels":[],"label_agreement":null},{"id":"W2900731516","doi":"10.1061/(asce)hy.1943-7900.0001552","title":"Discussion of “Finite-Volume Solutions to the Water-Hammer Equations in Conservation Form Incorporating Dynamic Friction Using the Godunov Scheme” by Aboudou Seck, Musandji Fuamba, and René Kahawita","year":2018,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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":"Finite volume method; Water hammer; Godunov's scheme; Conservation law; Hammer; Mechanics; Volume (thermodynamics); Scheme (mathematics); Geotechnical engineering; Mathematics; Geology; Engineering; Physics; Numerical analysis; Structural engineering; Mathematical analysis; Thermodynamics","score_opus":0.01193535020116696,"score_gpt":0.21116468654482337,"score_spread":0.19922933634365642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900731516","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019470392,0.010527625,0.8114114,0.060850006,0.016333451,0.00022552659,0.0002072675,0.000501579,0.08047268],"genre_scores_gemma":[0.5458132,0.008242601,0.32356414,0.017497811,0.010619096,0.00053989707,0.00025404376,0.00075882423,0.092710376],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993069,0.00032733585,0.00003261332,0.000059836988,0.00023039477,0.00004284043],"domain_scores_gemma":[0.9993886,0.0003534852,0.000043340115,0.000056799814,0.00011597376,0.00004187389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002164085,0.00077669584,0.00082191435,0.00060390565,0.0015987403,0.0016908234,0.0029430168,0.0034733736,0.0039437916],"category_scores_gemma":[0.004786657,0.0004672989,0.0014532765,0.00061191624,0.0039270236,0.0033816982,0.0018941317,0.0033663712,0.00049350195],"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":[0.000022624628,0.000019439973,0.000056984612,0.00008110254,0.000012436873,0.00013549918,0.000109589084,0.015238594,0.000930259,0.9699552,0.009070157,0.0043680873],"study_design_scores_gemma":[0.00005679883,0.000067750254,0.00018640341,0.00013024974,0.000020714533,0.00017146202,0.00009043689,0.27157423,0.00228121,0.6405236,0.08484111,0.000055964876],"about_ca_topic_score_codex":0.0028105048,"about_ca_topic_score_gemma":0.0024485295,"teacher_disagreement_score":0.0039437916,"about_ca_system_score_codex":0.0009286533,"about_ca_system_score_gemma":0.0011959956,"threshold_uncertainty_score":0.013193309},"labels":[],"label_agreement":null},{"id":"W2901290654","doi":"10.3390/w10111698","title":"Analysis of Pressure Transient Following Rapid Filling of a Vented Horizontal Pipe","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"National Natural Science Foundation of China","keywords":"Body orifice; Mechanics; Cabin pressurization; Oscillation (cell signaling); Overpressure; Transient (computer programming); Airflow; Fluent; Atmospheric pressure; Orifice plate; Pressure wave; Materials science; Environmental science; Geotechnical engineering; Computational fluid dynamics; Geology; Chemistry; Meteorology; Engineering; Thermodynamics; Physics; Composite material; Mechanical engineering","score_opus":0.006360124613621459,"score_gpt":0.18732082642048717,"score_spread":0.18096070180686571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901290654","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":[0.9701947,0.00012197129,0.027656404,0.00007955632,0.000014419305,0.000023768249,0.000117402946,0.00027383462,0.0015178284],"genre_scores_gemma":[0.9987398,0.000022501137,0.0009382532,0.0000035781588,0.00000172012,0.0000066434245,0.000034398203,0.000007113881,0.00024610863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999094,0.000010825488,0.000005008288,0.000020303154,0.000033662225,0.000020859532],"domain_scores_gemma":[0.9997836,0.00010976347,0.00004276891,0.000013320698,0.00003384784,0.000016749058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020603609,0.00022644755,0.0003353511,0.00037040675,0.00021144963,0.00027682615,0.00030829545,0.00044841733,0.0009039643],"category_scores_gemma":[0.00050276064,0.00014682795,0.00023937353,0.0002536137,0.0003526712,0.00037026903,0.00024292857,0.0003399176,0.00008795794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011222017,0.00042993514,0.044169426,0.0006462464,0.00011790895,0.0044914447,0.00095268275,0.47358,0.40868112,0.0036548371,0.001613972,0.060540237],"study_design_scores_gemma":[0.000006685331,0.0001558222,0.012822905,0.0000056322874,0.000008168713,0.00008397636,0.0001109643,0.97205037,0.0143115735,0.00018058186,0.0002478293,0.000015464224],"about_ca_topic_score_codex":0.0020756787,"about_ca_topic_score_gemma":0.0010743717,"teacher_disagreement_score":0.0020756787,"about_ca_system_score_codex":0.000218469,"about_ca_system_score_gemma":0.00023743416,"threshold_uncertainty_score":0.0041272044},"labels":[],"label_agreement":null},{"id":"W2902779323","doi":"10.1139/cjce-2017-0725","title":"A streamlined energy efficiency performance indicator for water distribution systems: a case study","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Energy performance; Energy (signal processing); Efficient energy use; Computer science; Performance indicator; Reliability engineering; Engineering; Statistics; Mathematics","score_opus":0.005848114151862798,"score_gpt":0.17034312733979665,"score_spread":0.16449501318793386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902779323","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":[0.95257795,0.00014885291,0.040833034,0.00023385623,0.000017189597,0.00022569211,0.00053663406,0.00017142198,0.00525552],"genre_scores_gemma":[0.98177147,0.00014011082,0.01694495,0.000009925869,0.000005041448,0.000058445887,0.00016855473,0.00001447356,0.0008870338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99886864,0.00054272765,0.00005449129,0.00011429367,0.00032928144,0.0000904861],"domain_scores_gemma":[0.9963413,0.0023615453,0.00032093405,0.00026849922,0.00062754273,0.00008013716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017579694,0.00045104977,0.0005064621,0.0011526583,0.0005563664,0.0010814839,0.0007969885,0.0007980825,0.0011888858],"category_scores_gemma":[0.0048034824,0.00033108034,0.00053396024,0.001677443,0.00050540233,0.0014679927,0.0005778378,0.00076097256,0.00011475575],"study_design_candidate":"observational","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":[0.00017609616,0.00037412014,0.0188575,0.00010417176,0.000040325096,0.00072265137,0.00021814347,0.9429724,0.0022621625,0.007341323,0.001128125,0.02580303],"study_design_scores_gemma":[0.00004115357,0.0003753391,0.0055021527,0.000014801712,0.000016682168,0.000080073485,0.00020589426,0.988689,0.0027567046,0.0010494866,0.0012469904,0.000021783537],"about_ca_topic_score_codex":0.021311153,"about_ca_topic_score_gemma":0.021597315,"teacher_disagreement_score":0.97868884,"about_ca_system_score_codex":0.0029941425,"about_ca_system_score_gemma":0.0014225643,"threshold_uncertainty_score":0.042374253},"labels":[],"label_agreement":null},{"id":"W2903027428","doi":"10.1016/j.watres.2018.11.066","title":"A two-time-scale point process model of water main breaks for infrastructure asset management","year":2018,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and 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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Asset management; Markov chain Monte Carlo; Poisson distribution; Computer science; Event (particle physics); Asset (computer security); Scale (ratio); Cumulative distribution function; Statistical model; Renewal theory; Point process; Econometrics; Statistics; Monte Carlo method; Probability density function; Mathematics","score_opus":0.020665811634908632,"score_gpt":0.2893989221295059,"score_spread":0.26873311049459725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903027428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3141923,0.0011303404,0.64452636,0.0038334767,0.00041271184,0.00026767355,0.0024428696,0.0006912997,0.032503005],"genre_scores_gemma":[0.97549427,0.00036793842,0.0071597253,0.000087867294,0.000064599066,0.0001296215,0.00039740838,0.000048260255,0.016250268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994124,0.00020404074,0.0000264089,0.00015571025,0.00007721239,0.0001241383],"domain_scores_gemma":[0.9986363,0.0007675814,0.00014901432,0.00006184586,0.00020594278,0.00017920727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012223654,0.0010813541,0.0019769697,0.00074343075,0.0009538807,0.0024172775,0.0034182535,0.005307312,0.007498726],"category_scores_gemma":[0.0036411053,0.0009212094,0.0014312017,0.0012518425,0.0015166346,0.0024071182,0.0013544764,0.0029625923,0.0006721616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039510585,0.000040863728,0.0004760065,0.00003122345,0.000020350142,0.00011985665,0.000036990674,0.9849161,0.0004070034,0.012298646,0.0004784163,0.0011351102],"study_design_scores_gemma":[0.000011262947,0.000011127431,0.00014382105,0.0000020998223,0.000007574945,0.0000057925063,0.000011809129,0.99810994,0.000034072706,0.0015188826,0.00013709738,0.000006456205],"about_ca_topic_score_codex":0.050973244,"about_ca_topic_score_gemma":0.022517003,"teacher_disagreement_score":0.050973244,"about_ca_system_score_codex":0.0019683973,"about_ca_system_score_gemma":0.0018828587,"threshold_uncertainty_score":0.10135305},"labels":[],"label_agreement":null},{"id":"W2903919019","doi":"10.14796/jwmm.c460","title":"Inverse Optimization based Detection of Leaks from Simulated Pressure in Water Networks, Part 1: Analysis for a Single Leak","year":2018,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Leak; Leak detection; Minification; Computer science; Inverse; Duration (music); Reliability engineering; Engineering; Mathematics; Acoustics; Physics; Environmental engineering","score_opus":0.016476365678593702,"score_gpt":0.19234614612325623,"score_spread":0.17586978044466253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903919019","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":[0.73945105,0.00009823899,0.2586761,0.00009894022,0.0000059196273,0.000049447866,0.00010801963,0.00048076623,0.0010314842],"genre_scores_gemma":[0.97171706,0.00003759161,0.02781852,0.000008720649,0.000001922851,0.000034790868,0.00008255911,0.000029106168,0.00026965045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997532,0.00007483635,0.00001384872,0.00004236136,0.000085117244,0.00003074249],"domain_scores_gemma":[0.99848986,0.001066118,0.00016895606,0.00007705773,0.00016733343,0.000030733994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069591124,0.00046532927,0.00072298833,0.0006206195,0.00018756102,0.0004341421,0.00047852413,0.0006260474,0.00057654467],"category_scores_gemma":[0.0037698383,0.00036455912,0.00047268812,0.00036731263,0.00060079375,0.00070346915,0.00041029355,0.00047541386,0.00006989523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011301408,0.000049593,0.0033743104,0.000066474684,0.000017360584,0.000045891844,0.00003578303,0.97684187,0.008082163,0.00043160268,0.00007146992,0.010870491],"study_design_scores_gemma":[0.000002233603,0.000018056358,0.000894911,0.0000014884965,0.0000021736478,0.000008774976,0.0000054406974,0.9967264,0.0022103628,0.00010827742,0.000018257637,0.0000035685932],"about_ca_topic_score_codex":0.0066564656,"about_ca_topic_score_gemma":0.003724323,"teacher_disagreement_score":0.0066564656,"about_ca_system_score_codex":0.0005102723,"about_ca_system_score_gemma":0.0007436224,"threshold_uncertainty_score":0.01323545},"labels":[],"label_agreement":null},{"id":"W2908623137","doi":"10.1002/aic.16532","title":"Long range pipeline leak detection and localization using discrete observer and support vector machine","year":2019,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Discretization; Control theory (sociology); Linearization; Observer (physics); Mathematics; Partial differential equation; Nonlinear system; State vector; Applied mathematics; Computer science; Mathematical analysis; Physics; Artificial intelligence","score_opus":0.009781508912874937,"score_gpt":0.2027032356732275,"score_spread":0.19292172676035257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908623137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04089103,0.000094684874,0.95790416,0.0000749748,0.00001904844,0.000018893266,0.000009398473,0.00033222878,0.00065562065],"genre_scores_gemma":[0.950352,0.00007121167,0.048633404,0.000018890154,0.000010124429,0.000056708494,0.000029726914,0.000011008614,0.00081696454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997712,0.000063429485,0.000015798983,0.00006192275,0.0000645519,0.000022979626],"domain_scores_gemma":[0.99952316,0.00019799653,0.00010743163,0.000045198434,0.00010524416,0.000020932845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046160168,0.000629577,0.0006335057,0.00024889768,0.00019972419,0.0005099798,0.00049867394,0.000722216,0.0005161348],"category_scores_gemma":[0.0010471307,0.00027556586,0.00042043775,0.00022148374,0.0004546991,0.00071935303,0.00056105264,0.00067313213,0.00010629448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002023192,0.00007949567,0.0014278498,0.0001305667,0.00005143299,0.00010702129,0.00012192737,0.89282465,0.021170868,0.003213848,0.000358182,0.0803119],"study_design_scores_gemma":[0.000003670809,0.000024218401,0.00006773169,0.0000013450235,0.0000021585174,0.0000035557978,0.0000024905064,0.9988079,0.00089442724,0.0001407923,0.00004956198,0.0000021364463],"about_ca_topic_score_codex":0.0027454777,"about_ca_topic_score_gemma":0.0015421449,"teacher_disagreement_score":0.0027454777,"about_ca_system_score_codex":0.0003785688,"about_ca_system_score_gemma":0.00051767786,"threshold_uncertainty_score":0.005458951},"labels":[],"label_agreement":null},{"id":"W2908743399","doi":"10.29007/2k64","title":"Shape Optimization of Hydraulic Structures: an Example of an Optimum Design of a Fish Passage","year":2018,"lang":"en","type":"article","venue":"EPiC series in engineering","topic":"Water Systems and 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":"BGC Engineering (Canada)","funders":"","keywords":"Computer science; Mathematical optimization; Optimization problem; Numerical analysis; Function (biology); Orientation (vector space); Optimal design; A priori and a posteriori; Position (finance); Shape optimization; Algorithm; Mathematics; Finite element method; Engineering; Geometry; Structural engineering","score_opus":0.017013076199943956,"score_gpt":0.20088713026427316,"score_spread":0.1838740540643292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908743399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16108084,0.0002517005,0.8142846,0.00028800228,0.0000711099,0.00022012072,0.00024749475,0.0004462247,0.023109913],"genre_scores_gemma":[0.6808091,0.00021138956,0.3106276,0.000057494606,0.000013580502,0.0003035843,0.00014075378,0.00016039902,0.007676194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996581,0.00007196737,0.000019420177,0.00007081669,0.00013145419,0.000048269812],"domain_scores_gemma":[0.9996643,0.00014197608,0.00003947812,0.000048694976,0.00006582954,0.000039824157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007176641,0.0005574889,0.000591323,0.0005474514,0.00048287504,0.00088698394,0.00060058164,0.0013383535,0.0049497094],"category_scores_gemma":[0.001075949,0.00038588073,0.0008572482,0.00032044336,0.00077837525,0.0004294641,0.0008531437,0.000546923,0.0006154766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007343909,0.000066341,0.0008409442,0.00016175664,0.00002394833,0.00035474473,0.00012121232,0.9503673,0.019156534,0.008799947,0.0004684719,0.019565405],"study_design_scores_gemma":[0.0000629267,0.00047969664,0.0010648753,0.000037042904,0.000043059536,0.0002633092,0.00010120637,0.9707154,0.013141946,0.0052215834,0.008832603,0.000036337515],"about_ca_topic_score_codex":0.0013714449,"about_ca_topic_score_gemma":0.001870644,"teacher_disagreement_score":0.0049497094,"about_ca_system_score_codex":0.00048470328,"about_ca_system_score_gemma":0.0011335327,"threshold_uncertainty_score":0.016558468},"labels":[],"label_agreement":null},{"id":"W2909207995","doi":"10.1016/j.jenvman.2019.01.023","title":"Multi-agent hybrid particle swarm optimization (MAHPSO) for wastewater treatment network planning","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":39,"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 Calgary; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Particle swarm optimization; Metaheuristic; Genetic algorithm; Multi-swarm optimization; Mathematical optimization; Computer science; Network planning and design; Engineering; Artificial intelligence; Machine learning; Mathematics","score_opus":0.010288337691390845,"score_gpt":0.19025195565139316,"score_spread":0.17996361796000232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909207995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06230115,0.00071590417,0.9306327,0.00041384646,0.00017017015,0.00011130185,0.00011872519,0.00024879404,0.0052874307],"genre_scores_gemma":[0.8711027,0.00029490946,0.12396915,0.000115718176,0.00007726259,0.0002593296,0.00013997081,0.000041726737,0.003999363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961156,0.0001910757,0.00001898678,0.000056997655,0.00007617504,0.000045340672],"domain_scores_gemma":[0.9991066,0.0006303906,0.00007857564,0.000028823204,0.00010846625,0.00004709715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010636066,0.0007201861,0.0012276564,0.00061111775,0.00045474424,0.0009918219,0.00090795616,0.0013301078,0.0014741921],"category_scores_gemma":[0.0019214583,0.0006002174,0.00083919003,0.00060265156,0.00041499166,0.0007039386,0.0009137548,0.00088652264,0.00014063787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022237899,0.000020607982,0.00015180491,0.000020105848,0.000019828862,0.000015468588,0.0000076181645,0.99192667,0.00014321155,0.0008054202,0.00019086745,0.006676185],"study_design_scores_gemma":[0.0000031462678,0.000010477383,0.000026671476,0.0000011544851,0.0000025967565,0.0000013099307,0.0000019785477,0.99958795,0.000033319546,0.00027238004,0.000058146623,8.454839e-7],"about_ca_topic_score_codex":0.009208978,"about_ca_topic_score_gemma":0.007871358,"teacher_disagreement_score":0.009208978,"about_ca_system_score_codex":0.0006152678,"about_ca_system_score_gemma":0.0010137303,"threshold_uncertainty_score":0.018310726},"labels":[],"label_agreement":null},{"id":"W2909580386","doi":"10.2166/wst.2019.014","title":"Experimental study on air pressure variation in a horizontal pipe of single-stack drainage system","year":2019,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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":"Science and Technology Program of Zhejiang Province; Zhejiang University","keywords":"Inlet; Drainage; Stack (abstract data type); Pressure drop; Flow (mathematics); Slug flow; Volumetric flow rate; Nominal Pipe Size; Geotechnical engineering; Environmental science; Mechanics; Geology; Two-phase flow; Materials science","score_opus":0.006362900238814434,"score_gpt":0.19694456842562127,"score_spread":0.19058166818680683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909580386","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":[0.99689615,0.00005436357,0.002658597,0.000015626572,0.000016433865,0.000017777718,0.00008172324,0.00006817763,0.00019116841],"genre_scores_gemma":[0.99846244,0.00003722998,0.0011922435,0.0000052083496,0.000005330403,0.000021062218,0.00004998907,0.000006720445,0.0002197561],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999383,0.00008259408,0.000048483438,0.00017329831,0.00021486459,0.000097738346],"domain_scores_gemma":[0.99930835,0.00023617479,0.00010842309,0.00009474858,0.00019062049,0.0000617365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043594214,0.00041169263,0.00057393464,0.00033491783,0.00059501023,0.00033180395,0.0005035177,0.0005442996,0.0014038627],"category_scores_gemma":[0.0007305107,0.00025288475,0.00042241666,0.00027331902,0.00051251234,0.0005368042,0.0004536192,0.00034823388,0.00017503617],"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":[0.00061057834,0.00044768996,0.008080356,0.00029281178,0.000036628655,0.00025815892,0.00046327448,0.003337718,0.97373134,0.00015965558,0.0002361961,0.01234559],"study_design_scores_gemma":[0.00011014172,0.008608645,0.04408022,0.000027614826,0.00012248135,0.00024838204,0.00060303125,0.042689186,0.901578,0.00018849813,0.0016645005,0.00007931593],"about_ca_topic_score_codex":0.00085867476,"about_ca_topic_score_gemma":0.0006460737,"teacher_disagreement_score":0.0014038627,"about_ca_system_score_codex":0.00019575439,"about_ca_system_score_gemma":0.00031177409,"threshold_uncertainty_score":0.004696429},"labels":[],"label_agreement":null},{"id":"W2911228488","doi":"10.1139/cjce-2018-0021","title":"Theoretical study on pipe friction parameter identification in water distribution systems","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Decoupling (probability); Identification (biology); Applied mathematics; Engineering; Computer science; Control theory (sociology); Mathematics; Control engineering","score_opus":0.004796419269450897,"score_gpt":0.16634658726249973,"score_spread":0.16155016799304883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911228488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017222779,0.0007674527,0.9700759,0.00034897606,0.00002352523,0.000020172227,0.00003083845,0.000041865285,0.011468421],"genre_scores_gemma":[0.93320835,0.0021955129,0.059709076,0.00009650975,0.000096516,0.00012318135,0.00006482887,0.00003824163,0.0044678077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993412,0.00019429403,0.000030344014,0.00011436257,0.00025344538,0.00006632421],"domain_scores_gemma":[0.998111,0.0014489357,0.0001399456,0.0000721093,0.00019996332,0.000028034105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014112277,0.00069820444,0.0006119807,0.00090834213,0.0005687366,0.0011690964,0.0010161299,0.0010207517,0.0019963079],"category_scores_gemma":[0.0051615913,0.00043800217,0.00070840784,0.0010833988,0.001965749,0.0021884202,0.0011582181,0.001102972,0.00021624158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000149462285,0.00002667942,0.00086702453,0.00022548351,0.00001529772,0.00022340927,0.00026305614,0.65233165,0.0014463536,0.3312213,0.0005275477,0.012837269],"study_design_scores_gemma":[0.0000027339631,0.00001793147,0.00014191476,0.000024403174,0.000005178842,0.000050226878,0.000050249397,0.9394112,0.0003768139,0.058770377,0.0011405745,0.000008378174],"about_ca_topic_score_codex":0.0033316368,"about_ca_topic_score_gemma":0.0014007778,"teacher_disagreement_score":0.0033316368,"about_ca_system_score_codex":0.0011014242,"about_ca_system_score_gemma":0.00085094915,"threshold_uncertainty_score":0.007991374},"labels":[],"label_agreement":null},{"id":"W2912457211","doi":"10.1016/j.asej.2018.10.007","title":"Explicit solutions for turbulent flow friction factor: A review, assessment and approaches classification","year":2019,"lang":"en","type":"article","venue":"Ain Shams Engineering Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":83,"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":"Computation; Turbulence; Flow (mathematics); Range (aeronautics); Diagram; Mathematics; Simplicity; Applied mathematics; Mathematical optimization; Computer science; Algorithm; Engineering; Mechanics; Statistics; Geometry; Physics","score_opus":0.04796142044760527,"score_gpt":0.2314943789454312,"score_spread":0.18353295849782594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912457211","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":[0.0024097455,0.9286828,0.058133606,0.000660978,0.0005348156,0.00003115364,0.00011322494,0.00007812241,0.009355648],"genre_scores_gemma":[0.031233443,0.9325505,0.030724838,0.00020766984,0.0011995482,0.00007783305,0.00025314413,0.000065473905,0.0036875836],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994469,0.00009642681,0.0000916329,0.00014014306,0.00018872054,0.000036187965],"domain_scores_gemma":[0.9989262,0.0005871423,0.00010716758,0.000062195075,0.00029262734,0.000024683686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009842677,0.0013815567,0.0017093054,0.0041969176,0.00051475025,0.0018953561,0.0016260203,0.00129625,0.003143241],"category_scores_gemma":[0.003233288,0.0006905102,0.0013073799,0.004789359,0.00085900584,0.0026691318,0.0010202228,0.0014771832,0.0012831875],"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":[0.00008749563,0.00018329477,0.0015469766,0.020256152,0.00017588495,0.00030744582,0.00038410982,0.018691592,0.0023939465,0.18136509,0.020028863,0.7545791],"study_design_scores_gemma":[0.000038142174,0.0003102203,0.0027499069,0.008239503,0.000436078,0.0022814712,0.0005702454,0.071931094,0.005250938,0.15541878,0.75248474,0.0002888775],"about_ca_topic_score_codex":0.0016692812,"about_ca_topic_score_gemma":0.00095884484,"teacher_disagreement_score":0.0041969176,"about_ca_system_score_codex":0.0007429237,"about_ca_system_score_gemma":0.0011783936,"threshold_uncertainty_score":0.010515213},"labels":[],"label_agreement":null},{"id":"W2912569345","doi":"10.1109/allerton.2018.8635893","title":"Safely landing water networks during power outages with energy storage","year":2018,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Water hammer; Flywheel; Energy storage; Interfacing; Power (physics); Marine engineering; Flywheel energy storage; Water pumping; Water storage; Pumped-storage hydroelectricity; Pipeline transport; Automotive engineering; Computer science; Environmental science; Energy (signal processing); Engineering; Mechanical engineering","score_opus":0.002674471794543528,"score_gpt":0.1450798739090334,"score_spread":0.14240540211448988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912569345","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":[0.56608135,0.0003145338,0.4214262,0.0007811761,0.00008518048,0.00010844072,0.00015785795,0.00046442082,0.010580862],"genre_scores_gemma":[0.99022335,0.00007498171,0.00796034,0.00002047442,0.000008259484,0.000029466157,0.000039385497,0.000027044409,0.0016167524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998136,0.00005707592,0.000007437954,0.000025998474,0.00002938888,0.000066556786],"domain_scores_gemma":[0.9995189,0.000256687,0.000070326394,0.00002746415,0.000057823156,0.00006893973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041476384,0.0006890282,0.00063248613,0.00027877736,0.0005851176,0.00085359375,0.00048169313,0.0009423465,0.0023648536],"category_scores_gemma":[0.0012291366,0.00032600627,0.00037628796,0.00028478418,0.0011348699,0.0015052124,0.0011446277,0.0006914338,0.00019367927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007574503,0.000023431463,0.000458663,0.000026577927,0.0000062519753,0.00015818825,0.000028417311,0.99245036,0.0020317365,0.0019114706,0.00024801708,0.0025811363],"study_design_scores_gemma":[0.000015393827,0.00005504026,0.0001102473,0.0000041355415,0.0000040774644,0.000014363313,0.00006746169,0.9965707,0.0007574633,0.0022560316,0.00014042041,0.000004604686],"about_ca_topic_score_codex":0.0068660537,"about_ca_topic_score_gemma":0.00548553,"teacher_disagreement_score":0.0068660537,"about_ca_system_score_codex":0.0006772472,"about_ca_system_score_gemma":0.0008696007,"threshold_uncertainty_score":0.0136522055},"labels":[],"label_agreement":null},{"id":"W2913721679","doi":"10.1680/jinam.17.00037","title":"Water mains’ prioritisation for small to medium-sized utilities of Canada","year":2019,"lang":"en","type":"article","venue":"Infrastructure Asset Management","topic":"Water Systems and 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":true,"ca_institutions":"University of British Columbia, Okanagan Campus; Golder Associates (Canada); University of British Columbia; University of Regina","funders":"","keywords":"Mains electricity; Life cycle costing; Activity-based costing; Water resource management; Electricity; Water supply; Water resources; Scarcity; Environmental science; Environmental economics; Business; Engineering; Environmental engineering; Operations management; Economics","score_opus":0.003543647029312242,"score_gpt":0.16531581751198737,"score_spread":0.16177217048267514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913721679","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":[0.9655889,0.00023840685,0.013162763,0.0008263723,0.000014364217,0.00022843968,0.0014627203,0.00013592772,0.018342009],"genre_scores_gemma":[0.9913462,0.000100915124,0.0029344347,0.000028471122,0.0000012355762,0.000025478987,0.00036759244,0.000014088597,0.0051816227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99969244,0.00004888988,0.000006981529,0.00003927472,0.00007391845,0.00013853314],"domain_scores_gemma":[0.9995521,0.00010510026,0.000034172415,0.000010999921,0.00020347537,0.00009413834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003742242,0.0004851098,0.00029842128,0.00057012006,0.0009444867,0.0014125031,0.00082537555,0.00043297434,0.0034141012],"category_scores_gemma":[0.001017455,0.00019191104,0.00037458577,0.00086080673,0.00045287327,0.00043842098,0.0005375153,0.00049834535,0.00014208541],"study_design_candidate":"observational","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":[0.00019125265,0.0001122112,0.029470047,0.00007611352,0.000030993775,0.00033583885,0.00016746052,0.9328329,0.0017972584,0.005160199,0.005427851,0.024397857],"study_design_scores_gemma":[0.00003522153,0.00006981548,0.020325126,0.000020418727,0.000022982998,0.00003418153,0.0009789568,0.97216773,0.0011538337,0.0011835352,0.0039817058,0.000026405382],"about_ca_topic_score_codex":0.9569207,"about_ca_topic_score_gemma":0.9644251,"teacher_disagreement_score":0.043079317,"about_ca_system_score_codex":0.026821366,"about_ca_system_score_gemma":0.019820163,"threshold_uncertainty_score":0.19460356},"labels":[],"label_agreement":null},{"id":"W2913751434","doi":"10.1061/(asce)wr.1943-5452.0001035","title":"Battle of the Water Networks District Metered Areas","year":2019,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Battle; Water supply; Competition (biology); Economic shortage; Water resources; Quality (philosophy); Water quality; Water scarcity; Session (web analytics); Operations research; Distribution (mathematics); Computer science; Engineering; Civil engineering; Environmental science; Water resource management; Environmental engineering; Mathematics; Geography","score_opus":0.005554981406202443,"score_gpt":0.169021320778019,"score_spread":0.16346633937181657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913751434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14117844,0.013051333,0.3958425,0.17494419,0.0033548768,0.00055594195,0.0008721237,0.00063780596,0.2695628],"genre_scores_gemma":[0.79471594,0.0049908315,0.132487,0.005599593,0.0008005758,0.00025338668,0.0006102407,0.0003216402,0.06022084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996083,0.0017986015,0.00011075078,0.000426863,0.0011571607,0.00042357564],"domain_scores_gemma":[0.99691236,0.0012208541,0.00018527337,0.00025971667,0.0007548606,0.00066684006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006113381,0.00048160058,0.00038342425,0.00070737227,0.0024581943,0.0059553357,0.001536945,0.002072342,0.006545513],"category_scores_gemma":[0.006773243,0.0004658918,0.00039247834,0.0010135362,0.0021605026,0.007205796,0.0043330714,0.0044944887,0.0006140606],"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":[0.00020769797,0.00016514605,0.0030974795,0.00037529308,0.00004700898,0.0002981604,0.0023103186,0.06546207,0.0043515502,0.46960685,0.08140766,0.37267077],"study_design_scores_gemma":[0.00008051055,0.00023071337,0.0036928433,0.0003623844,0.000018927578,0.00029851487,0.006694682,0.117489696,0.005263646,0.15678684,0.7089803,0.00010087591],"about_ca_topic_score_codex":0.008084506,"about_ca_topic_score_gemma":0.01467611,"teacher_disagreement_score":0.008084506,"about_ca_system_score_codex":0.0065391967,"about_ca_system_score_gemma":0.0047648773,"threshold_uncertainty_score":0.047445416},"labels":[],"label_agreement":null},{"id":"W2914828287","doi":"10.9753/icce.v36.papers.4","title":"PROBABILISTIC MODELING OF ABOVEGROUND STORAGE TANKS UNDER SURGE AND WAVE LOADS","year":2018,"lang":"en","type":"article","venue":"Coastal Engineering Proceedings","topic":"Water Systems and 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; National Science Foundation","keywords":"Fragility; Finite element method; Probabilistic logic; Buckling; Surge; Structural engineering; Storm surge; Range (aeronautics); Statistical model; Wave model; Engineering; Computer science; Geology; Storm; Meteorology; Physics","score_opus":0.012258508968432433,"score_gpt":0.17566936947490694,"score_spread":0.16341086050647452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914828287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33781713,0.00020670214,0.6517641,0.00022382531,0.000018641535,0.000091686525,0.0005126178,0.0004750213,0.008890242],"genre_scores_gemma":[0.9884284,0.00011415233,0.008359987,0.000012452179,0.000005231239,0.00006669098,0.00012930442,0.000026503816,0.0028573829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996791,0.00007826541,0.000015374819,0.00005768519,0.00011357712,0.000055996366],"domain_scores_gemma":[0.9993963,0.00029651058,0.00015506845,0.000036143574,0.00009521991,0.000020711868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006875342,0.0005257394,0.00041629918,0.0005915987,0.00032647498,0.00062614615,0.0010245168,0.00084352645,0.001752204],"category_scores_gemma":[0.0014788801,0.00048369274,0.0005660936,0.00042312476,0.00071661576,0.0008341689,0.00052080635,0.00043793055,0.0002252782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053407825,0.0000036282925,0.00021876178,0.0000037032219,0.0000023019782,0.00000968393,0.0000061640158,0.99801105,0.00046580314,0.00048614052,0.000020006431,0.0007674828],"study_design_scores_gemma":[4.5582598e-7,0.0000049292676,0.00015058099,7.0982026e-7,0.0000012072653,0.0000030488388,0.0000030614374,0.9994153,0.00015155128,0.00024127756,0.000026748881,0.0000012255837],"about_ca_topic_score_codex":0.0099915285,"about_ca_topic_score_gemma":0.0074074343,"teacher_disagreement_score":0.0099915285,"about_ca_system_score_codex":0.00076026877,"about_ca_system_score_gemma":0.00077819533,"threshold_uncertainty_score":0.019866765},"labels":[],"label_agreement":null},{"id":"W2914939462","doi":"10.1109/cdc.2018.8619725","title":"Asymptotic Reverse-Waterfilling Characterization of Nonanticipative Rate Distortion Function of Vector-Valued Gauss-Markov Processes with MSE Distortion","year":2018,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Ottawa","funders":"Stiftelsen för Strategisk Forskning","keywords":"Distortion (music); Characterization (materials science); Markov chain; Markov process; Gauss; Function (biology); Algorithm; Mathematics; Rate distortion; Computer science; Applied mathematics; Mathematical optimization; Statistics; Physics; Telecommunications","score_opus":0.007856707265967097,"score_gpt":0.18376300000483098,"score_spread":0.17590629273886388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914939462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013114658,0.0001792473,0.9832722,0.00015453351,0.00001915457,0.000020944914,0.000026102114,0.00007687995,0.0031362635],"genre_scores_gemma":[0.836544,0.0011319167,0.1550654,0.00022092118,0.00011975796,0.00016460472,0.00011728684,0.00014095416,0.0064951023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99909604,0.00032131324,0.000044548047,0.00012485018,0.0002984895,0.00011479799],"domain_scores_gemma":[0.9960626,0.0024913438,0.0003856636,0.0003849199,0.0005725185,0.00010288909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026839438,0.0010054364,0.00083203905,0.0005221912,0.00034759907,0.0014055176,0.0010093999,0.0008633478,0.0022196486],"category_scores_gemma":[0.010768778,0.00035873338,0.0005509014,0.00061856216,0.0020057084,0.001815426,0.0013231856,0.0015453361,0.00046776814],"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":[0.00010826259,0.000052355415,0.000573424,0.00012870044,0.000028818076,0.00018005478,0.00016009303,0.56030446,0.010815925,0.40356696,0.0009744442,0.02310653],"study_design_scores_gemma":[0.00000472075,0.000025854444,0.00007781276,0.000008151049,0.0000046431937,0.00009213971,0.0000115761395,0.9674522,0.0024321335,0.0294821,0.0003987322,0.000009900248],"about_ca_topic_score_codex":0.000918212,"about_ca_topic_score_gemma":0.0005792654,"teacher_disagreement_score":0.0026839438,"about_ca_system_score_codex":0.0011510783,"about_ca_system_score_gemma":0.0013458867,"threshold_uncertainty_score":0.0141941905},"labels":[],"label_agreement":null},{"id":"W2917540622","doi":"10.5539/ijb.v11n2p29","title":"Assessment of the Quality of Water for Injection Marketed in South-Eastern Nigeria","year":2019,"lang":"en","type":"preprint","venue":"International Journal of Biology","topic":"Water Systems and 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":"Environmental science; Toxicology; Biology","score_opus":0.023726089349246197,"score_gpt":0.311310899686153,"score_spread":0.2875848103369068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917540622","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":[0.9980293,0.00028781762,0.00041496678,0.000021764487,0.0000042777924,0.000021423442,0.00007802678,0.0000029726984,0.0011394068],"genre_scores_gemma":[0.99665487,0.00059548905,0.0017800779,0.000022571261,0.0000032213177,0.000008929351,0.0001047152,0.0000028592497,0.0008272512],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994512,0.000086791086,0.000055966255,0.00007120975,0.00028714948,0.00004761527],"domain_scores_gemma":[0.9990563,0.00017448541,0.00046028337,0.000036161196,0.00024420815,0.000028467666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005746424,0.00019943397,0.00026672316,0.00080457225,0.0003582694,0.0010735292,0.00015558694,0.0003978672,0.0009101761],"category_scores_gemma":[0.0010011317,0.0001609078,0.00017654471,0.00082380243,0.0005012553,0.0005147826,0.0003113054,0.0002247624,0.00016236302],"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":[0.00059562153,0.00020329152,0.5794342,0.00060919096,0.00007066048,0.0009089228,0.0026300165,0.00075526093,0.36411592,0.00045101225,0.00013939646,0.050086472],"study_design_scores_gemma":[0.000014449437,0.002247096,0.766803,0.00019180853,0.00016517701,0.001808592,0.008030444,0.001642789,0.21340032,0.0004658132,0.0051779193,0.00005259023],"about_ca_topic_score_codex":0.0020964963,"about_ca_topic_score_gemma":0.004808235,"teacher_disagreement_score":0.0020964963,"about_ca_system_score_codex":0.00034027782,"about_ca_system_score_gemma":0.00035902642,"threshold_uncertainty_score":0.00416857},"labels":[],"label_agreement":null},{"id":"W2921234803","doi":"","title":"Pressure Sensor Placement for Leak Diagnosis under Demand Uncertainty in Water Distribution Systems","year":2019,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Water Systems and 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 Waterloo; Queen's University","keywords":"Leak detection; Leak; Environmental science; On demand; Pressure sensor; Engineering; Computer science; Petroleum engineering; Civil engineering; Water resource management; Environmental engineering; Mechanical engineering","score_opus":0.007824120254364039,"score_gpt":0.18079589535127394,"score_spread":0.1729717750969099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921234803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48044616,0.00028390507,0.516458,0.00023769322,0.00004107442,0.00006759948,0.00019446903,0.0009533069,0.0013178552],"genre_scores_gemma":[0.9927308,0.000033259636,0.0070738024,0.000006501585,0.0000030642448,0.000010339864,0.00003505107,0.000007546345,0.00009959936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996319,0.00012709746,0.00002075553,0.00008976463,0.00008744732,0.000043068267],"domain_scores_gemma":[0.9988422,0.00077579345,0.00014493396,0.00006295641,0.00014143514,0.000032734046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051852857,0.00068293494,0.00057012925,0.0003974131,0.00027491376,0.00048222486,0.00055232964,0.00078210136,0.00067684357],"category_scores_gemma":[0.0032405346,0.0003564981,0.0003152529,0.00034012116,0.0003964977,0.00057998503,0.0005621553,0.00042332386,0.00008950522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014831145,0.000038585553,0.0021915187,0.000058284284,0.000010374341,0.00009725574,0.00004776791,0.97688943,0.005105529,0.0003780128,0.00012095214,0.01491404],"study_design_scores_gemma":[0.0000034720397,0.000022979255,0.0003536696,0.0000018286071,0.0000020839243,0.000009331658,0.000009086898,0.9980451,0.0013290632,0.00019317036,0.000027652293,0.000002553757],"about_ca_topic_score_codex":0.004897526,"about_ca_topic_score_gemma":0.002613796,"teacher_disagreement_score":0.004897526,"about_ca_system_score_codex":0.0005255979,"about_ca_system_score_gemma":0.0004976217,"threshold_uncertainty_score":0.009738028},"labels":[],"label_agreement":null},{"id":"W2922594799","doi":"10.1002/clen.201800247","title":"Impacts of Water Quality on the Spatiotemporal Susceptibility of Water Distribution Systems","year":2019,"lang":"en","type":"article","venue":"CLEAN - Soil Air Water","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"American University of Sharjah; University of Sharjah","keywords":"Residual; Environmental science; Water quality; Chlorine; Hotspot (geology); Hydrology (agriculture); Environmental engineering; Geology; Chemistry; Computer science; Ecology; Geotechnical engineering","score_opus":0.013474642854254633,"score_gpt":0.21399367896372745,"score_spread":0.20051903610947283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922594799","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":[0.99665016,0.000041119183,0.0020659587,0.000032933927,0.000002388849,0.000013975457,0.00020495908,0.000019800034,0.0009687045],"genre_scores_gemma":[0.99958843,0.000015995469,0.00020186421,0.0000019819006,6.5439787e-7,0.0000026340085,0.00006425208,0.0000018073794,0.00012251406],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999655,0.0000808021,0.00003327668,0.00007315779,0.000094936324,0.00006280475],"domain_scores_gemma":[0.9986731,0.00047550417,0.00043207128,0.00010530277,0.00025358697,0.00006049158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040126024,0.00018927745,0.00018403551,0.0007074377,0.00027370238,0.0006375627,0.00018869631,0.0002507415,0.0009014656],"category_scores_gemma":[0.0018930229,0.00013327556,0.0002790853,0.0009046204,0.00039324327,0.0005546654,0.00050546136,0.00016314643,0.00007587878],"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":[0.00030398564,0.00015114022,0.74890596,0.00011393483,0.00025117962,0.0007952633,0.00040040136,0.19775864,0.029087499,0.0011305256,0.00032414944,0.020777265],"study_design_scores_gemma":[0.000007623623,0.00030277806,0.83427405,0.000010337361,0.00007615524,0.00022837565,0.001241593,0.15433809,0.0077038943,0.00096683553,0.00082235114,0.000027855061],"about_ca_topic_score_codex":0.015160188,"about_ca_topic_score_gemma":0.015031365,"teacher_disagreement_score":0.015160188,"about_ca_system_score_codex":0.0007584625,"about_ca_system_score_gemma":0.00030547907,"threshold_uncertainty_score":0.030143917},"labels":[],"label_agreement":null},{"id":"W2924223267","doi":"10.1109/sustech.2018.8671365","title":"Reliability Implications of Riverflow Variations in Planning Hydropower Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Saskatchewan","funders":"","keywords":"Reliability (semiconductor); Hydropower; Reliability engineering; Computer science; Engineering; Electrical engineering; Physics","score_opus":0.010000374957727641,"score_gpt":0.2242414254037703,"score_spread":0.21424105044604264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924223267","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":[0.5414709,0.0006051836,0.4483198,0.0008743927,0.000036331316,0.000107995525,0.00043080878,0.00021873621,0.007935894],"genre_scores_gemma":[0.99344903,0.00012492266,0.006048336,0.000012961075,0.000007408813,0.000018893968,0.000042430544,0.000013086258,0.00028289578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994461,0.00035164863,0.000017147458,0.000054628763,0.00008309674,0.00004745505],"domain_scores_gemma":[0.9980343,0.0014032748,0.00025992544,0.00006381768,0.000186637,0.000051945535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011445022,0.0003482802,0.00032353518,0.000492328,0.00029363867,0.0006888795,0.00043498137,0.00039230366,0.0005397273],"category_scores_gemma":[0.0055129905,0.0003413881,0.0002958767,0.00066707894,0.00038706654,0.0008465999,0.00043014257,0.00051719137,0.000044017383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011768317,0.000004897014,0.0014501542,0.000008449282,0.000005788159,0.000038704537,0.000013175176,0.9945484,0.00014771063,0.001140403,0.000058599217,0.002571908],"study_design_scores_gemma":[0.0000017539616,0.000020076683,0.0009094393,0.000003966078,0.0000046730484,0.000008993923,0.000022023045,0.9969662,0.0001552003,0.0018144693,0.000089614696,0.0000036515523],"about_ca_topic_score_codex":0.010001883,"about_ca_topic_score_gemma":0.014181549,"teacher_disagreement_score":0.010001883,"about_ca_system_score_codex":0.0008040046,"about_ca_system_score_gemma":0.00081501715,"threshold_uncertainty_score":0.019887388},"labels":[],"label_agreement":null},{"id":"W2937141166","doi":"10.1680/jinam.18.00039","title":"A different kind of partnership: an infrastructure performance stock exchange","year":2019,"lang":"en","type":"article","venue":"Infrastructure Asset Management","topic":"Water Systems and 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":"Regional Municipality of Waterloo; Fleming College; University of Waterloo","funders":"Society for Sedimentary Geology","keywords":"Business; General partnership; Finance; Asset management; Order (exchange); Public–private partnership; Stock exchange; Asset (computer security); Stock (firearms); Government (linguistics); Critical infrastructure; Industrial organization; Computer security; Engineering; Computer science","score_opus":0.0054640498566946175,"score_gpt":0.19467681989726535,"score_spread":0.18921277004057072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937141166","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":[0.08249157,0.001318023,0.29596168,0.024760399,0.0011569632,0.0006635924,0.0006000254,0.0008381433,0.5922096],"genre_scores_gemma":[0.8857732,0.0006073465,0.040051904,0.0014235267,0.0004727609,0.00028730044,0.00020490107,0.00013596062,0.071043104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99516845,0.0013534036,0.00033858264,0.00089548744,0.0016498258,0.0005942633],"domain_scores_gemma":[0.99507624,0.0010963388,0.0008995668,0.001403355,0.0007366534,0.00078794995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044486225,0.0005424075,0.0005174717,0.0015529454,0.0027678763,0.013586611,0.0020172969,0.005499473,0.025209846],"category_scores_gemma":[0.010166692,0.0004955343,0.00085898204,0.0014764076,0.0050216434,0.016750757,0.0063550198,0.00398367,0.002513606],"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":[0.000026283105,0.000047431608,0.0009144339,0.000016719594,0.000013790623,0.0001591621,0.0002550982,0.0012090781,0.0003187007,0.9844711,0.0027301414,0.009838121],"study_design_scores_gemma":[0.00010886449,0.00030783008,0.0023355815,0.0002312402,0.000060571067,0.0008024084,0.0013122732,0.025093723,0.0018680437,0.7485824,0.2191268,0.00017019587],"about_ca_topic_score_codex":0.0027409275,"about_ca_topic_score_gemma":0.0021836492,"teacher_disagreement_score":0.025209846,"about_ca_system_score_codex":0.0033999872,"about_ca_system_score_gemma":0.0030163801,"threshold_uncertainty_score":0.08433533},"labels":[],"label_agreement":null},{"id":"W2937788992","doi":"","title":"Stratégies hydrauliques pour améliorer la qualité de l'eau potable en réseau de distribution","year":2008,"lang":"fr","type":"article","venue":"","topic":"Water Systems and 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":"","keywords":"Humanities; Geography; Environmental science; Art","score_opus":0.009756311373536752,"score_gpt":0.2273031355742011,"score_spread":0.21754682420066435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937788992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07642321,0.00081694283,0.9138179,0.000417028,0.000087416905,0.0004054684,0.00040507474,0.0011528664,0.0064740544],"genre_scores_gemma":[0.5321607,0.0016876913,0.45134515,0.00018775996,0.000031393283,0.00072629837,0.00040239005,0.0002643233,0.013194237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891305,0.00020688976,0.000056344194,0.00027353657,0.0004315034,0.000118680866],"domain_scores_gemma":[0.9987054,0.00061378966,0.0001550671,0.00009697162,0.00037002823,0.000058754704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015092079,0.0013264524,0.0011881653,0.0010886665,0.0008039579,0.0027445287,0.0010731571,0.0014637789,0.003660629],"category_scores_gemma":[0.0022795603,0.00070443953,0.0013585325,0.0007298232,0.0007891874,0.0014040332,0.00094775314,0.0019094369,0.0007709258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041615433,0.0004430485,0.023509579,0.0014919464,0.00022899319,0.0004212595,0.0012896878,0.38375583,0.22781214,0.014621083,0.0018104758,0.34419978],"study_design_scores_gemma":[0.00010174173,0.0007771368,0.012863918,0.00020602475,0.00015259761,0.00022333978,0.00095441344,0.77864957,0.1799999,0.0099570695,0.015883477,0.00023078037],"about_ca_topic_score_codex":0.016112532,"about_ca_topic_score_gemma":0.027202416,"teacher_disagreement_score":0.016112532,"about_ca_system_score_codex":0.0021134566,"about_ca_system_score_gemma":0.0035820294,"threshold_uncertainty_score":0.032037497},"labels":[],"label_agreement":null},{"id":"W2940925925","doi":"10.1139/cjce-2018-0104","title":"Markov chain modulated Poisson process to stimulate the number of blockages in sewer networks","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"","keywords":"Markov chain; Computer science; Markov process; Combined sewer; Variable (mathematics); Bayesian network; Flooding (psychology); Process (computing); Continuous-time Markov chain; Property (philosophy); Markov model; Mathematical optimization; Engineering; Reliability engineering; Markov property; Mathematics; Statistics","score_opus":0.002863617904824723,"score_gpt":0.17284060641353002,"score_spread":0.1699769885087053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940925925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1438921,0.0004633078,0.8518753,0.00039621256,0.00006550266,0.00011461123,0.00028843846,0.00033233734,0.0025721912],"genre_scores_gemma":[0.9699561,0.00042760273,0.026523495,0.00005933226,0.00004108356,0.00015651183,0.0002553922,0.00002966012,0.0025508758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992205,0.00027366952,0.000031427113,0.00017413122,0.0001722669,0.00012796906],"domain_scores_gemma":[0.996079,0.0028230106,0.0005029432,0.00012436353,0.00037002433,0.000100601304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023774067,0.0005994464,0.000931545,0.0009353466,0.0003942778,0.00075118395,0.0019897504,0.001057001,0.0019430501],"category_scores_gemma":[0.006844578,0.00052658044,0.0010056137,0.00094159093,0.0007176272,0.0012747481,0.0007621443,0.0010723374,0.00021745423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042704633,0.00002500853,0.0027942169,0.000027312124,0.000021938751,0.000080217396,0.00004862449,0.9780757,0.00045355564,0.013669823,0.00024494002,0.004515906],"study_design_scores_gemma":[0.0000028067197,0.0000104178425,0.0003032806,0.0000032760975,0.000005948017,0.000008498482,0.0000064021256,0.99680746,0.00010507429,0.0026564763,0.000086857384,0.0000034526208],"about_ca_topic_score_codex":0.017164141,"about_ca_topic_score_gemma":0.010305936,"teacher_disagreement_score":0.017164141,"about_ca_system_score_codex":0.0013076639,"about_ca_system_score_gemma":0.0011713102,"threshold_uncertainty_score":0.034128487},"labels":[],"label_agreement":null},{"id":"W2943543561","doi":"10.14796/jwmm.c464","title":"Testing an Optimization–Simulation Model for Optimal Pump and Valve Operations with Required Storage Tank Turnovers","year":2019,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Computer science; Environmental science; Process engineering; Engineering","score_opus":0.023747643167673436,"score_gpt":0.22151160509210974,"score_spread":0.1977639619244363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943543561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44549406,0.00014125157,0.5252081,0.00049227715,0.00005210522,0.0003357235,0.0008346907,0.0010841625,0.026357625],"genre_scores_gemma":[0.97320604,0.000042650187,0.023988264,0.000020170843,0.0000040527393,0.00017842813,0.00017671286,0.000040907293,0.002342767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997167,0.000089224544,0.000016215747,0.000050084764,0.00007527122,0.00005249349],"domain_scores_gemma":[0.999295,0.00046502778,0.00007098208,0.000039602957,0.00010328345,0.000026014766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005284872,0.00047026353,0.0005551121,0.00031790618,0.00029296978,0.000626842,0.00062009925,0.00073183107,0.0030483303],"category_scores_gemma":[0.0015474816,0.00026618587,0.0005409528,0.00028449082,0.000376059,0.0003833018,0.00032057718,0.0004642898,0.0002495012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026345582,0.000017485789,0.00024834226,0.000009145543,0.0000026397074,0.000012851375,0.000005876397,0.9976496,0.00057336316,0.00054417236,0.000046293837,0.00086381286],"study_design_scores_gemma":[0.000006169775,0.000011752779,0.00007877259,9.709654e-7,0.000001778343,0.0000019992979,0.0000024277042,0.99915695,0.0005172138,0.00011289723,0.00010768076,0.000001405093],"about_ca_topic_score_codex":0.018225232,"about_ca_topic_score_gemma":0.0056397393,"teacher_disagreement_score":0.018225232,"about_ca_system_score_codex":0.0009145065,"about_ca_system_score_gemma":0.0018739882,"threshold_uncertainty_score":0.036238253},"labels":[],"label_agreement":null},{"id":"W2944043438","doi":"10.1029/2018wr024124","title":"Demand Satisfaction as a Framework for Understanding Intermittent Water Supply Systems","year":2019,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Global Health Research; University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Customer satisfaction; Peak demand; Environmental economics; Computer science; Water resource management; Environmental science; Business; Economics; Engineering; Marketing","score_opus":0.052579677707162265,"score_gpt":0.29474242133449685,"score_spread":0.2421627436273346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944043438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25154483,0.00050219725,0.70770305,0.0038975868,0.00006853237,0.00009800746,0.00063567347,0.00019279933,0.0353573],"genre_scores_gemma":[0.98952395,0.00011847341,0.008927615,0.00007282991,0.000025264988,0.000043697553,0.000101608566,0.000016865299,0.0011696911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99884355,0.00068562577,0.000039890398,0.00014538686,0.00015018745,0.00013540332],"domain_scores_gemma":[0.99743396,0.0014395072,0.00044671452,0.00015019736,0.00037195976,0.00015765682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001763135,0.0006334031,0.00051135855,0.0010971235,0.00050827884,0.0023572743,0.0015670395,0.0015113796,0.0046339524],"category_scores_gemma":[0.0052893837,0.0003600765,0.0008883986,0.0013333325,0.0016709937,0.0023415957,0.0014714936,0.0014767218,0.000273544],"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":[0.000032949923,0.000058847694,0.0040155356,0.00003962983,0.000027419437,0.00011595056,0.00026331586,0.78601676,0.00049445656,0.20343532,0.00087662827,0.0046231737],"study_design_scores_gemma":[0.000005842015,0.00001732149,0.0007883401,0.000005910738,0.0000035630867,0.000014881773,0.00012411992,0.9588594,0.00004497847,0.039410632,0.00071768096,0.000007332303],"about_ca_topic_score_codex":0.012825456,"about_ca_topic_score_gemma":0.0040970095,"teacher_disagreement_score":0.012825456,"about_ca_system_score_codex":0.002557662,"about_ca_system_score_gemma":0.00074842764,"threshold_uncertainty_score":0.025501609},"labels":[],"label_agreement":null},{"id":"W2945094647","doi":"10.1109/tii.2019.2911064","title":"Long-Term Monitoring for Leaks in Water Distribution Networks Using Association Rules Mining","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":37,"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":"Ontario Water Consortium","keywords":"Leak; Term (time); Computer science; Data mining; Leak detection; Identification (biology); Acoustic emission; Key (lock); Association rule learning; Process (computing); Condition monitoring; Real-time computing; Engineering; Computer security; Acoustics","score_opus":0.0322227014413549,"score_gpt":0.23269763079832884,"score_spread":0.20047492935697392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945094647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34883276,0.0017709435,0.64288855,0.0005647206,0.000094066614,0.00024573252,0.0019234085,0.0023677128,0.0013120736],"genre_scores_gemma":[0.8820801,0.00048318898,0.11416002,0.000098517674,0.00004185887,0.00012846064,0.0023936715,0.00004446481,0.00056975614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690187,0.00045966587,0.0005707153,0.00095239806,0.00093414,0.00018130471],"domain_scores_gemma":[0.99185055,0.0038538787,0.0021115113,0.0006081744,0.0013464789,0.00022935054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030452528,0.0011616976,0.0013344772,0.004567711,0.0006150807,0.0014604562,0.001803387,0.0011872002,0.00035412898],"category_scores_gemma":[0.0076291305,0.00048453905,0.0011348294,0.0032192543,0.00043247952,0.002226889,0.0011697437,0.0012076336,0.0003836544],"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":[0.00056373747,0.0010838017,0.2144034,0.00058155664,0.0008334503,0.0022268072,0.00049925223,0.28621164,0.01566207,0.0018497518,0.003456246,0.4726283],"study_design_scores_gemma":[0.000016460841,0.00016550525,0.018573374,0.000058674046,0.00013724453,0.0005174062,0.00026133208,0.96794665,0.006910414,0.0039527067,0.0014180351,0.000042164997],"about_ca_topic_score_codex":0.004619167,"about_ca_topic_score_gemma":0.005692459,"teacher_disagreement_score":0.004619167,"about_ca_system_score_codex":0.00057152484,"about_ca_system_score_gemma":0.0010738489,"threshold_uncertainty_score":0.016104996},"labels":[],"label_agreement":null},{"id":"W2945884555","doi":"","title":"Online Monitoring Framework for Pressure Transient Detection in Water Distribution Networks","year":2019,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Water Systems and 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":"Transient (computer programming); Computer science; Environmental science; Petroleum engineering; Engineering; Operating system","score_opus":0.006484078463571129,"score_gpt":0.1846716858378018,"score_spread":0.17818760737423067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945884555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004536419,0.000087691245,0.99122846,0.00013528446,0.000030426885,0.00008206411,0.00009835014,0.0021517863,0.0016495597],"genre_scores_gemma":[0.6362474,0.00038754358,0.3547104,0.00017547909,0.00012915557,0.0004864969,0.00061987166,0.00022768557,0.0070160287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899787,0.00017312073,0.0000691132,0.00029507693,0.0003332645,0.00013164265],"domain_scores_gemma":[0.999209,0.00025347565,0.00011133932,0.00007416996,0.00028246094,0.00006960895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012738657,0.00069391483,0.0008447696,0.0010648734,0.0006809368,0.001880711,0.0021462454,0.0009745994,0.0036390508],"category_scores_gemma":[0.0030269814,0.0003518112,0.0007818603,0.0005955978,0.00064208434,0.0020166498,0.001680093,0.0010516904,0.00065574725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026224562,0.0002514081,0.0044626947,0.00020698913,0.00010824061,0.0007002602,0.00044970162,0.68305975,0.010238007,0.09702304,0.0074098236,0.19582796],"study_design_scores_gemma":[0.000005082162,0.000012625812,0.0001849488,0.0000054110733,0.0000067664682,0.00002030232,0.000018365963,0.9931143,0.00066888676,0.0043523884,0.0016048896,0.00000596272],"about_ca_topic_score_codex":0.014176777,"about_ca_topic_score_gemma":0.011964547,"teacher_disagreement_score":0.014176777,"about_ca_system_score_codex":0.0014674872,"about_ca_system_score_gemma":0.0013766355,"threshold_uncertainty_score":0.028188527},"labels":[],"label_agreement":null},{"id":"W2948600054","doi":"","title":"Caractérisation spatio-temporelle de la réactivité des précurseurs de sous-produits de la désinfection en réseau de distribution d'eau potable","year":2010,"lang":"fr","type":"article","venue":"Corpus Université Laval (Université Laval)","topic":"Water Systems and 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":"","keywords":"Chemistry","score_opus":0.004159499786690853,"score_gpt":0.18269072850842927,"score_spread":0.1785312287217384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948600054","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":[0.9834963,0.0026441372,0.0064653773,0.00014942505,0.0000649455,0.00006462091,0.001961964,0.00017686005,0.004976303],"genre_scores_gemma":[0.980891,0.002013624,0.003952977,0.000089977446,0.000011264296,0.0000618114,0.0010916897,0.000060741895,0.011827075],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99972075,0.000011241494,0.0000075839866,0.000088687564,0.00011420989,0.000057541987],"domain_scores_gemma":[0.99968195,0.000055460972,0.0000533855,0.0000129433765,0.00017242537,0.000023783572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029358297,0.00038231176,0.0004604698,0.00046672617,0.000469256,0.00081739714,0.00034342048,0.000470732,0.00397313],"category_scores_gemma":[0.00027218548,0.00025213504,0.0005519931,0.0003316863,0.0002778982,0.00042325852,0.00026318335,0.00065336394,0.0006063175],"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":[0.00032216028,0.000041947613,0.016295979,0.00047334988,0.00007495306,0.00022024046,0.00051435624,0.0013715158,0.9645869,0.0002827043,0.000540314,0.01527558],"study_design_scores_gemma":[0.000012590816,0.0006241584,0.1735938,0.00007052419,0.00014745763,0.0003222746,0.001212593,0.0076793088,0.79914695,0.00030464778,0.016811153,0.00007454879],"about_ca_topic_score_codex":0.06003019,"about_ca_topic_score_gemma":0.10587408,"teacher_disagreement_score":0.06003019,"about_ca_system_score_codex":0.00092222594,"about_ca_system_score_gemma":0.00078301824,"threshold_uncertainty_score":0.11936158},"labels":[],"label_agreement":null},{"id":"W2955680721","doi":"10.3390/w11071372","title":"Using Nodal Infection Risks to Guide Interventions Following Accidental Intrusion due to Sustained Low Pressure Events in a Drinking Water Distribution System","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Intrusion; Cryptosporidium; Environmental science; Accidental; Risk assessment; Sewage; Weibull distribution; Environmental health; Toxicology; Environmental engineering; Computer science; Biology; Statistics; Mathematics; Medicine; Ecology; Computer security; Geology; Physics","score_opus":0.01271778323627004,"score_gpt":0.257713313183334,"score_spread":0.24499552994706397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955680721","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":[0.8003497,0.0002575921,0.19513549,0.00028302317,0.000023020826,0.00019813945,0.00029453903,0.00042475792,0.003033708],"genre_scores_gemma":[0.99319893,0.00004651106,0.0063203787,0.000012554221,0.0000018159697,0.000037863996,0.000059738242,0.000009097304,0.00031305914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946505,0.00022608788,0.000029255674,0.000114755654,0.00008805102,0.00007676519],"domain_scores_gemma":[0.9977506,0.0012735113,0.0006159699,0.00006886056,0.00019265036,0.00009837197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014019628,0.00093026826,0.00051031215,0.00065476244,0.00024308727,0.0010370185,0.0008336313,0.0008153718,0.0011154073],"category_scores_gemma":[0.004630228,0.0004961121,0.0005392863,0.00023357283,0.0004371875,0.0009141441,0.0010138602,0.0004992366,0.00012936942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007420201,0.000033301,0.011907479,0.000030633735,0.00002756325,0.000040643565,0.00004731411,0.9819405,0.0014692222,0.0004215561,0.00005161165,0.0039559403],"study_design_scores_gemma":[0.0000066383323,0.00010765581,0.0031437723,0.000008693562,0.000022831362,0.0000144118,0.00006497615,0.9952057,0.0007399591,0.0005825913,0.000092927956,0.000009835541],"about_ca_topic_score_codex":0.009080197,"about_ca_topic_score_gemma":0.0061909393,"teacher_disagreement_score":0.009080197,"about_ca_system_score_codex":0.0011805727,"about_ca_system_score_gemma":0.0011727934,"threshold_uncertainty_score":0.018054724},"labels":[],"label_agreement":null},{"id":"W2958212485","doi":"10.3390/w11071425","title":"Experimental Investigations and CFD Modeling of Contaminant Intrusion in a Water Network during Transient Events","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Plan for Science,Technology and Innovation; United Arab Emirates University; King Abdulaziz City for Science and Technology; National Water Center, United Arab Emirates University; King Fahd University of Petroleum and Minerals","keywords":"Intrusion; Transient (computer programming); Environmental science; Contamination; Petroleum engineering; Computer science; Environmental engineering; Geology; Operating system","score_opus":0.006283673439366831,"score_gpt":0.17215208930497244,"score_spread":0.1658684158656056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958212485","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":[0.98904806,0.00006974581,0.008931444,0.00008395525,0.000017364215,0.00004148036,0.00027642568,0.00011762653,0.0014139104],"genre_scores_gemma":[0.9972018,0.00006228562,0.002211553,0.0000052959736,0.0000023551604,0.000026672285,0.00008775817,0.0000074581285,0.000394838],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999728,0.000033167777,0.000019376876,0.00006344992,0.00009363939,0.00006223295],"domain_scores_gemma":[0.99946946,0.0002436402,0.00007261848,0.00005362881,0.00013041281,0.00003020484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031584135,0.00031190104,0.0003605756,0.0003112938,0.00045497317,0.00043937296,0.00043677198,0.00053309725,0.0013898641],"category_scores_gemma":[0.0009781762,0.0001885039,0.00031598832,0.0003723156,0.0005833363,0.00059170893,0.0003422,0.00048302626,0.00014135608],"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":[0.0008260735,0.00059634395,0.021307318,0.00043482345,0.000027968781,0.0007999828,0.00049452746,0.40929574,0.54267174,0.0017757013,0.0008397832,0.02093003],"study_design_scores_gemma":[0.000031005224,0.00063262385,0.008690549,0.000013966715,0.00001495954,0.00006442267,0.0002535132,0.83151585,0.15754619,0.0002607188,0.00094706437,0.000029131827],"about_ca_topic_score_codex":0.0056223874,"about_ca_topic_score_gemma":0.003797833,"teacher_disagreement_score":0.0056223874,"about_ca_system_score_codex":0.0006565392,"about_ca_system_score_gemma":0.00051249744,"threshold_uncertainty_score":0.011179328},"labels":[],"label_agreement":null},{"id":"W2961897783","doi":"10.1016/j.ress.2019.106581","title":"Managing water main breaks in distribution networks––A risk-based decision making","year":2019,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Water Systems and Optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prioritization; Reliability engineering; Failure mode, effects, and criticality analysis; Redundancy (engineering); Mains electricity; Reliability (semiconductor); Computer science; Risk analysis (engineering); Risk assessment; Risk management; Engineering; Failure mode and effects analysis; Business","score_opus":0.0017192180632996273,"score_gpt":0.160792879497475,"score_spread":0.15907366143417537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961897783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18905511,0.00089145603,0.7877453,0.004199046,0.00019263173,0.0006626262,0.00033243303,0.0003145492,0.016606925],"genre_scores_gemma":[0.9298039,0.0002828701,0.06695832,0.0001295254,0.000091505506,0.00011176942,0.00013173952,0.000047405832,0.0024427802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948127,0.0024039294,0.00029998823,0.00075369107,0.0010905142,0.0006391243],"domain_scores_gemma":[0.9937791,0.0036088664,0.00092055363,0.00027354097,0.00084222126,0.0005756506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007695425,0.0013730719,0.0016992869,0.0018258065,0.0011049655,0.00591623,0.0023365216,0.0027690202,0.004558545],"category_scores_gemma":[0.011801047,0.0010128455,0.00145258,0.0010800045,0.0014936336,0.0070727337,0.0030974343,0.0021209477,0.00039778056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030206234,0.00024047372,0.0024547416,0.0001270333,0.00019692346,0.00020270205,0.0002690752,0.92760783,0.0015593952,0.019072063,0.0013690437,0.046598706],"study_design_scores_gemma":[0.000018589217,0.00015940717,0.0006158593,0.000051250856,0.000053767733,0.00003821498,0.00022673272,0.97537756,0.00073430093,0.021829924,0.0008629995,0.000031363874],"about_ca_topic_score_codex":0.0041679577,"about_ca_topic_score_gemma":0.0038081196,"teacher_disagreement_score":0.007695425,"about_ca_system_score_codex":0.0023655386,"about_ca_system_score_gemma":0.002850552,"threshold_uncertainty_score":0.040697753},"labels":[],"label_agreement":null},{"id":"W2964439072","doi":"10.3390/w11081591","title":"Ranking Approach to Scheduling Repairs of a Water Distribution System for the Post-Disaster Response and Restoration Service","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"Operations research; Ranking (information retrieval); Computer science; Scheduling (production processes); Schedule; Damages; Service (business); Task (project management); Risk analysis (engineering); Operations management; Engineering; Systems engineering; Business; Artificial intelligence","score_opus":0.008213333439923405,"score_gpt":0.18255694034400655,"score_spread":0.17434360690408315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964439072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10379966,0.0007680417,0.8890908,0.000264473,0.00006258588,0.00042283282,0.0003411948,0.00034391068,0.0049065445],"genre_scores_gemma":[0.5735365,0.00051066873,0.42111057,0.00006467005,0.0000403479,0.00031897228,0.00041248888,0.00006870776,0.00393704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973008,0.00142428,0.00014690647,0.0002810282,0.000526517,0.00032039318],"domain_scores_gemma":[0.9975439,0.0013095487,0.00025230815,0.00007828922,0.0006395754,0.00017636102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032701558,0.0013069608,0.0014342149,0.0026783021,0.0007422995,0.0017111714,0.0009550829,0.0007188565,0.003044665],"category_scores_gemma":[0.004016856,0.0005057609,0.0011066836,0.0026466842,0.0003689106,0.0012204134,0.000605806,0.00080927304,0.00033963076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004246409,0.0004035054,0.004041524,0.0008357948,0.00030211447,0.00016084628,0.00036741636,0.654717,0.005408902,0.011795875,0.0024952784,0.31904715],"study_design_scores_gemma":[0.00002818137,0.00064868544,0.0019218604,0.000035876612,0.000092930306,0.000059276925,0.00030263522,0.9876894,0.0018064317,0.005221543,0.002164572,0.000028593488],"about_ca_topic_score_codex":0.007371727,"about_ca_topic_score_gemma":0.0102282055,"teacher_disagreement_score":0.007371727,"about_ca_system_score_codex":0.0018654214,"about_ca_system_score_gemma":0.0022697528,"threshold_uncertainty_score":0.017294407},"labels":[],"label_agreement":null},{"id":"W2965343198","doi":"10.3808/jeil.201900010","title":"Short-Term Wastewater Influent Prediction Based on Random Forests and Multi-Layer Perceptron","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Informatics Letters","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"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":"Perceptron; Multilayer perceptron; Random forest; Wastewater; Volumetric flow rate; Predictive modelling; Computer science; Set (abstract data type); Data set; Artificial neural network; Machine learning; Artificial intelligence; Environmental science; Data mining; Engineering; Environmental engineering","score_opus":0.00617378438646219,"score_gpt":0.17598656390500902,"score_spread":0.16981277951854684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965343198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29083976,0.002086829,0.70251477,0.00047832902,0.00021710494,0.00008993675,0.00035021422,0.0018021072,0.0016209177],"genre_scores_gemma":[0.93748236,0.0004357733,0.060191136,0.00009626757,0.000050430615,0.00008093834,0.00039092355,0.00003923643,0.0012330324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916315,0.00023384117,0.000072533934,0.00024032923,0.00014845957,0.00014180262],"domain_scores_gemma":[0.9987393,0.0007259696,0.00014981558,0.000050150742,0.00027025017,0.00006455063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022536383,0.0013020961,0.0011598024,0.0009853115,0.0004472713,0.00073909876,0.0012848459,0.001148185,0.0006022847],"category_scores_gemma":[0.0027523546,0.00060398417,0.0015406763,0.0007800077,0.00037501773,0.0012931358,0.00051033235,0.0012138757,0.00023184683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019645944,0.00012192674,0.0038792074,0.00006252164,0.000114961666,0.00010875749,0.000029643914,0.9335371,0.0020768247,0.00045977146,0.0005030227,0.05890972],"study_design_scores_gemma":[0.0000024571784,0.000020043597,0.00034304342,0.0000022551694,0.0000064583373,0.00000502483,0.0000023815678,0.99917597,0.00027141953,0.00013620162,0.000029357481,0.0000053384465],"about_ca_topic_score_codex":0.02446889,"about_ca_topic_score_gemma":0.014822439,"teacher_disagreement_score":0.02446889,"about_ca_system_score_codex":0.0008034632,"about_ca_system_score_gemma":0.0010817188,"threshold_uncertainty_score":0.048652947},"labels":[],"label_agreement":null},{"id":"W2965721913","doi":"10.1002/opfl.1229","title":"Save Energy With Anti‐Stagnation Valves","year":2019,"lang":"en","type":"article","venue":"Opflow","topic":"Water Systems and 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":"Escarpment; Situated; Elevation (ballistics); Distribution (mathematics); Geology; Geography; Hydrology (agriculture); Physical geography; Oceanography; Archaeology; Geometry; Mathematics; Geotechnical engineering; Computer science","score_opus":0.002637367278015678,"score_gpt":0.1454124883671159,"score_spread":0.14277512108910023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965721913","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":[0.09683484,0.004293913,0.72414166,0.0017485962,0.001229429,0.00010491217,0.0004888119,0.006168669,0.16498928],"genre_scores_gemma":[0.93902844,0.00080261193,0.02336986,0.0001702043,0.00011916798,0.00003376922,0.00022291065,0.00031311897,0.03593986],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99989843,0.000015066921,0.0000038010587,0.000013206079,0.000042445005,0.000027050844],"domain_scores_gemma":[0.9998847,0.0000372663,0.0000109767925,0.000018808105,0.00003623684,0.000011980404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013550941,0.000515483,0.00049127423,0.00032534834,0.00041783924,0.0007544912,0.0007416861,0.00030553908,0.013556289],"category_scores_gemma":[0.0005072979,0.00012268733,0.00025938664,0.0003570171,0.0002810334,0.0006790328,0.0004990287,0.00037576444,0.0017489368],"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":[0.0010464669,0.00019238744,0.0015607589,0.00040333348,0.00009487788,0.00030357274,0.00013576665,0.17389442,0.025807807,0.07791402,0.05838846,0.6602582],"study_design_scores_gemma":[0.00020356248,0.00055291614,0.0020681892,0.00009370355,0.000082969564,0.00023966178,0.000089366345,0.8261303,0.017236788,0.06291882,0.09033288,0.000050749542],"about_ca_topic_score_codex":0.0022548717,"about_ca_topic_score_gemma":0.0036689865,"teacher_disagreement_score":0.013556289,"about_ca_system_score_codex":0.00036701022,"about_ca_system_score_gemma":0.0004024666,"threshold_uncertainty_score":0.045350373},"labels":[],"label_agreement":null},{"id":"W2968093915","doi":"10.3390/en12163172","title":"Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Leak; SIGNAL (programming language); Pipeline (software); False alarm; Acoustics; Isolation (microbiology); Computer science; Detection theory; Pipeline transport; Constant false alarm rate; ALARM; Real-time computing; Engineering; Algorithm; Artificial intelligence; Physics; Telecommunications; Electrical engineering; Detector","score_opus":0.005400928142259878,"score_gpt":0.17222080629682357,"score_spread":0.1668198781545637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968093915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02185483,0.00007698677,0.97669923,0.000038310398,0.0000129801865,0.000015147657,0.000017999424,0.0006292817,0.0006552025],"genre_scores_gemma":[0.79538935,0.00019117752,0.20142819,0.00006114679,0.00003009152,0.000079479316,0.00018467092,0.00012982373,0.0025060754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968874,0.000046667385,0.00001605154,0.00008337255,0.00012298103,0.00004211952],"domain_scores_gemma":[0.99964476,0.00011121614,0.000080083904,0.000050237202,0.00009247317,0.000021187749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002796804,0.0010113679,0.0009438921,0.00072070473,0.00035380488,0.000729184,0.00064950227,0.00067675975,0.0011300854],"category_scores_gemma":[0.0014400095,0.00039257415,0.00075297203,0.00035904456,0.0004228901,0.0013102344,0.0008335102,0.0006248558,0.00046835645],"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":[0.00058853975,0.00022729015,0.0050599664,0.0003761198,0.000114378854,0.00034456482,0.0002896785,0.39926153,0.12999299,0.005486513,0.0018591788,0.4563992],"study_design_scores_gemma":[0.000010524113,0.000066012,0.0007720113,0.000005441769,0.000018185197,0.00010179588,0.000018947345,0.9850273,0.012291089,0.0010319944,0.00064093055,0.000015683321],"about_ca_topic_score_codex":0.0026364017,"about_ca_topic_score_gemma":0.0021015222,"teacher_disagreement_score":0.0026364017,"about_ca_system_score_codex":0.00029697522,"about_ca_system_score_gemma":0.0008567301,"threshold_uncertainty_score":0.0052420497},"labels":[],"label_agreement":null},{"id":"W2968632776","doi":"10.29008/etc2019-381","title":"Part load resonance risk assessment of francis hydropower units","year":2019,"lang":"en","type":"article","venue":"Proceedings of ... European Conference on Turbomachinery Fluid Dynamics & Thermodynamics","topic":"Water Systems and 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":"FP7 Energy; Bundesamt für Energie; BC Hydro; Kommission für Technologie und Innovation; European Commission","keywords":"Francis turbine; Draft tube; Hydropower; Turbine; Resonance (particle physics); Marine engineering; Hydraulic turbines; Precession; Cavitation; Rotor (electric); Rope; Mechanics; Engineering; Computer science; Structural engineering; Physics; Mechanical engineering; Electrical engineering","score_opus":0.00789669191578986,"score_gpt":0.19951665056660325,"score_spread":0.19161995865081338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968632776","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":[0.9885904,0.00005362834,0.009781889,0.000035408164,0.0000033172525,0.000023052839,0.00008609737,0.0000779353,0.0013483334],"genre_scores_gemma":[0.9991979,0.000011137382,0.0005847794,0.000002000591,8.613581e-7,0.000004904719,0.00003855341,0.0000047170965,0.00015522659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993876,0.0001895627,0.000024039316,0.00007039298,0.00026129957,0.0000670875],"domain_scores_gemma":[0.99702126,0.0018591932,0.0004606821,0.0001968291,0.00034495135,0.00011713166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011676569,0.00041113357,0.00043976266,0.0013131998,0.0002816471,0.00055888353,0.000553497,0.00073842675,0.0009797735],"category_scores_gemma":[0.0042849677,0.00024988543,0.00036752492,0.0003594041,0.00045565548,0.00083648314,0.00085494417,0.00030118064,0.00016168357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011509253,0.00011531248,0.06711046,0.00012165227,0.00009398067,0.0010830029,0.0003231102,0.8795868,0.01929527,0.0016207099,0.00073636597,0.028762462],"study_design_scores_gemma":[0.00001683563,0.0007705337,0.060119417,0.000017300168,0.00006428122,0.00022162849,0.0004004558,0.92479753,0.012185104,0.00090989424,0.00044012364,0.000056894754],"about_ca_topic_score_codex":0.003075592,"about_ca_topic_score_gemma":0.0022054897,"teacher_disagreement_score":0.003075592,"about_ca_system_score_codex":0.0006371475,"about_ca_system_score_gemma":0.00019332393,"threshold_uncertainty_score":0.00617522},"labels":[],"label_agreement":null},{"id":"W2969283176","doi":"","title":"Examining the Role of Layer Growth Duration on Layer Strength and Turbidity Response in a Full-Scale Laboratory Drinking Water Distribution System at Queen’s University:","year":2018,"lang":"en","type":"article","venue":"WDSA / CCWI Joint Conference Proceedings","topic":"Water Systems and 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":"Turbidity; Environmental science; Water flow; Water quality; Flow (mathematics); Duration (music); Hydrology (agriculture); Environmental engineering; Ecology; Geotechnical engineering; Engineering; Geology; Mechanics; Oceanography","score_opus":0.012466675019268224,"score_gpt":0.1751554518293262,"score_spread":0.16268877681005797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969283176","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":[0.9994617,0.0000142116405,0.00023956042,0.000011992604,0.0000014777662,0.000011403007,0.00006651027,0.000013856636,0.0001793638],"genre_scores_gemma":[0.9984744,0.000025842135,0.0006653404,0.00001647656,7.6795806e-7,0.000016046786,0.000108183216,0.000005278579,0.0006875734],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998388,0.000017519245,0.000008018815,0.000040537263,0.000058749236,0.00003629342],"domain_scores_gemma":[0.999212,0.000191582,0.00013624708,0.00005199171,0.0003024783,0.00010562601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026605013,0.00016348512,0.00020357406,0.00012441468,0.00039201675,0.00052425894,0.00035904505,0.00025061052,0.001347991],"category_scores_gemma":[0.0005827053,0.00015278305,0.00020260313,0.00014728999,0.0002523255,0.0003272007,0.00031000905,0.00034285517,0.00027095797],"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":[0.0005588576,0.00041026156,0.03549559,0.00008281748,0.00001637627,0.0001128667,0.00038931752,0.0009397006,0.95435506,0.000031308013,0.0002050585,0.007402883],"study_design_scores_gemma":[0.000031669308,0.0022860102,0.40796584,0.0000111636555,0.000048604754,0.0000857981,0.0012855969,0.012282595,0.5749346,0.000028193064,0.0010092305,0.000030787232],"about_ca_topic_score_codex":0.025526494,"about_ca_topic_score_gemma":0.03544609,"teacher_disagreement_score":0.025526494,"about_ca_system_score_codex":0.0008750195,"about_ca_system_score_gemma":0.0005338847,"threshold_uncertainty_score":0.0507558},"labels":[],"label_agreement":null},{"id":"W2969685657","doi":"","title":"Impacts of the Integration of Water Demand Prediction in Real Time Control of Water Distribution Systems","year":2018,"lang":"en","type":"article","venue":"WDSA / CCWI Joint Conference Proceedings","topic":"Water Systems and 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":"","keywords":"Leakage (economics); Environmental science; Real-time Control System; Computer science; Control theory (sociology); Control (management); Artificial intelligence; Economics","score_opus":0.011082093856011575,"score_gpt":0.18932300186582102,"score_spread":0.17824090800980943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969685657","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":[0.8559818,0.0004995123,0.13848871,0.00041816596,0.00010260569,0.000057103334,0.000059157126,0.00082512264,0.0035677352],"genre_scores_gemma":[0.99704355,0.0000377716,0.002619738,0.000014552462,0.000008462535,0.0000067311435,0.000015285927,0.0000065211425,0.00024732095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991516,0.00027272524,0.000047076635,0.0001782876,0.00023231674,0.000118129865],"domain_scores_gemma":[0.9987703,0.00052300974,0.00017772854,0.00015129481,0.00029470128,0.0000829024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011392724,0.00079679064,0.00046677972,0.00021234911,0.00017586141,0.0010146927,0.00065854145,0.0005451605,0.0006133035],"category_scores_gemma":[0.0032437549,0.00026143398,0.00026060178,0.00026690835,0.00040099485,0.00090450404,0.00054278166,0.00075798534,0.00008254721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058793987,0.00036696918,0.005589657,0.000077792385,0.00010433187,0.00010576909,0.000064917316,0.92522895,0.010305823,0.00054490817,0.000198166,0.05682477],"study_design_scores_gemma":[0.000035381363,0.0005805762,0.00412439,0.000008478555,0.00005888911,0.000024248084,0.000036712605,0.98783755,0.006635974,0.00023083013,0.00041424125,0.000012741057],"about_ca_topic_score_codex":0.0120742265,"about_ca_topic_score_gemma":0.0056166453,"teacher_disagreement_score":0.0120742265,"about_ca_system_score_codex":0.00051540614,"about_ca_system_score_gemma":0.0007045144,"threshold_uncertainty_score":0.024007916},"labels":[],"label_agreement":null},{"id":"W2970863477","doi":"10.1139/cjce-2019-0188","title":"Optimized maintenance model for wastewater treatment plants","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Concordia University; Custom Security Industries (Canada); Aecom (Canada)","funders":"","keywords":"Intervention (counseling); Sewage treatment; Wastewater; Rehabilitation; Engineering; Environmental science; Operations research; Operations management; Environmental engineering","score_opus":0.008872132434146914,"score_gpt":0.165363844775492,"score_spread":0.1564917123413451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970863477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15793888,0.0011626156,0.7791663,0.00093913474,0.00019488046,0.0005803315,0.0024214678,0.0011963302,0.0564001],"genre_scores_gemma":[0.9300867,0.00048145774,0.043711446,0.00011595741,0.000033032517,0.00072746736,0.001029343,0.00012885171,0.023685751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935144,0.00015908193,0.000032928812,0.00012970855,0.00015395753,0.00017297469],"domain_scores_gemma":[0.9989681,0.00047599972,0.00012606622,0.000023518905,0.00036160232,0.000044696037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011190834,0.001332791,0.0013067076,0.0009412397,0.0007354253,0.0018181994,0.0020912695,0.0022175226,0.007609664],"category_scores_gemma":[0.0023065228,0.00081543706,0.0013078924,0.00074440415,0.00060775236,0.00093343435,0.00093261333,0.0018517704,0.0007505696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020374453,0.000013430036,0.00014993545,0.000020838155,0.0000044978124,0.000023723664,0.000009998475,0.9972428,0.00018719549,0.0008043243,0.00015200523,0.001370919],"study_design_scores_gemma":[0.000006912368,0.000013537412,0.00010223736,0.0000039955307,0.0000055820424,0.000004695412,0.000007067334,0.99916124,0.00008345247,0.00037569014,0.00023298238,0.0000027281476],"about_ca_topic_score_codex":0.049789924,"about_ca_topic_score_gemma":0.020872379,"teacher_disagreement_score":0.049789924,"about_ca_system_score_codex":0.0022230526,"about_ca_system_score_gemma":0.0025572474,"threshold_uncertainty_score":0.099000275},"labels":[],"label_agreement":null},{"id":"W2971691499","doi":"10.1201/9780429319778-94","title":"Recent remote underwater surveys: Advances in methods and technologies for structural assessments of dams and spillways","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Water Systems and 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":"Underwater; Environmental science; Engineering; Civil engineering; Geology; Oceanography","score_opus":0.027845213379455655,"score_gpt":0.3236799166702968,"score_spread":0.2958347032908411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971691499","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":[0.010210616,0.25923875,0.52489674,0.0030350424,0.003085914,0.0002452185,0.0011534982,0.0020451616,0.19608903],"genre_scores_gemma":[0.02752279,0.24289988,0.35656372,0.0010311962,0.0017546343,0.00019785414,0.0016612732,0.0011049687,0.3672637],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992931,0.00011906152,0.000032105054,0.0001504285,0.00038144368,0.000023828228],"domain_scores_gemma":[0.9991597,0.00041499923,0.00004710311,0.00008753582,0.00026158325,0.0000291878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011018316,0.0010042585,0.0007245823,0.0027244654,0.00045703593,0.0018723605,0.0008984593,0.0009442726,0.017247917],"category_scores_gemma":[0.0012314486,0.0007844518,0.0006357324,0.0036459714,0.00069965614,0.0022726818,0.0012822731,0.0012219347,0.007937262],"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":[0.00002105144,0.000037991995,0.0007914516,0.0018784368,0.00003743474,0.00009057001,0.0003165607,0.0034298792,0.010669868,0.01637153,0.02790229,0.938453],"study_design_scores_gemma":[0.0000043015734,0.00006438833,0.002954789,0.00059497816,0.00002920604,0.0005490503,0.00020878878,0.0032491197,0.0049600718,0.008151807,0.97918963,0.000043885375],"about_ca_topic_score_codex":0.0023425294,"about_ca_topic_score_gemma":0.008429534,"teacher_disagreement_score":0.017247917,"about_ca_system_score_codex":0.0007072965,"about_ca_system_score_gemma":0.00083441695,"threshold_uncertainty_score":0.057700038},"labels":[],"label_agreement":null},{"id":"W2974813952","doi":"10.1061/(asce)wr.1943-5452.0001124","title":"Mechanical and Hydraulic Reliability Estimators for Water Distribution Systems","year":2019,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Estimator; Reliability (semiconductor); Benchmark (surveying); Reliability engineering; Entropy (arrow of time); Computer science; Hydraulic machinery; Mathematical optimization; Mathematics; Statistics; Engineering","score_opus":0.0063274733813242405,"score_gpt":0.19411223101801175,"score_spread":0.1877847576366875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974813952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016197199,0.00047656093,0.98195803,0.00006603363,0.000017068589,0.000024305367,0.000042603548,0.00010942638,0.0011087027],"genre_scores_gemma":[0.8488201,0.00054950785,0.1488106,0.000050809296,0.00009411911,0.00014650523,0.00027125375,0.000041784424,0.0012152408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981351,0.0008913872,0.00008674494,0.00023721113,0.0005844073,0.00006508182],"domain_scores_gemma":[0.99300826,0.0044340286,0.0012395171,0.00038528338,0.0008698308,0.0000631018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037551085,0.0006073186,0.0006908492,0.0014665884,0.00019922624,0.0006830593,0.0005629172,0.00062425755,0.0009182605],"category_scores_gemma":[0.018160932,0.00022934265,0.00045319696,0.00083720766,0.00054431544,0.0013428451,0.00072659936,0.0006458951,0.00018880321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006618447,0.00002987464,0.0021409574,0.0001072279,0.000050650437,0.00002264247,0.00004063462,0.901572,0.0030906175,0.024303569,0.00044951175,0.06812621],"study_design_scores_gemma":[0.000003938614,0.000054291148,0.0011372216,0.0000150808955,0.000008133261,0.00002658751,0.000007902507,0.9889819,0.0012488179,0.007822189,0.0006829035,0.000011095876],"about_ca_topic_score_codex":0.00069491885,"about_ca_topic_score_gemma":0.00061037496,"teacher_disagreement_score":0.0037551085,"about_ca_system_score_codex":0.0005658859,"about_ca_system_score_gemma":0.0005006772,"threshold_uncertainty_score":0.019859135},"labels":[],"label_agreement":null},{"id":"W2978810585","doi":"10.3390/s19194317","title":"Pressure Signal Enhancement of Slowly Increasing Leaks Using Digital Compensator Based on Acoustic Sensor","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Leak; Pipeline transport; SIGNAL (programming language); Pipeline (software); Acoustics; Engineering; Digital signal; Compensation (psychology); Computer science; Control theory (sociology); Electronic engineering; Digital signal processing; Physics; Mechanical engineering; Artificial intelligence","score_opus":0.007855727732628357,"score_gpt":0.1899708691984571,"score_spread":0.18211514146582874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978810585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29723474,0.0011359403,0.6965341,0.0003058055,0.00017199328,0.00009614268,0.000056818008,0.0014665525,0.0029979276],"genre_scores_gemma":[0.8786304,0.0006280063,0.11810821,0.0001631232,0.000063229374,0.000050574334,0.000050437928,0.000055662396,0.002250367],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966514,0.000044621564,0.000015531554,0.00008128195,0.00016938771,0.00002416777],"domain_scores_gemma":[0.99960774,0.0001321812,0.000071696995,0.00003303446,0.00013876324,0.000016674812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033986138,0.0005131231,0.00030562445,0.0005017861,0.00017913041,0.00028694244,0.0004909746,0.00048103443,0.000769539],"category_scores_gemma":[0.0007652784,0.00021885177,0.00025433893,0.0003555586,0.0003520098,0.0010379624,0.00040653086,0.00030448727,0.0001538036],"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":[0.00021042967,0.00005007685,0.0011004885,0.00022429321,0.00001647257,0.00008366403,0.000121648045,0.0036996864,0.88494194,0.0005732282,0.0004274229,0.10855061],"study_design_scores_gemma":[0.00006541574,0.0008808903,0.0051099868,0.00002770778,0.000081346094,0.00044496235,0.00008592179,0.15295802,0.8319858,0.0004230554,0.007850691,0.000086185064],"about_ca_topic_score_codex":0.00036801258,"about_ca_topic_score_gemma":0.00051673554,"teacher_disagreement_score":0.000769539,"about_ca_system_score_codex":0.0002163158,"about_ca_system_score_gemma":0.00022986103,"threshold_uncertainty_score":0.0025743246},"labels":[],"label_agreement":null},{"id":"W2981951106","doi":"10.4095/299766","title":"Recharge offsetting - maintaining recharge in an urban environment","year":2017,"lang":"en","type":"report","venue":"","topic":"Water Systems and 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":"Groundwater recharge; Environmental science; Water resource management; Hydrology (agriculture); Geography; Business; Geology; Groundwater; Geotechnical engineering; Aquifer","score_opus":0.039476375873651055,"score_gpt":0.25489836945114713,"score_spread":0.21542199357749608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981951106","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":[0.5185922,0.0024486152,0.04580427,0.020351283,0.00040653648,0.0005683468,0.0018056273,0.0019395408,0.4080836],"genre_scores_gemma":[0.9482519,0.0010982025,0.013650515,0.0008756719,0.000049289887,0.000102841186,0.0007062033,0.00013880932,0.03512663],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993643,0.00016845872,0.000019527479,0.000053667365,0.00018271377,0.00021127464],"domain_scores_gemma":[0.9995671,0.00003394905,0.000039799175,0.00005141307,0.0001928646,0.00011477459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006828184,0.00022595926,0.00016220643,0.00040352962,0.0016126888,0.0020058833,0.00081738195,0.000479841,0.008026522],"category_scores_gemma":[0.0012239575,0.00012976749,0.0002558771,0.00089911744,0.0009495717,0.0013549428,0.0027307759,0.0005903232,0.00082280266],"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":[0.00030757987,0.00061152846,0.09160133,0.0010428808,0.00007805242,0.0022072333,0.004969203,0.027083628,0.013454202,0.18170254,0.15274604,0.52419573],"study_design_scores_gemma":[0.00007540982,0.00046251994,0.070351556,0.0004356691,0.000082268474,0.0006256785,0.00918448,0.01374044,0.009624239,0.03577943,0.85953766,0.00010064376],"about_ca_topic_score_codex":0.10336595,"about_ca_topic_score_gemma":0.35869792,"teacher_disagreement_score":0.10336595,"about_ca_system_score_codex":0.0036116915,"about_ca_system_score_gemma":0.010579764,"threshold_uncertainty_score":0.20552862},"labels":[],"label_agreement":null},{"id":"W2985518870","doi":"10.1016/j.scitotenv.2019.134436","title":"Risk assessment of hybrid rain harvesting system and other small drinking water supply systems by game theory and fuzzy logic modeling","year":2019,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Water Systems and Optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China","keywords":"Rainwater harvesting; Fuzzy logic; Environmental science; Risk assessment; Computer science; Water supply; Reliability (semiconductor); Cloud computing; Risk analysis (engineering); Environmental economics; Environmental engineering; Business; Computer security","score_opus":0.006839757996480919,"score_gpt":0.1738347382860892,"score_spread":0.16699498028960827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985518870","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":[0.54334986,0.00044491983,0.44715148,0.00046717198,0.000040162184,0.00013600952,0.00014672542,0.00011485855,0.008148728],"genre_scores_gemma":[0.9944289,0.000071926166,0.0036755726,0.000013433598,0.0000067038304,0.000018891693,0.000020367072,0.0000055597116,0.0017587831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936324,0.00026034797,0.000023049599,0.00010146458,0.00014047253,0.0001114753],"domain_scores_gemma":[0.9988193,0.00079054845,0.00015160137,0.000027074635,0.00015722534,0.000054260177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013129307,0.0007238583,0.00073809543,0.00094819843,0.00052736735,0.0014872,0.0008582282,0.0009558367,0.0013430057],"category_scores_gemma":[0.0019461856,0.0003711453,0.0010370787,0.0005274387,0.0007136971,0.0016660169,0.00079263316,0.00050897454,0.000051285322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049564136,0.000032567106,0.0013646573,0.000017703998,0.00004704616,0.0001011443,0.0000332598,0.98805654,0.00069800817,0.0067122215,0.00012506507,0.002762239],"study_design_scores_gemma":[0.000002895921,0.000017097957,0.0002644132,0.0000011398523,0.000011273717,0.0000081484295,0.0000145122585,0.9977704,0.000090336805,0.0017752344,0.00004081312,0.0000037829411],"about_ca_topic_score_codex":0.020683596,"about_ca_topic_score_gemma":0.013494671,"teacher_disagreement_score":0.020683596,"about_ca_system_score_codex":0.00176979,"about_ca_system_score_gemma":0.0010093868,"threshold_uncertainty_score":0.04112637},"labels":[],"label_agreement":null},{"id":"W2988736739","doi":"10.1002/wer.1268","title":"Characterizing the settleability of grit particles","year":2019,"lang":"en","type":"article","venue":"Water Environment Research","topic":"Water Systems and 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":"Settling; Grit; Elutriation; Particle size; Materials science; Particle (ecology); Environmental science; Process engineering; Environmental engineering; Chemical engineering; Engineering; Chemistry; Geology","score_opus":0.025936789590984992,"score_gpt":0.23306725790064886,"score_spread":0.20713046830966386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988736739","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":[0.9651079,0.0006575111,0.031274166,0.000047396858,0.000022674929,0.00013622132,0.0004214127,0.000126301,0.0022063837],"genre_scores_gemma":[0.9657903,0.000692055,0.02933517,0.000059385802,0.0000092783375,0.00009041852,0.000949699,0.00006880647,0.0030048373],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992061,0.00006464452,0.000046073033,0.00018353757,0.00041852053,0.000081048835],"domain_scores_gemma":[0.9990827,0.00035284966,0.00014794094,0.00006705949,0.0003147438,0.00003467024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000933112,0.00041052338,0.0003420196,0.0009605245,0.000413169,0.0010587545,0.0003922169,0.0005176063,0.0009456807],"category_scores_gemma":[0.0016303469,0.00015273731,0.00052306,0.00058805756,0.0002651173,0.0006019039,0.00039145676,0.00050135754,0.00043895602],"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":[0.00014189625,0.00007220523,0.009588779,0.00012305714,0.00002193878,0.000048409216,0.00015730844,0.0018995542,0.9627379,0.00017057717,0.00009852695,0.024939863],"study_design_scores_gemma":[0.000006974347,0.00048433826,0.023849906,0.0000078461435,0.00001933109,0.0000674886,0.00011071482,0.009326662,0.96470183,0.00009702856,0.0013068629,0.000021005442],"about_ca_topic_score_codex":0.0044441037,"about_ca_topic_score_gemma":0.0070384094,"teacher_disagreement_score":0.0044441037,"about_ca_system_score_codex":0.00070037233,"about_ca_system_score_gemma":0.00032423486,"threshold_uncertainty_score":0.008836448},"labels":[],"label_agreement":null},{"id":"W2990287639","doi":"10.1088/1755-1315/22/0/001002","title":"Sponsorship Statement","year":2014,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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":"Statement (logic); Political science; Engineering; Management; Library science; Aeronautics; Humanities; Art; Computer science; Economics; Law","score_opus":0.009375068173620077,"score_gpt":0.17202524401227207,"score_spread":0.162650175838652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990287639","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":[0.0027084006,0.010536921,0.005616918,0.13260527,0.2988783,0.0019353399,0.035780758,0.0020264324,0.5099117],"genre_scores_gemma":[0.011336283,0.0036372198,0.0027195134,0.014819958,0.025895242,0.0008869992,0.012859211,0.00085219915,0.92699337],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951191,0.00056321756,0.00028949932,0.0004981803,0.002706566,0.0008234801],"domain_scores_gemma":[0.9482819,0.0023580163,0.0016574737,0.0021900875,0.0274548,0.018057786],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0061810333,0.000893286,0.0017073699,0.002128897,0.0017118565,0.0055205193,0.002014279,0.0033698604,0.47196352],"category_scores_gemma":[0.025026055,0.00035293185,0.00055304874,0.0020179083,0.0004971244,0.0015384767,0.0036233515,0.002656878,0.34692365],"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":[0.00013651681,0.000017705592,0.00020162738,0.00011485008,0.0000035079026,0.00005061591,0.000025237734,0.000040462925,0.00031165112,0.0016544083,0.9692807,0.028162716],"study_design_scores_gemma":[0.000031055508,0.000023305216,0.00055101246,0.00010364044,0.0000026856533,0.00005739382,0.000045597644,0.000106788495,0.00011526641,0.0005181785,0.9984397,0.0000053557897],"about_ca_topic_score_codex":0.0032472569,"about_ca_topic_score_gemma":0.0055004065,"teacher_disagreement_score":0.5280365,"about_ca_system_score_codex":0.001922737,"about_ca_system_score_gemma":0.010153087,"threshold_uncertainty_score":0.7531799},"labels":[],"label_agreement":null},{"id":"W2990940339","doi":"10.1016/j.jenvman.2019.109869","title":"A methodology to prioritize spatio-temporal monitoring of drinking water quality considering population vulnerability","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water Systems and 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":"Université Laval","funders":"","keywords":"Water quality; Vulnerability (computing); Population; Water supply; Environmental science; Adaptive capacity; Environmental resource management; Index (typography); Geography; Computer science; Environmental engineering; Environmental health; Climate change; Ecology","score_opus":0.031248125639082754,"score_gpt":0.26643566138710456,"score_spread":0.23518753574802181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990940339","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":[0.007847639,0.000113502334,0.9909685,0.00012073034,0.000027772667,0.00008083066,0.00008596095,0.0001317376,0.0006232713],"genre_scores_gemma":[0.28342873,0.00020944347,0.7137662,0.00008960063,0.00007773272,0.00032144587,0.00024076193,0.000044804565,0.0018212659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875,0.0003868369,0.00009722283,0.00029977626,0.00032368908,0.00014237314],"domain_scores_gemma":[0.9977323,0.0012590211,0.00023418693,0.00012429415,0.0005437568,0.00010633857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002516573,0.0009596568,0.0011037114,0.002805551,0.00057787716,0.001484757,0.0016226387,0.0012074652,0.0019079119],"category_scores_gemma":[0.0045496346,0.0005215115,0.0012087494,0.0018619496,0.0006327412,0.0012221669,0.0015351798,0.00079282426,0.00020845911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009399068,0.00021698937,0.0063778856,0.00022557408,0.0003100242,0.00023499518,0.00019068934,0.68983793,0.009250297,0.030102815,0.0021035033,0.26105532],"study_design_scores_gemma":[0.000010251633,0.000038373284,0.000503852,0.000008857002,0.000028639464,0.0000448939,0.000028661378,0.9913317,0.000984248,0.006186095,0.00082312396,0.000011321172],"about_ca_topic_score_codex":0.01111055,"about_ca_topic_score_gemma":0.010909379,"teacher_disagreement_score":0.01111055,"about_ca_system_score_codex":0.0012169871,"about_ca_system_score_gemma":0.002969797,"threshold_uncertainty_score":0.022091746},"labels":[],"label_agreement":null},{"id":"W2991203936","doi":"10.1139/cjce-2019-0481","title":"Water pipe failure prediction and risk models: state-of-the-art review","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":67,"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":"Purdue University","keywords":"Pipeline (software); Predictive modelling; Computer science; Statistical model; Work (physics); Risk analysis (engineering); Risk assessment; Reliability engineering; Engineering; Machine learning","score_opus":0.0038469683508518416,"score_gpt":0.13845705219157206,"score_spread":0.1346100838407202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991203936","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":[0.0010944001,0.98490715,0.009210497,0.0014005314,0.0003930887,0.000024877612,0.0003355939,0.00007147742,0.0025623986],"genre_scores_gemma":[0.010305028,0.9850445,0.003072373,0.00021664827,0.0005144893,0.000029072902,0.00032902672,0.000015219252,0.0004736171],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987336,0.00031597403,0.00020659117,0.0002520563,0.00044351147,0.00004828021],"domain_scores_gemma":[0.9880926,0.009497369,0.00060816726,0.00020973888,0.0015098923,0.00008221548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034130937,0.0014347804,0.0020538666,0.004381833,0.00033233644,0.0025653641,0.0019752006,0.0014348064,0.0040307506],"category_scores_gemma":[0.01341184,0.0005733633,0.0018382182,0.0069139446,0.0005670043,0.002890576,0.0007308575,0.0012214512,0.0014032492],"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":[0.000076455966,0.0000997579,0.0025257228,0.04390411,0.00053126813,0.00012533176,0.00017167733,0.012115766,0.00031890333,0.017202415,0.030280737,0.8926479],"study_design_scores_gemma":[0.000038439677,0.00047233322,0.009023669,0.06595517,0.003800701,0.0011017292,0.0009060183,0.038781535,0.0020232645,0.044364322,0.83323693,0.00029581884],"about_ca_topic_score_codex":0.006246614,"about_ca_topic_score_gemma":0.0039662337,"teacher_disagreement_score":0.006246614,"about_ca_system_score_codex":0.0010617033,"about_ca_system_score_gemma":0.0037679349,"threshold_uncertainty_score":0.018050373},"labels":[],"label_agreement":null},{"id":"W2991839335","doi":"10.2175/193864705784291394","title":"A TALE OF TWO PROCESSES - PILOTING OF HIGH RATE TREATMENT PROCESSES AT WOODWARD AVENUE WASTEWATER TREATMENT PLANT","year":2005,"lang":"en","type":"article","venue":"Proceedings of the Water Environment Federation","topic":"Water Systems and 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":"Wastewater; Stormwater; Sewage treatment; Combined sewer; Environmental science; Population; Environmental engineering; Hydrology (agriculture); Engineering; Ecology; Biology; Surface runoff; Demography; Sociology","score_opus":0.008860202302949026,"score_gpt":0.17292879893683324,"score_spread":0.1640685966338842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991839335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21686342,0.011356119,0.4696435,0.024018629,0.004580498,0.007057896,0.0022009634,0.014916962,0.24936198],"genre_scores_gemma":[0.41660756,0.0065263347,0.2332786,0.0030488754,0.0006497079,0.0009444025,0.0022315825,0.0012071135,0.3355059],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985019,0.00016924288,0.00006126147,0.00026951372,0.0007186368,0.00027949113],"domain_scores_gemma":[0.99915385,0.00009734399,0.000044717395,0.00013185333,0.00031916556,0.00025307157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016484461,0.0010782158,0.00075157743,0.00066664134,0.0021653457,0.0022919518,0.0010062385,0.0012232512,0.021513386],"category_scores_gemma":[0.0010147053,0.00068305404,0.0008858897,0.00071311946,0.00073650153,0.0014889701,0.0018812913,0.002188693,0.0044142813],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023413845,0.0016934935,0.0043485877,0.0007714141,0.00014423249,0.00072839233,0.0010227786,0.023301154,0.086960144,0.007920379,0.09064488,0.78012305],"study_design_scores_gemma":[0.00073400734,0.008708431,0.026434146,0.00029077948,0.00018834586,0.0009277762,0.0018693212,0.036961097,0.14040658,0.01256125,0.77051187,0.00040650327],"about_ca_topic_score_codex":0.019466925,"about_ca_topic_score_gemma":0.0321863,"teacher_disagreement_score":0.021513386,"about_ca_system_score_codex":0.003025521,"about_ca_system_score_gemma":0.0050030844,"threshold_uncertainty_score":0.07196939},"labels":[],"label_agreement":null},{"id":"W2992584058","doi":"10.1016/j.watres.2019.115369","title":"Can routine monitoring of E. coli fully account for peak event concentrations at drinking water intakes in agricultural and urban rivers?","year":2019,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and 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":"Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Sampling (signal processing); Hydrometeorology; Water quality; Hydrology (agriculture); Metric (unit); Event (particle physics); Parametric statistics; Statistics; Contamination; Surface water; Environmental engineering; Mathematics; Ecology; Meteorology; Computer science; Biology; Geography","score_opus":0.02551532733404057,"score_gpt":0.26352817202005246,"score_spread":0.2380128446860119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992584058","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":[0.86574066,0.0046244306,0.11206611,0.0066284817,0.00066288514,0.00019037674,0.0052512875,0.0009844337,0.003851247],"genre_scores_gemma":[0.98232687,0.0007126675,0.014419707,0.0006296085,0.00011066051,0.000040162413,0.0011644205,0.000048547743,0.0005473189],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843234,0.00053495436,0.0001477057,0.0004417462,0.0002059017,0.0002373137],"domain_scores_gemma":[0.99657464,0.0012277116,0.0007319917,0.0005756791,0.0007537314,0.00013639743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032392426,0.0011220494,0.0017779592,0.00055374263,0.0003049934,0.0015272471,0.0015575049,0.0019994027,0.0007155587],"category_scores_gemma":[0.0105062695,0.0005598366,0.0011287739,0.00100547,0.00032609116,0.0020495437,0.00068165397,0.0009591358,0.00031204085],"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":[0.00062150153,0.0003413955,0.82270324,0.00044830868,0.0008202748,0.00029600156,0.00017373277,0.054732937,0.006977551,0.0009955994,0.0063003567,0.105589084],"study_design_scores_gemma":[0.000086141335,0.00069905724,0.5430277,0.00019486209,0.0008312911,0.0005544996,0.0007809194,0.42597893,0.0070637707,0.009455323,0.011238053,0.000089500485],"about_ca_topic_score_codex":0.021458702,"about_ca_topic_score_gemma":0.02978131,"teacher_disagreement_score":0.021458702,"about_ca_system_score_codex":0.00043221246,"about_ca_system_score_gemma":0.0017702685,"threshold_uncertainty_score":0.042667627},"labels":[],"label_agreement":null},{"id":"W2997397387","doi":"10.1002/9781119300762.wsts0172","title":"Advanced Water Main Deterioration Model Using Bayesian Geoadditive Quantile Regression","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Covariate; Quantile regression; Quantile; Econometrics; Breakage; Nonlinear system; Bayesian probability; Quantile function; Computer science; Regression; Statistics; Mathematics; Probability density function; Cumulative distribution function","score_opus":0.007198951984544449,"score_gpt":0.21080427346166336,"score_spread":0.2036053214771189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997397387","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":[0.06963125,0.0003822044,0.92633224,0.0003733444,0.000028017992,0.000060132657,0.0006747064,0.00033526766,0.0021828574],"genre_scores_gemma":[0.96498954,0.00043608848,0.026324155,0.00009133678,0.000034194145,0.00011982328,0.0007860998,0.000059469585,0.007159405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913174,0.00040483588,0.000028834706,0.00020706357,0.00011245961,0.00011516363],"domain_scores_gemma":[0.99843794,0.00094948965,0.00025356523,0.000086510896,0.00022875096,0.00004376151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026046988,0.00055327016,0.0011074967,0.0006274999,0.00019638047,0.0008278622,0.001508955,0.00091309735,0.0031297246],"category_scores_gemma":[0.004078322,0.0005004296,0.0008951315,0.0008368838,0.00067374605,0.000685123,0.0007815869,0.001161816,0.00044011849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004462748,0.000027454444,0.0020234094,0.00003195276,0.00003605908,0.000059826754,0.000029612891,0.9786633,0.00038377245,0.0110627925,0.00042764802,0.007209496],"study_design_scores_gemma":[0.0000031474021,0.000007688941,0.00049233023,0.000002635367,0.0000051532083,0.000005253358,0.00000293467,0.99741024,0.000045033514,0.0019060274,0.00011601405,0.0000035712458],"about_ca_topic_score_codex":0.018983403,"about_ca_topic_score_gemma":0.008767021,"teacher_disagreement_score":0.018983403,"about_ca_system_score_codex":0.0008833026,"about_ca_system_score_gemma":0.00075527205,"threshold_uncertainty_score":0.037745774},"labels":[],"label_agreement":null},{"id":"W2997480570","doi":"10.1002/9781119300762.wsts0175","title":"Pipeline Failure and Deterioration Models","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Regina","funders":"","keywords":"Pipeline (software); Inference; Predictive modelling; Computer science; Bayesian inference; Bayesian probability; Reliability engineering; Economic shortage; Statistical model; Data mining; Engineering; Machine learning; Artificial intelligence","score_opus":0.004597953900989318,"score_gpt":0.1671714019514471,"score_spread":0.1625734480504578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997480570","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":[0.098253995,0.0058852276,0.80229855,0.0028004674,0.00047993803,0.00023844153,0.011554997,0.0021253054,0.07636312],"genre_scores_gemma":[0.88686204,0.0036222113,0.018121285,0.00020581628,0.00022477347,0.00031356432,0.0059660855,0.00018107377,0.08450326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991468,0.00016993057,0.000049394122,0.00025899906,0.00019552771,0.00017922914],"domain_scores_gemma":[0.998852,0.00044559452,0.00024145133,0.00006637834,0.00035127072,0.000043219465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014269834,0.0016134275,0.0012873485,0.0023884997,0.00062293606,0.0016203922,0.0028137125,0.002386175,0.009683121],"category_scores_gemma":[0.0034192626,0.00055730395,0.0015349772,0.0023666013,0.0012223258,0.0016260651,0.0011943727,0.0017846328,0.002498757],"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":[0.00003222068,0.000021943926,0.0021667373,0.000093116076,0.000026261463,0.00010444918,0.000053373173,0.95505357,0.0003054303,0.025860274,0.0035929037,0.012689729],"study_design_scores_gemma":[0.000006604247,0.000019379942,0.0011253671,0.000021838467,0.00001773825,0.00004256906,0.000015389112,0.98510855,0.0001329339,0.010238321,0.003258153,0.000013112862],"about_ca_topic_score_codex":0.05853876,"about_ca_topic_score_gemma":0.021835124,"teacher_disagreement_score":0.05853876,"about_ca_system_score_codex":0.002376328,"about_ca_system_score_gemma":0.0016545469,"threshold_uncertainty_score":0.11639607},"labels":[],"label_agreement":null},{"id":"W2997608257","doi":"10.1002/9781119300762.wsts0178","title":"Water Infrastructure and Asset Management","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Regina","funders":"","keywords":"Business; Asset (computer security); Asset management; Prosperity; Risk analysis (engineering); Environmental economics; Water supply; Service (business); Environmental resource management; Environmental planning; Finance; Computer science; Environmental science; Computer security; Economics; Environmental engineering","score_opus":0.0020884548348501647,"score_gpt":0.16183499980053084,"score_spread":0.1597465449656807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997608257","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":[0.022303652,0.01889366,0.10391524,0.018210458,0.00061380805,0.00015060429,0.0004865527,0.00021903537,0.83520705],"genre_scores_gemma":[0.81585974,0.024541672,0.024533799,0.00093599036,0.0004708848,0.00013227525,0.0004394243,0.00006052791,0.13302556],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995571,0.00014610974,0.00001848127,0.000054373373,0.00014264442,0.00008130069],"domain_scores_gemma":[0.9996773,0.00007929839,0.00006046406,0.000027484697,0.000110072135,0.000045384582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004966181,0.0004289661,0.00016982673,0.0015205288,0.0008328505,0.0036982445,0.0006613636,0.0010472289,0.0146948425],"category_scores_gemma":[0.0012566873,0.00011600946,0.00021732281,0.0017834615,0.0017083943,0.0024144598,0.0020667787,0.000781008,0.0011450071],"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":[0.0000053687227,0.000027692922,0.00092705595,0.00009275998,0.0000075851544,0.000107559346,0.00014889507,0.01769461,0.00025073063,0.863086,0.015067478,0.10258433],"study_design_scores_gemma":[0.000004761505,0.00003236959,0.0019365251,0.00036898986,0.0000111411055,0.00017173229,0.001041739,0.027866248,0.00059358514,0.63315755,0.33479235,0.000023103097],"about_ca_topic_score_codex":0.0135731585,"about_ca_topic_score_gemma":0.010988699,"teacher_disagreement_score":0.0146948425,"about_ca_system_score_codex":0.0030021528,"about_ca_system_score_gemma":0.0026343963,"threshold_uncertainty_score":0.04915911},"labels":[],"label_agreement":null},{"id":"W2997700958","doi":"10.1002/9781119300762.wsts0185","title":"Water Distribution Systems: Hydraulics and Quality Modeling","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Water quality; Context (archaeology); Urbanization; Quality (philosophy); Hydraulics; Environmental science; Population; Environmental engineering; Civil engineering; Engineering; Geography","score_opus":0.010384671143610563,"score_gpt":0.2025495725067733,"score_spread":0.19216490136316275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997700958","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":[0.026473561,0.0057164324,0.85452616,0.0024365406,0.00031947647,0.00029011592,0.0040305713,0.0023709899,0.103836134],"genre_scores_gemma":[0.8286807,0.011702959,0.07077574,0.00036279982,0.00038333607,0.0006232162,0.0030721624,0.00038533346,0.08401379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948955,0.00012832314,0.00003175357,0.00009460863,0.00020092085,0.00005485353],"domain_scores_gemma":[0.9995322,0.00016084306,0.00007054659,0.000030666888,0.00018575318,0.000020018017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048929505,0.0009112361,0.00074330077,0.0011272084,0.00062770623,0.0021000926,0.001365939,0.0015305796,0.009042915],"category_scores_gemma":[0.0013641989,0.00041611728,0.00083101535,0.0022054245,0.0006852464,0.0019199463,0.0010893081,0.0008103232,0.0016682557],"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":[0.000016491964,0.00003574028,0.00090891944,0.00013203434,0.000017964932,0.000060542607,0.000030429967,0.94104946,0.0005936304,0.023869408,0.0045718667,0.028713478],"study_design_scores_gemma":[0.000009263242,0.000017766497,0.00040843483,0.000036013873,0.000013057012,0.000033076343,0.000024663375,0.9697882,0.0003601406,0.011485965,0.017808726,0.000014656305],"about_ca_topic_score_codex":0.028852265,"about_ca_topic_score_gemma":0.01079103,"teacher_disagreement_score":0.028852265,"about_ca_system_score_codex":0.0019217655,"about_ca_system_score_gemma":0.0018734247,"threshold_uncertainty_score":0.057368636},"labels":[],"label_agreement":null},{"id":"W2998017130","doi":"10.1002/9781119300762.wsts0197","title":"Energy Use in Water Distribution Systems: A Life Cycle Perspective","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Environmental economics; Environmental science; Sizing; Climate change; Energy (signal processing); Environmental resource management; Water cycle; Life-cycle assessment; Distribution (mathematics); Computer science; Environmental planning; Production (economics); Ecology; Economics","score_opus":0.004901726874482779,"score_gpt":0.1761558089353746,"score_spread":0.17125408206089182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998017130","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":[0.07692558,0.10654667,0.27228636,0.009041462,0.0006212449,0.00026280267,0.0037621504,0.00032180897,0.53023195],"genre_scores_gemma":[0.7988211,0.092229635,0.050959583,0.00067181786,0.000316498,0.00027855198,0.0018772723,0.00021068158,0.054634877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99937445,0.00020895638,0.000026209636,0.00005196695,0.00028710437,0.00005137072],"domain_scores_gemma":[0.9995153,0.00016959604,0.00005840259,0.000025282161,0.00020588614,0.000025565105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066795043,0.000719879,0.00025339954,0.0024786214,0.00039115295,0.0024994833,0.0006401281,0.0006328365,0.0058275517],"category_scores_gemma":[0.00083050743,0.00020443145,0.0004502094,0.0034827208,0.0006431932,0.0020901053,0.0009010101,0.0006155701,0.00082156825],"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":[0.000057167097,0.00016364254,0.0049361554,0.0012912222,0.0000881998,0.0003162891,0.00021567798,0.20890862,0.0041512228,0.42762324,0.01585601,0.33639267],"study_design_scores_gemma":[0.000009551051,0.00030222605,0.00727504,0.0018036376,0.00008639001,0.00052667095,0.0011015546,0.15137899,0.007814035,0.31482995,0.514766,0.00010594098],"about_ca_topic_score_codex":0.006638455,"about_ca_topic_score_gemma":0.00642803,"teacher_disagreement_score":0.006638455,"about_ca_system_score_codex":0.0026188814,"about_ca_system_score_gemma":0.0012066242,"threshold_uncertainty_score":0.01949507},"labels":[],"label_agreement":null},{"id":"W2998471261","doi":"10.1002/9781119300762.wsts0170","title":"Pipe Failure Prediction with Consideration of Climate Change","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and 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 Victoria; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Golder Associates (Canada)","funders":"","keywords":"Environmental science; Coupled model intercomparison project; Climate change; Hydrology (agriculture); Climatology; Climate model; Geotechnical engineering; Engineering; Geology","score_opus":0.0053748253094489705,"score_gpt":0.16902265418368337,"score_spread":0.1636478288742344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998471261","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":[0.93777037,0.00022643701,0.056284156,0.00019900515,0.00003453557,0.000027753737,0.0022338005,0.0003849223,0.0028390961],"genre_scores_gemma":[0.9966994,0.000049448907,0.0021932768,0.0000066151556,0.0000052641376,0.000009285851,0.0006038916,0.000007905392,0.00042486124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998741,0.000034660723,0.0000057554957,0.000034959667,0.000028160759,0.000022408589],"domain_scores_gemma":[0.9996748,0.00012146325,0.00006701028,0.000019414352,0.00009548846,0.000021916794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045518353,0.00041978856,0.00027366582,0.00062200846,0.00015367885,0.00041130194,0.0003254681,0.00035007714,0.000771068],"category_scores_gemma":[0.0011085712,0.00015426376,0.00039292572,0.00049417955,0.00011344772,0.00027835535,0.0003019051,0.00031348312,0.0001491954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018441548,0.000014395175,0.019585727,0.000010124408,0.0000174763,0.000020586576,0.000009185347,0.9736221,0.00049995916,0.00018644657,0.00025113474,0.0057644662],"study_design_scores_gemma":[0.000001436089,0.000008855612,0.011891458,0.0000017121998,0.000004178179,0.0000039687775,0.0000064469773,0.9875351,0.0002048346,0.00020702275,0.00013094183,0.0000041078633],"about_ca_topic_score_codex":0.061536267,"about_ca_topic_score_gemma":0.03770425,"teacher_disagreement_score":0.061536267,"about_ca_system_score_codex":0.00063747336,"about_ca_system_score_gemma":0.0006516858,"threshold_uncertainty_score":0.12235612},"labels":[],"label_agreement":null},{"id":"W2998725312","doi":"10.1002/9781119300762.wsts0168","title":"Optimization for Booster Chlorination","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and 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; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Booster (rocketry); Chlorine; Odor; Haloacetic acids; Residual; Trihalomethane; Environmental science; Computer science; Water treatment; Waste management; Environmental engineering; Chemistry; Engineering; Algorithm","score_opus":0.005239891968949983,"score_gpt":0.1847017508861985,"score_spread":0.17946185891724853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998725312","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":[0.15943475,0.0041258903,0.76052696,0.0012772188,0.00032505803,0.00040974538,0.00078040984,0.0011783084,0.07194171],"genre_scores_gemma":[0.89768445,0.0012727544,0.08152303,0.00024923004,0.0000414248,0.00026298745,0.00042188066,0.00016316155,0.01838105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99976474,0.000057098227,0.0000073019264,0.000050919905,0.00006396809,0.000055929166],"domain_scores_gemma":[0.99965334,0.00019239145,0.00003450264,0.000019392694,0.000082164086,0.000018244944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006756493,0.0009589704,0.0009626082,0.0004952316,0.00032671227,0.00105323,0.00050369865,0.0008462234,0.0065714647],"category_scores_gemma":[0.001420564,0.00033624153,0.000832874,0.00041511687,0.00033360175,0.00059210777,0.0005035654,0.0009292468,0.00070197973],"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":[0.000120440345,0.000079552716,0.00050303177,0.00025250978,0.000027197193,0.000073569885,0.000026145832,0.96505696,0.004075809,0.005061404,0.0018303092,0.022893174],"study_design_scores_gemma":[0.000014955055,0.000087903034,0.00030652364,0.000017925078,0.000017775597,0.000013418299,0.000016549997,0.9939247,0.0017936049,0.0018378944,0.0019628566,0.0000058462433],"about_ca_topic_score_codex":0.0053340266,"about_ca_topic_score_gemma":0.003212507,"teacher_disagreement_score":0.0065714647,"about_ca_system_score_codex":0.0008564244,"about_ca_system_score_gemma":0.0011503948,"threshold_uncertainty_score":0.021983743},"labels":[],"label_agreement":null},{"id":"W2998774476","doi":"10.1061/9780784481653.010","title":"Integrated Asset Management Planning of Wastewater Collection and Treatment Systems","year":2018,"lang":"en","type":"article","venue":"Pipelines 2018","topic":"Water Systems and 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":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sustainability; Asset (computer security); Sewage treatment; Wastewater; Business; Government (linguistics); Asset management; Environmental economics; Environmental science; Computer science; Environmental engineering; Finance; Economics","score_opus":0.018807537692449455,"score_gpt":0.22035941821932267,"score_spread":0.20155188052687323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998774476","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":[0.6435689,0.0006287204,0.32950142,0.000398066,0.000033866243,0.00048173958,0.0010859168,0.00036305675,0.023938406],"genre_scores_gemma":[0.9794844,0.00016434261,0.016278666,0.000015137042,0.0000028107459,0.00012722966,0.00023671542,0.000017518696,0.0036731728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994271,0.0001434762,0.000017653152,0.000096123294,0.00011176261,0.00020399607],"domain_scores_gemma":[0.9996923,0.00010998837,0.00005255021,0.000015811907,0.00007638103,0.000052915115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006473884,0.0009304185,0.0009208372,0.0009316964,0.00069971115,0.0018881247,0.0011480778,0.001049887,0.002463785],"category_scores_gemma":[0.0012107949,0.0007751616,0.00065494596,0.0014057665,0.00067595625,0.0013257446,0.0010597728,0.0006476556,0.00017241959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016125,0.000013911692,0.00048291363,0.0000085981055,0.000008854135,0.000027931228,0.000011261638,0.99516934,0.00017855583,0.0008540939,0.000068097,0.0031603735],"study_design_scores_gemma":[0.0000059143895,0.000030954598,0.000380521,0.000003040396,0.000009773021,0.000005731504,0.000031843258,0.998123,0.00021759691,0.00090579654,0.00028113008,0.0000047271365],"about_ca_topic_score_codex":0.121252656,"about_ca_topic_score_gemma":0.11147116,"teacher_disagreement_score":0.121252656,"about_ca_system_score_codex":0.0042622825,"about_ca_system_score_gemma":0.005059542,"threshold_uncertainty_score":0.24109375},"labels":[],"label_agreement":null},{"id":"W2998800982","doi":"10.1061/9780784482506.001","title":"Recent Survey Results of Water Main Failures in the U.S. and Canada","year":2019,"lang":"en","type":"article","venue":"Pipelines 2019","topic":"Water Systems and 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":"","keywords":"Environmental science; Computer science","score_opus":0.008202615608414414,"score_gpt":0.18145124361810053,"score_spread":0.1732486280096861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998800982","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":[0.82095903,0.005352454,0.00263142,0.0029091728,0.00008098421,0.00032013812,0.11153855,0.0003163523,0.05589194],"genre_scores_gemma":[0.91607714,0.012428255,0.0020486927,0.0011536741,0.000036231115,0.00013233758,0.04431734,0.00008106732,0.023725279],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99720424,0.00010287969,0.00013811784,0.00020393541,0.001780502,0.0005702169],"domain_scores_gemma":[0.9850277,0.00057184265,0.0010301595,0.00013096095,0.012359185,0.0008802417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00118674,0.00047167612,0.00030569526,0.004515662,0.0016925039,0.0012953744,0.0006585885,0.00028712087,0.003006136],"category_scores_gemma":[0.0054447777,0.00030040607,0.00040705243,0.012132542,0.000567027,0.0006581317,0.0009647944,0.0006013973,0.00053003617],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012984799,0.00006372187,0.84684026,0.000523066,0.00012543518,0.00024152036,0.004191683,0.0020048462,0.00088507833,0.0008785474,0.053270333,0.090845734],"study_design_scores_gemma":[0.000002898633,0.00003421653,0.9643917,0.00011965293,0.000028157401,0.00007991961,0.006231493,0.00054014515,0.00045483783,0.00006398088,0.028017934,0.00003502371],"about_ca_topic_score_codex":0.99044305,"about_ca_topic_score_gemma":0.9954318,"teacher_disagreement_score":0.016849037,"about_ca_system_score_codex":0.016849037,"about_ca_system_score_gemma":0.029222911,"threshold_uncertainty_score":0.12224895},"labels":[],"label_agreement":null},{"id":"W2998874261","doi":"10.1007/978-3-030-26458-1_11","title":"Biogeography Based Optimization for Water Pump Switching Problem","year":2020,"lang":"en","type":"book-chapter","venue":"Modeling and optimization in science and technologies","topic":"Water Systems and 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":"Thompson Rivers University","funders":"","keywords":"Mathematical optimization; Optimization problem; Particle swarm optimization; Harmony search; Computer science; Genetic algorithm; Minification; Mathematics","score_opus":0.015783394658404667,"score_gpt":0.19633138161474425,"score_spread":0.18054798695633958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998874261","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":[0.025110789,0.0033390196,0.9244832,0.0014513461,0.00032877887,0.00006168624,0.00017664667,0.00012849762,0.04491999],"genre_scores_gemma":[0.79342425,0.0042059687,0.14058943,0.00069521763,0.00041118538,0.0003347437,0.000424262,0.00034600752,0.059569016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998368,0.0000694225,0.000004812947,0.00003297721,0.000033420743,0.000022648399],"domain_scores_gemma":[0.99976856,0.0001591805,0.000021203632,0.000009349822,0.000023946342,0.00001777058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004549456,0.0006482024,0.0008397743,0.00038541015,0.00030688403,0.00096603297,0.0008268192,0.0012758116,0.004973826],"category_scores_gemma":[0.0010150771,0.00040691314,0.0007527414,0.00050178013,0.00072031544,0.0009641213,0.0009529391,0.0012360876,0.00033385406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033045144,0.000028590935,0.00024028514,0.00011523015,0.00004089846,0.000045045406,0.000025704308,0.91888434,0.0013266047,0.059331078,0.0034912485,0.016437931],"study_design_scores_gemma":[0.0000042100382,0.00001132591,0.00010158257,0.0000070616784,0.0000043586265,0.000010369309,0.000005014624,0.9810209,0.000117291485,0.017677734,0.001036246,0.0000038528915],"about_ca_topic_score_codex":0.003722706,"about_ca_topic_score_gemma":0.002488141,"teacher_disagreement_score":0.004973826,"about_ca_system_score_codex":0.0009850515,"about_ca_system_score_gemma":0.0005913495,"threshold_uncertainty_score":0.016639113},"labels":[],"label_agreement":null},{"id":"W2999154415","doi":"10.1061/9780784481653.043","title":"Leakage Rate Uncertainty in Water Distribution Systems with Uncertain Demands: Impacts on Delivery Pressures","year":2018,"lang":"en","type":"article","venue":"Pipelines 2018","topic":"Water Systems and 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; McMaster University","funders":"","keywords":"Leakage (economics); Monte Carlo method; Leak; Pipeline transport; Environmental science; Petroleum engineering; Mechanics; Computer science; Engineering; Environmental engineering; Statistics; Mathematics","score_opus":0.011507215400662756,"score_gpt":0.20886335306236428,"score_spread":0.19735613766170151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999154415","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":[0.9170205,0.00039972292,0.08040797,0.00023638236,0.000012532669,0.0000225925,0.00015934976,0.00014341566,0.0015975732],"genre_scores_gemma":[0.9986206,0.000054362645,0.0012080761,0.000006463902,0.0000023537386,0.000005098258,0.000027344253,0.000008072738,0.0000675507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898845,0.0003191748,0.000058286638,0.00012855184,0.00036446773,0.00014119763],"domain_scores_gemma":[0.9910556,0.007259192,0.00083934417,0.00026732325,0.0005052332,0.0000734495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014032292,0.00054114516,0.0007134424,0.0006109858,0.00033433308,0.0009052011,0.00032181246,0.00065641885,0.00041487833],"category_scores_gemma":[0.0076940055,0.00040839377,0.00048339224,0.0006943217,0.0006401852,0.0013728106,0.0006226728,0.00064804056,0.00004489219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007170139,0.000024271922,0.0042974832,0.000047830294,0.000020150072,0.00011067964,0.000034519704,0.9864559,0.0030865986,0.00068727107,0.00006297393,0.0051005385],"study_design_scores_gemma":[0.0000051388374,0.00009000841,0.0061649326,0.000005886725,0.000016782342,0.00004565003,0.00005322833,0.9891622,0.0034863758,0.0008457645,0.00010611663,0.000017873894],"about_ca_topic_score_codex":0.004408776,"about_ca_topic_score_gemma":0.0023561262,"teacher_disagreement_score":0.004408776,"about_ca_system_score_codex":0.0008028184,"about_ca_system_score_gemma":0.00054563873,"threshold_uncertainty_score":0.008766234},"labels":[],"label_agreement":null},{"id":"W2999235517","doi":"10.1061/9780784482506.055","title":"Rigid versus Flexible Pipe Material Surge Response: A Case Study for a Raw Water Pipeline","year":2019,"lang":"en","type":"article","venue":"Pipelines 2019","topic":"Water Systems and 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":"HydraTek (Canada)","funders":"","keywords":"Water hammer; Surge; Pipeline (software); Water pipe; Pipeline transport; Transient (computer programming); Canalisation; Pipe; Nominal Pipe Size; Structural engineering; Head (geology); Materials science; Engineering; Mechanical engineering; Piping; Computer science; Composite material; Geology; Electrical engineering","score_opus":0.01718675867834463,"score_gpt":0.25149776940716945,"score_spread":0.23431101072882482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999235517","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":[0.9907795,0.000036139576,0.0054632477,0.00011686788,0.000007444932,0.000104344595,0.00019272904,0.00009240903,0.003207432],"genre_scores_gemma":[0.9948979,0.00004207651,0.0035614832,0.00001110591,0.0000037044795,0.00003176526,0.000054180862,0.00001325856,0.0013845884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"case_report","domain_scores_codex":[0.9995907,0.0001436551,0.000020782922,0.00004050792,0.00013653794,0.00006784861],"domain_scores_gemma":[0.99812216,0.0013961397,0.00012368656,0.00007882395,0.00019054224,0.00008865348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060997054,0.0006759237,0.0004298823,0.0005695935,0.0006795216,0.00058769726,0.000774785,0.0014219751,0.0025515095],"category_scores_gemma":[0.0019553541,0.00043559217,0.00069021416,0.00055764534,0.00061614654,0.000614236,0.00044918418,0.0006904112,0.00024146706],"study_design_candidate":"case_report","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":[0.0008115066,0.0011562898,0.01543035,0.00059261825,0.00006466529,0.0070118043,0.0009863962,0.91550666,0.035142932,0.0026980527,0.0017874168,0.0188114],"study_design_scores_gemma":[0.00028427166,0.0061205155,0.01697577,0.000037838956,0.00008024193,0.00095500227,0.002269675,0.9325805,0.036186162,0.0015103874,0.0029021131,0.00009758437],"about_ca_topic_score_codex":0.0058669373,"about_ca_topic_score_gemma":0.009891154,"teacher_disagreement_score":0.0058669373,"about_ca_system_score_codex":0.0007582501,"about_ca_system_score_gemma":0.00053228455,"threshold_uncertainty_score":0.011665583},"labels":[],"label_agreement":null},{"id":"W2999960383","doi":"10.1061/9780784481653.029","title":"River Crossing Inspections—Balancing Risk with the Need to Obtain Advanced Pipeline Condition Assessment Data","year":2018,"lang":"en","type":"article","venue":"Pipelines 2018","topic":"Water Systems and 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":"Aecom (Canada)","funders":"","keywords":"Software deployment; Pipeline transport; Computer science; Engineering; Risk analysis (engineering)","score_opus":0.013350017476427624,"score_gpt":0.26510280584746476,"score_spread":0.25175278837103715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999960383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20321666,0.0056291977,0.674663,0.036952235,0.0005126477,0.002447398,0.0009849479,0.005165226,0.07042859],"genre_scores_gemma":[0.72949547,0.0028008735,0.25840023,0.0013046629,0.00027403323,0.00048215708,0.0007146959,0.00028462356,0.006243252],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.97618437,0.009545359,0.0013267238,0.002358069,0.009455814,0.0011297233],"domain_scores_gemma":[0.92163634,0.03795039,0.0118505545,0.006588993,0.018045535,0.003928135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027935274,0.00072493806,0.0007761972,0.005149637,0.0016873563,0.0051151584,0.0035737574,0.0018125518,0.004156009],"category_scores_gemma":[0.056782965,0.0008200849,0.0004451011,0.0020848857,0.0014986634,0.0061269407,0.0054783104,0.0018000004,0.0013562312],"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":[0.00045910032,0.00053146796,0.09720989,0.0005805653,0.00011005574,0.001016121,0.002414498,0.029012647,0.006090763,0.017730322,0.025504874,0.8193397],"study_design_scores_gemma":[0.0004060248,0.0037212025,0.25669685,0.0035567475,0.00029630138,0.012184604,0.01706542,0.3842911,0.016465787,0.10027069,0.2043849,0.000660351],"about_ca_topic_score_codex":0.0069634626,"about_ca_topic_score_gemma":0.0102398945,"teacher_disagreement_score":0.027935274,"about_ca_system_score_codex":0.0027419757,"about_ca_system_score_gemma":0.0058213007,"threshold_uncertainty_score":0.14773768},"labels":[],"label_agreement":null},{"id":"W2999982299","doi":"10.1061/9780784481660.001","title":"Water and Energy Efficiency: Transmission Operations Optimizer (TOO) City of Toronto Water Supply","year":2018,"lang":"en","type":"article","venue":"Pipelines 2018","topic":"Water Systems and 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":"IBI Group (Canada); Toronto Public Health","funders":"","keywords":"Water supply; Efficient energy use; Transmission (telecommunications); Computer science; Energy (signal processing); Environmental economics; Environmental science; Water resource management; Telecommunications; Environmental engineering; Engineering; Electrical engineering; Economics","score_opus":0.008383317634524086,"score_gpt":0.2019680846251963,"score_spread":0.19358476699067223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999982299","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":[0.4380504,0.0009805902,0.4193738,0.0009826615,0.0002067908,0.0014268117,0.005097086,0.010805247,0.12307672],"genre_scores_gemma":[0.89956576,0.00036691915,0.07824246,0.00008957449,0.000033830845,0.00029319327,0.002598355,0.00048559197,0.01832435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993482,0.000100382116,0.000024534398,0.00012406877,0.0002869499,0.00011582075],"domain_scores_gemma":[0.99965155,0.00007673362,0.00003869438,0.000022371021,0.00017839552,0.00003226282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005650481,0.0012977226,0.0005380566,0.0006983252,0.00062427716,0.0015910616,0.0004998909,0.0003427119,0.0058263256],"category_scores_gemma":[0.0011384657,0.00037175365,0.00040322024,0.0009220769,0.00037156534,0.00041527717,0.00048654494,0.00052055274,0.000482221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003249961,0.0000979528,0.0049943635,0.00016071528,0.00007071934,0.00012881742,0.00017116521,0.87671965,0.006200394,0.0050814482,0.012772804,0.09327697],"study_design_scores_gemma":[0.000055123284,0.00013512156,0.005229655,0.000017004988,0.000028262335,0.000023230345,0.000089080735,0.9792027,0.0043891487,0.00056581036,0.010235794,0.000029088957],"about_ca_topic_score_codex":0.44635013,"about_ca_topic_score_gemma":0.4677475,"teacher_disagreement_score":0.5536499,"about_ca_system_score_codex":0.0054100575,"about_ca_system_score_gemma":0.0071610427,"threshold_uncertainty_score":0.88750416},"labels":[],"label_agreement":null},{"id":"W3000272076","doi":"10.1061/9780784481653.050","title":"When Transit Expansions Requires You to Really Understand Your Water System","year":2018,"lang":"en","type":"article","venue":"Pipelines 2018","topic":"Water Systems and 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":"Toronto Public Health","funders":"","keywords":"Redundancy (engineering); Transit (satellite); Transit system; Public transport; Work (physics); Water supply; Transport engineering; Computer science; Rapid transit; Engineering; Environmental engineering","score_opus":0.02714455311024397,"score_gpt":0.2285112035677868,"score_spread":0.20136665045754285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000272076","genre_codex":"commentary","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":[0.19834049,0.01039268,0.1008727,0.40292948,0.0063075037,0.0004206749,0.0023829085,0.0047828453,0.27357072],"genre_scores_gemma":[0.80750775,0.01024037,0.040452447,0.023140822,0.0013043343,0.00010866419,0.000993268,0.0012923991,0.11495999],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990109,0.00017709618,0.00003541373,0.00008162744,0.00042147216,0.00027352315],"domain_scores_gemma":[0.997451,0.00055848097,0.0002857484,0.00016974266,0.0009850787,0.00054987916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010929722,0.00049459003,0.00043835217,0.00038389972,0.0025490702,0.0033150322,0.0006083864,0.0025300558,0.019412443],"category_scores_gemma":[0.0060221115,0.00040795346,0.0002639445,0.0004671628,0.0016193466,0.007374756,0.0017117375,0.0030928457,0.0046495586],"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":[0.00028090586,0.00015418435,0.02831176,0.0006614065,0.000048727656,0.0053600376,0.022213053,0.0073547736,0.008470559,0.038977973,0.6238744,0.2642922],"study_design_scores_gemma":[0.000029172146,0.00020456647,0.020335551,0.00050218287,0.0000369172,0.0018517922,0.046626393,0.006146806,0.0033629287,0.033137336,0.8875924,0.00017385538],"about_ca_topic_score_codex":0.07714434,"about_ca_topic_score_gemma":0.15288676,"teacher_disagreement_score":0.07714434,"about_ca_system_score_codex":0.0042578406,"about_ca_system_score_gemma":0.0043659043,"threshold_uncertainty_score":0.15339059},"labels":[],"label_agreement":null},{"id":"W3000336046","doi":"10.1016/j.ress.2020.106828","title":"Reliability of water distribution networks subjected to seismic hazard: Application of an improved entropy function","year":2020,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Water Systems and 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":"Toronto Metropolitan University","funders":"","keywords":"Reliability engineering; Serviceability (structure); Fragility; Entropy (arrow of time); Seismic hazard; Redundancy (engineering); Reliability (semiconductor); Engineering; Computer science; Structural engineering; Civil engineering","score_opus":0.0026197451242231137,"score_gpt":0.1587971235387577,"score_spread":0.15617737841453458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000336046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4583961,0.00063806056,0.53683233,0.00037751236,0.00007712291,0.000037232086,0.00011606529,0.00022576642,0.0032998023],"genre_scores_gemma":[0.991323,0.0001536318,0.007933847,0.000014324407,0.000041049767,0.000012855877,0.00004616542,0.000030280149,0.00044495254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961156,0.00023324614,0.000015595937,0.00003375389,0.00006621505,0.000039561586],"domain_scores_gemma":[0.997456,0.0018683856,0.00018758743,0.0001345738,0.00028624924,0.0000672412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018941053,0.00047882457,0.0006152962,0.00078745355,0.00023674495,0.000561425,0.00046129597,0.00054421637,0.0005463879],"category_scores_gemma":[0.0049786046,0.00021663203,0.0005050142,0.0004425913,0.0006920485,0.0009910851,0.0006913794,0.00044848784,0.000050736704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080683516,0.000022022943,0.001405878,0.00003111484,0.000025495876,0.00004416966,0.000031342013,0.98392314,0.0019711668,0.005449416,0.00016291354,0.006852722],"study_design_scores_gemma":[0.0000015235104,0.000010186132,0.00033695632,0.000001335061,0.0000037170933,0.0000040587515,0.0000026526866,0.9988483,0.0001806105,0.0005815269,0.000026958214,0.0000021969115],"about_ca_topic_score_codex":0.0030660813,"about_ca_topic_score_gemma":0.0015681402,"teacher_disagreement_score":0.0030660813,"about_ca_system_score_codex":0.00065654423,"about_ca_system_score_gemma":0.00058151025,"threshold_uncertainty_score":0.010017097},"labels":[],"label_agreement":null},{"id":"W3001400872","doi":"10.1115/imece2019-11514","title":"Modelling and Experimental Validation of a Controllable Energy Harvester for Pressure Regulation","year":2019,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Computational fluid dynamics; Turbine; Pressure drop; Mechanical engineering; Sensitivity (control systems); Power (physics); Engineering; Electricity generation; Electric generator; Automotive engineering; Simulation; Marine engineering; Mechanics; Aerospace engineering; Electronic engineering; Physics","score_opus":0.008347601282968716,"score_gpt":0.18065464827646405,"score_spread":0.17230704699349533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001400872","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":[0.92628354,0.0002266266,0.06871528,0.00006595927,0.000068326815,0.00026834407,0.0004370953,0.00062333693,0.003311429],"genre_scores_gemma":[0.9903054,0.000060749142,0.007997766,0.000007790222,0.0000032950632,0.00007574643,0.00009965904,0.000025289619,0.0014242597],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999617,0.000037889648,0.000019042991,0.00007297649,0.00021136233,0.000041663156],"domain_scores_gemma":[0.9997266,0.000096262935,0.000028605078,0.000062657826,0.00006593763,0.000020013988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062498264,0.0003245652,0.00052301213,0.00029556686,0.00032335107,0.00054895756,0.0008904694,0.0005692342,0.0023849313],"category_scores_gemma":[0.0006795993,0.0002204224,0.0003375323,0.00022959118,0.0005074,0.00051692105,0.00033447336,0.00038402347,0.00048903626],"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":[0.00052804983,0.0004692512,0.0034615279,0.0006301537,0.000035594298,0.0005495179,0.00022302453,0.10005254,0.8603301,0.0014683348,0.0009912711,0.031260666],"study_design_scores_gemma":[0.00010447461,0.002387295,0.008271545,0.000026313988,0.000039297123,0.00015867052,0.00008081291,0.2053173,0.7769421,0.00025297725,0.0063638366,0.000055360986],"about_ca_topic_score_codex":0.0007884323,"about_ca_topic_score_gemma":0.0010321131,"teacher_disagreement_score":0.0023849313,"about_ca_system_score_codex":0.000388644,"about_ca_system_score_gemma":0.0003262883,"threshold_uncertainty_score":0.00797838},"labels":[],"label_agreement":null},{"id":"W3001441818","doi":"10.7939/r3-cjja-eb52","title":"ON THE DRIVING SOURCES AND VARIABILITY OF NORTH ATLANTIC DEEP WATER","year":2019,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Water Systems and 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":"Deep water; Geology; Geography; Oceanography","score_opus":0.0021293263520342754,"score_gpt":0.11262285903858972,"score_spread":0.11049353268655544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001441818","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":[0.99780244,0.000083380466,0.0003750674,0.00027474744,0.000013593469,0.0000062095637,0.00031250136,0.000019762374,0.0011123786],"genre_scores_gemma":[0.999616,0.00003231781,0.000087709275,0.000015827083,0.0000048258235,0.0000017757952,0.00013799685,0.0000027908554,0.00010080152],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99988365,0.000021039801,0.0000075920684,0.00004550799,0.00002132581,0.000020805313],"domain_scores_gemma":[0.99951816,0.00021745494,0.00008857232,0.00004311979,0.00008092599,0.000051899922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003558353,0.00017980208,0.00018295232,0.00025328386,0.00033525104,0.00061224436,0.00025577442,0.0004849669,0.000828117],"category_scores_gemma":[0.0011784952,0.00026110615,0.00038760138,0.0003412256,0.00023501345,0.00027527806,0.0003283328,0.00034893543,0.00012836352],"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":[0.00023395603,0.0001308016,0.90989846,0.000054661807,0.00018603801,0.0003926684,0.00032123248,0.047850996,0.029443799,0.0013615161,0.0012653419,0.008860374],"study_design_scores_gemma":[0.00001720319,0.000039855066,0.90260786,0.000009680413,0.0000434436,0.000055061762,0.00018996389,0.09387323,0.0016120027,0.00046429838,0.0010672448,0.00002022555],"about_ca_topic_score_codex":0.0373493,"about_ca_topic_score_gemma":0.034803286,"teacher_disagreement_score":0.0373493,"about_ca_system_score_codex":0.00075302226,"about_ca_system_score_gemma":0.0004662595,"threshold_uncertainty_score":0.07426375},"labels":[],"label_agreement":null},{"id":"W3003124290","doi":"10.37358/rc.19.3.7014","title":"Seasonal Statistical Analysis on the Quality of Underwater Supplies in South-Eastern Romania","year":2019,"lang":"en","type":"article","venue":"Revista de Chimie","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"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":"Quarter (Canadian coin); Underwater; Statistical analysis; Sampling (signal processing); Environmental science; Cover (algebra); Resource (disambiguation); Square (algebra); Hydrology (agriculture); Geography; Statistics; Engineering; Mathematics; Computer science; Archaeology; Telecommunications; Geotechnical engineering","score_opus":0.015699608972018886,"score_gpt":0.23970314365854534,"score_spread":0.22400353468652645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003124290","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":[0.9978866,0.00006117323,0.00081592164,0.000019085595,0.0000023909733,0.000005440603,0.0003694968,0.000009700293,0.0008302063],"genre_scores_gemma":[0.99890935,0.000035115943,0.00036337395,0.0000033977924,0.0000024096344,0.000005364189,0.00044995756,0.000004340715,0.00022678211],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995617,0.00015410688,0.00003861964,0.00010120966,0.000072668,0.000071662995],"domain_scores_gemma":[0.9987596,0.0004525106,0.00033604074,0.00013007048,0.00026426796,0.000057511123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007730338,0.00008981721,0.00021004846,0.0009048432,0.00013899656,0.00030548053,0.00018485541,0.0001038557,0.00079539214],"category_scores_gemma":[0.0020761963,0.000090273934,0.0002544408,0.0013138615,0.00025491428,0.0001698656,0.0002993511,0.00014573509,0.000100913574],"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":[0.00011699953,0.000018262,0.98080266,0.000025335135,0.00011286383,0.00012319042,0.00022654772,0.004254214,0.002126404,0.0002971862,0.00027920405,0.011617038],"study_design_scores_gemma":[8.5343595e-7,0.000024369312,0.9960757,0.0000032034402,0.000008809885,0.000044327982,0.00021469298,0.0029851845,0.00022649513,0.000030144764,0.0003834455,0.0000028024028],"about_ca_topic_score_codex":0.008379431,"about_ca_topic_score_gemma":0.0125268055,"teacher_disagreement_score":0.008379431,"about_ca_system_score_codex":0.0003015487,"about_ca_system_score_gemma":0.00024257028,"threshold_uncertainty_score":0.016661286},"labels":[],"label_agreement":null},{"id":"W3003970291","doi":"10.1109/pesgm40551.2019.8973455","title":"Cubature Particle Filtering Approach for State Estimation in Electrical Distribution Systems","year":2019,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Particle filter; Robustness (evolution); Estimator; Gaussian; Computer science; Algorithm; Range (aeronautics); MATLAB; State (computer science); State estimator; Mathematical optimization; Kalman filter; Mathematics; Engineering; Artificial intelligence; Statistics","score_opus":0.006037834598712868,"score_gpt":0.1845419532902124,"score_spread":0.1785041186914995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003970291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019394011,0.00016983195,0.9971328,0.000046109824,0.000025731228,0.0000094931465,0.000017157832,0.00011749384,0.00054204214],"genre_scores_gemma":[0.56126183,0.0016914413,0.42894012,0.00017263545,0.00016180726,0.00037146328,0.00035405674,0.0001352546,0.006911391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971074,0.00010382811,0.000018181148,0.000056381657,0.00008799341,0.000022867163],"domain_scores_gemma":[0.9994041,0.0003577542,0.00005336562,0.000037262103,0.00013538371,0.000012203467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085502886,0.00050701154,0.0008848032,0.0005295678,0.00037538097,0.0007845479,0.0005405727,0.00073541095,0.0015291317],"category_scores_gemma":[0.0017079267,0.00034935898,0.00051136024,0.0007049573,0.0003906804,0.0006917534,0.00041599353,0.0009774324,0.00031757238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048308226,0.00002168863,0.0003902627,0.00011031825,0.000043393917,0.000035894558,0.00004761941,0.9273072,0.0021255994,0.013658247,0.0009124853,0.055298973],"study_design_scores_gemma":[0.000001619571,0.0000067344995,0.00006898015,0.0000029381342,0.0000021023507,0.000004241652,0.0000020112184,0.9982145,0.000247657,0.001029389,0.00041711668,0.0000027360604],"about_ca_topic_score_codex":0.008600443,"about_ca_topic_score_gemma":0.004262657,"teacher_disagreement_score":0.008600443,"about_ca_system_score_codex":0.0005924871,"about_ca_system_score_gemma":0.0008375575,"threshold_uncertainty_score":0.017100751},"labels":[],"label_agreement":null},{"id":"W3004080441","doi":"10.1002/aws2.1169","title":"Pressure surge control strategies revised","year":2020,"lang":"en","type":"article","venue":"AWWA Water Science","topic":"Water Systems and 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 Toronto; HydraTek (Canada)","funders":"","keywords":"Transient (computer programming); Surge; Risk analysis (engineering); Control (management); Computer science; Engineering; Business; Electrical engineering","score_opus":0.0077125526474198115,"score_gpt":0.173901208304123,"score_spread":0.16618865565670318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004080441","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":[0.035614323,0.00213644,0.55199164,0.0027615626,0.00090363063,0.0006469114,0.0005513636,0.0024059343,0.40298823],"genre_scores_gemma":[0.9036449,0.001332754,0.02886332,0.00042159623,0.0003275362,0.0002519336,0.00034781126,0.00017794083,0.06463218],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995882,0.000068884045,0.00002402294,0.00007943194,0.00017849842,0.000060902294],"domain_scores_gemma":[0.99966,0.00006071599,0.000042519387,0.00003397579,0.00018018277,0.0000225789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035874156,0.0009406521,0.00043277233,0.0008288954,0.0005010329,0.0015509364,0.0012258417,0.0007847972,0.024539027],"category_scores_gemma":[0.0011217672,0.00017830412,0.0003950928,0.00045435637,0.00049150584,0.001122839,0.0008765912,0.0010082816,0.0033917814],"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":[0.00040060337,0.00030177596,0.0012038171,0.0009131017,0.00009175558,0.0004710912,0.00030331974,0.25603536,0.034421705,0.116410814,0.03703372,0.552413],"study_design_scores_gemma":[0.00024305115,0.0014793357,0.0035247083,0.00033868823,0.00014162209,0.0004946758,0.0005144495,0.7648937,0.027671749,0.052291583,0.1483019,0.00010445975],"about_ca_topic_score_codex":0.0031794219,"about_ca_topic_score_gemma":0.0021139062,"teacher_disagreement_score":0.024539027,"about_ca_system_score_codex":0.0007635673,"about_ca_system_score_gemma":0.0008734934,"threshold_uncertainty_score":0.08209121},"labels":[],"label_agreement":null},{"id":"W3004433568","doi":"10.1061/(asce)wr.1943-5452.0001191","title":"Real-Time Operation of Water-Supply Canal Systems under Limited Electrical Power and/or Water Availability","year":2020,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Stantec (Canada)","funders":"","keywords":"Interdependence; Water supply; Electric power system; Electric power; Engineering; Electricity generation; Computer science; Power (physics); Environmental engineering","score_opus":0.011301153605876748,"score_gpt":0.19741937325915013,"score_spread":0.18611821965327338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004433568","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":[0.95011866,0.00011492203,0.042632494,0.0002252198,0.000019753665,0.00006562489,0.00034109538,0.0005863984,0.0058958656],"genre_scores_gemma":[0.9984414,0.00001830088,0.0010096616,0.000004202139,0.0000010362886,0.000013911002,0.000045820252,0.000005949914,0.00045971174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997706,0.00007699207,0.00001160623,0.00004285532,0.00005263086,0.000045307686],"domain_scores_gemma":[0.9993755,0.00036038036,0.000109237364,0.000026653755,0.000085824584,0.000042464577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039529492,0.00042235182,0.0004490697,0.0003337713,0.0004101426,0.0007384022,0.00038474202,0.0005283149,0.0018942676],"category_scores_gemma":[0.001262832,0.00018972719,0.00032502812,0.00028846008,0.00043853797,0.00042886657,0.00045316177,0.0003655684,0.00013928872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012888064,0.000027007327,0.0015689335,0.000027310049,0.000013553123,0.0000886207,0.00003402423,0.9916175,0.0031415639,0.00038565334,0.00016094648,0.0028059809],"study_design_scores_gemma":[0.0000071641953,0.000037657202,0.00057165854,0.0000010051147,0.0000039473125,0.0000053864014,0.000017022681,0.9983961,0.00075980584,0.00011357099,0.00008268314,0.0000040365335],"about_ca_topic_score_codex":0.019361485,"about_ca_topic_score_gemma":0.012345718,"teacher_disagreement_score":0.019361485,"about_ca_system_score_codex":0.0009827797,"about_ca_system_score_gemma":0.0008024922,"threshold_uncertainty_score":0.038497627},"labels":[],"label_agreement":null},{"id":"W3007473358","doi":"10.1016/b978-0-12-819594-9.00006-1","title":"Multibarrier approach for the removal of organic micro-pollutants","year":2020,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Contingency plan; Pollutant; Environmental planning; Process (computing); Engineering; Risk analysis (engineering); Water quality; Water treatment; Environmental science; Environmental engineering; Business; Computer science; Computer security","score_opus":0.014408668439803578,"score_gpt":0.19079222206126256,"score_spread":0.176383553621459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007473358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09037099,0.09189153,0.74747413,0.0009790929,0.0013036806,0.000145636,0.0003196469,0.0018976582,0.06561761],"genre_scores_gemma":[0.2569097,0.04479797,0.3685568,0.00070579094,0.00032696346,0.00027889071,0.00042913822,0.00051313656,0.32748166],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998479,0.000010592364,0.0000046965874,0.000031419386,0.00008712297,0.000018299537],"domain_scores_gemma":[0.9999753,0.000008710513,0.0000043515506,0.0000025618215,0.000005658614,0.0000033397987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014176304,0.0005977306,0.0007366068,0.00041606827,0.0003525463,0.0007907155,0.00090354326,0.0008032573,0.007732303],"category_scores_gemma":[0.00008501517,0.00031436302,0.0006358478,0.00050917856,0.00018774855,0.0007234925,0.00073719356,0.0012561914,0.0023684439],"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":[0.0002281128,0.00048162285,0.00018881804,0.0013713107,0.00006675658,0.0002290899,0.000106295774,0.020080646,0.56491995,0.03414623,0.006236661,0.37194455],"study_design_scores_gemma":[0.00005536269,0.001255774,0.0011697101,0.00022549977,0.00013033723,0.0009791971,0.00010441078,0.14152299,0.5600798,0.030525723,0.26385364,0.00009759602],"about_ca_topic_score_codex":0.0006337391,"about_ca_topic_score_gemma":0.001311142,"teacher_disagreement_score":0.007732303,"about_ca_system_score_codex":0.00036170759,"about_ca_system_score_gemma":0.00030388,"threshold_uncertainty_score":0.025867105},"labels":[],"label_agreement":null},{"id":"W3008064482","doi":"10.1115/1.4046402","title":"Modeling Arbitrarily Shaped Liquid Pipelines Using a Segmented Transmission Line Model","year":2020,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Water Systems and 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 Saskatchewan","funders":"","keywords":"Pipeline transport; Transmission line; Line (geometry); Laminar flow; Flow (mathematics); Pipeline (software); Computer science; Flexibility (engineering); Transmission (telecommunications); Mechanics; Algorithm; Control theory (sociology); Geometry; Mathematics; Engineering; Mechanical engineering; Physics","score_opus":0.036161157301243294,"score_gpt":0.20819095141083516,"score_spread":0.17202979410959185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008064482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07415394,0.00009958857,0.9200106,0.00009359523,0.000019184019,0.000044341814,0.0001165586,0.0005655658,0.0048965155],"genre_scores_gemma":[0.8783129,0.00024285987,0.11479275,0.00003604072,0.000014572911,0.00015031068,0.00014567749,0.00010666464,0.0061983354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998765,0.00003501932,0.000004804993,0.000023745295,0.000047549845,0.0000124209855],"domain_scores_gemma":[0.999814,0.00007940474,0.000036983765,0.000020230693,0.000038872997,0.000010666347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017334618,0.00041392373,0.00033235797,0.0003126957,0.00019393134,0.00053463015,0.00090219866,0.0008298355,0.001756769],"category_scores_gemma":[0.00051034405,0.00031538488,0.00051255705,0.00030121682,0.000523463,0.00073010835,0.00035935274,0.00038440383,0.00030534892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001874655,0.000014367804,0.00019455506,0.00001994483,0.0000067884885,0.00005389353,0.00003268798,0.9857119,0.008612367,0.0024215195,0.00010173284,0.0028115255],"study_design_scores_gemma":[0.0000024457854,0.000009889093,0.000029599722,0.0000013204244,0.0000012765056,0.0000062109752,0.0000030952585,0.9990109,0.00056500494,0.00015629308,0.00021200592,0.0000018469489],"about_ca_topic_score_codex":0.0055829845,"about_ca_topic_score_gemma":0.0036254302,"teacher_disagreement_score":0.0055829845,"about_ca_system_score_codex":0.00056727434,"about_ca_system_score_gemma":0.00070069043,"threshold_uncertainty_score":0.011101007},"labels":[],"label_agreement":null},{"id":"W3012276947","doi":"10.1061/(asce)wr.1943-5452.0001222","title":"Effect of Pipe Size and Location on Water-Main Head Loss in Water Distribution Systems","year":2020,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":23,"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; Hydro One (Canada)","funders":"Engineering and Physical Sciences Research Council","keywords":"Hydraulic head; Head (geology); Environmental science; Water pipe; Water level; Friction loss; Water flow; Volumetric flow rate; Flow (mathematics); Pipe network analysis; Mechanics; Geotechnical engineering; Environmental engineering; Geology; Engineering; Mechanical engineering; Inlet","score_opus":0.006365934857180147,"score_gpt":0.20016954699294964,"score_spread":0.1938036121357695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012276947","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":[0.99611557,0.000090109344,0.0030502065,0.000026084437,0.000004684422,0.000010646074,0.00007390749,0.00006295285,0.0005659382],"genre_scores_gemma":[0.99952304,0.000017216636,0.00025960928,0.0000040343475,0.0000015316978,0.000002815322,0.000036733905,0.00000611679,0.00014889453],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991124,0.0003331607,0.000052366187,0.00013302802,0.0002190853,0.00014987712],"domain_scores_gemma":[0.99261427,0.0050142086,0.0011682948,0.0004039668,0.0005666383,0.00023265612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009731621,0.00032900274,0.0003142962,0.0006390614,0.00027598048,0.00049145194,0.0003794654,0.0002809501,0.0013027076],"category_scores_gemma":[0.004768363,0.00023363809,0.00036531495,0.00052822736,0.0005492912,0.0009967007,0.0005793814,0.000296742,0.00017262192],"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":[0.003280016,0.0003504746,0.4977619,0.00033761916,0.00030522243,0.0013626649,0.00046579447,0.3657132,0.058766536,0.00038717236,0.00058256305,0.07068676],"study_design_scores_gemma":[0.00003918713,0.0020795492,0.8960911,0.000019184352,0.00014256839,0.00038950186,0.00060100516,0.078193784,0.021477122,0.00038611007,0.0005332487,0.000047603193],"about_ca_topic_score_codex":0.0033679744,"about_ca_topic_score_gemma":0.0058341143,"teacher_disagreement_score":0.0033679744,"about_ca_system_score_codex":0.00059499237,"about_ca_system_score_gemma":0.00026151325,"threshold_uncertainty_score":0.006696701},"labels":[],"label_agreement":null},{"id":"W3013019105","doi":"10.1016/j.ymssp.2020.106821","title":"Estimating viscoelasticity of pipes with unknown leaks","year":2020,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Water Systems and 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 British Columbia","funders":"Università degli Studi di Perugia; Ministero dell’Istruzione, dell’Università e della Ricerca; Research Grants Council, University Grants Committee","keywords":"Transient (computer programming); Viscoelasticity; Convergence (economics); SIGNAL (programming language); Control theory (sociology); Regular polygon; Steady state (chemistry); Mechanics; Computer science; Mathematics; Applied mathematics; Physics; Thermodynamics; Chemistry; Artificial intelligence","score_opus":0.012896455832583429,"score_gpt":0.18957647891002888,"score_spread":0.17668002307744546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013019105","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":[0.6059759,0.0001636141,0.3930564,0.00010868128,0.000019222556,0.000015049689,0.000065758475,0.00017029211,0.00042512114],"genre_scores_gemma":[0.9865509,0.00011389715,0.012603511,0.000010133937,0.000016856755,0.000008747488,0.0000592617,0.000017403361,0.0006192986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998585,0.000030670642,0.000008606935,0.000047922054,0.00003524901,0.000019069566],"domain_scores_gemma":[0.99897265,0.0006605188,0.00017326434,0.00006960105,0.0000870815,0.0000369506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041224484,0.00063860934,0.00048689265,0.0006678922,0.00016287892,0.00045736387,0.0004252041,0.0010268037,0.00031998727],"category_scores_gemma":[0.0035951037,0.0005401426,0.0003461272,0.00045082517,0.00053884595,0.0016345233,0.00064608967,0.0005911018,0.00011292578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031055833,0.00009568378,0.00937855,0.00010645283,0.00005114204,0.00033858142,0.00016543432,0.87709486,0.065172225,0.0014724034,0.00027129997,0.045542777],"study_design_scores_gemma":[0.000004491021,0.000039103397,0.0020162433,0.0000040730047,0.000006998468,0.00003580942,0.000015635955,0.9942919,0.0029370585,0.00056933815,0.00007198858,0.0000073972924],"about_ca_topic_score_codex":0.0014062277,"about_ca_topic_score_gemma":0.0011248598,"teacher_disagreement_score":0.0014062277,"about_ca_system_score_codex":0.00021308413,"about_ca_system_score_gemma":0.000256881,"threshold_uncertainty_score":0.0027961135},"labels":[],"label_agreement":null},{"id":"W3013879037","doi":"","title":"Regulating the Integrity of Pipeline Systems","year":2000,"lang":"en","type":"article","venue":"16th World Petroleum Congress","topic":"Water Systems and 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":"Canada Energy Regulator","funders":"","keywords":"Pipeline (software); Computer science","score_opus":0.010482205297600465,"score_gpt":0.20708560065119497,"score_spread":0.1966033953535945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013879037","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":[0.11115791,0.006303333,0.3336891,0.058769945,0.0016462174,0.0002573126,0.0006835397,0.00078388583,0.48670873],"genre_scores_gemma":[0.9566476,0.0022573273,0.01164277,0.001988186,0.00053658144,0.000079795005,0.00014725055,0.00014379274,0.026556738],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9944613,0.0012753063,0.00028471948,0.000698237,0.0024225353,0.0008578303],"domain_scores_gemma":[0.99308276,0.0022592347,0.0008374777,0.0014655836,0.002072888,0.0002819739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063196053,0.0005522633,0.0006281907,0.0013285183,0.0018113729,0.007222591,0.0017499719,0.004273243,0.0068976297],"category_scores_gemma":[0.017616102,0.00051931775,0.00067553535,0.00081209926,0.0051185833,0.0054543763,0.0038250159,0.0047073783,0.0011693838],"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":[0.000074262854,0.000030758638,0.0009818926,0.00009266742,0.000039548053,0.00008154554,0.00048530597,0.02484365,0.0047901957,0.9234314,0.008732319,0.036416456],"study_design_scores_gemma":[0.000051796105,0.00010099683,0.001621117,0.00024303693,0.000081607715,0.00008376014,0.00029470737,0.04276726,0.012540488,0.82574743,0.11639621,0.000071672825],"about_ca_topic_score_codex":0.006509041,"about_ca_topic_score_gemma":0.0058655026,"teacher_disagreement_score":0.007222591,"about_ca_system_score_codex":0.004190728,"about_ca_system_score_gemma":0.007939113,"threshold_uncertainty_score":0.033421695},"labels":[],"label_agreement":null},{"id":"W3014150406","doi":"10.2166/aqua.2020.127","title":"Evolution of the relationship between total suspended solids concentration and turbidity during flushing sequences of water pipes","year":2020,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique","funders":"Mitacs","keywords":"Turbidity; Flushing; Suspended solids; Environmental science; Particle (ecology); Total suspended solids; Particle counter; Filtration (mathematics); Sampling (signal processing); Environmental engineering; Particulates; Hydrology (agriculture); Mathematics; Chemistry; Ecology; Geotechnical engineering; Geology; Aerosol; Biology; Filter (signal processing); Engineering; Wastewater; Chemical oxygen demand","score_opus":0.03769711349528456,"score_gpt":0.2589870274482027,"score_spread":0.2212899139529181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014150406","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":[0.9992035,0.00003332589,0.0005371165,0.000004340204,0.0000018036347,0.0000034353657,0.000055060813,0.000013253809,0.00014819496],"genre_scores_gemma":[0.99924386,0.000018808045,0.00043779882,0.000002696196,8.6009334e-7,0.0000047101794,0.00008209448,0.0000028973554,0.00020642999],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99977666,0.000035060595,0.000015609825,0.000058017446,0.000080616344,0.00003410784],"domain_scores_gemma":[0.99915564,0.000302952,0.00017871849,0.00003304652,0.0002636137,0.00006606471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031387818,0.00023165526,0.00029578843,0.00048537436,0.00022232583,0.00035155404,0.00014589177,0.0003265469,0.00051611924],"category_scores_gemma":[0.00087992265,0.00017504151,0.00021343897,0.00034577397,0.00021509867,0.00025478794,0.00016173908,0.00030654247,0.00014327325],"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":[0.0012785046,0.00020759639,0.14246695,0.0000938529,0.00008215997,0.00023734337,0.00036715803,0.005061019,0.83385557,0.00006782106,0.00011204977,0.016169937],"study_design_scores_gemma":[0.00001212409,0.0015982402,0.5090875,0.000009232921,0.000053854714,0.00017188863,0.00042880804,0.029453395,0.45866224,0.000058658108,0.00042392104,0.000040215633],"about_ca_topic_score_codex":0.0028973746,"about_ca_topic_score_gemma":0.0021428056,"teacher_disagreement_score":0.0028973746,"about_ca_system_score_codex":0.00024027629,"about_ca_system_score_gemma":0.00015192304,"threshold_uncertainty_score":0.0057610273},"labels":[],"label_agreement":null},{"id":"W3015029302","doi":"10.3968/11206","title":"Development of a New Model for Leak Detection in Pipelines","year":2019,"lang":"en","type":"article","venue":"Advances in petroleum exploration and development","topic":"Water Systems and 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":"Pipeline transport; Pipeline (software); Leak; Damages; Niger delta; Leak detection; ALARM; Engineering; Petroleum engineering; Product (mathematics); Environmental engineering; Delta; Mechanical engineering; Electrical engineering","score_opus":0.020927311403221106,"score_gpt":0.23473743607212197,"score_spread":0.21381012466890087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015029302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01235602,0.00039016575,0.97605103,0.00042852672,0.000094936404,0.00006434927,0.00027638595,0.00029359246,0.010045052],"genre_scores_gemma":[0.839933,0.0012548072,0.12059001,0.0002731166,0.000111270674,0.00061668013,0.0006814082,0.00022536388,0.03631446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959534,0.000088453045,0.000026513802,0.00011480652,0.00011131186,0.000063558735],"domain_scores_gemma":[0.99912685,0.0004439504,0.000097833865,0.000030698393,0.0002723689,0.000028326655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007205774,0.0008917604,0.0010571867,0.00079316506,0.0005667571,0.0015266496,0.0015546608,0.0020400884,0.003919017],"category_scores_gemma":[0.0022986226,0.00061192125,0.0014744491,0.00058834295,0.0007329653,0.0015940298,0.0010935398,0.0019485414,0.0006123564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014764813,0.000017776167,0.0003942401,0.000060793067,0.000012340371,0.00007979408,0.000034229903,0.9856527,0.001360661,0.007857527,0.00041221967,0.0041029],"study_design_scores_gemma":[0.0000022749543,0.0000070247565,0.00004457695,0.0000042410115,0.000003202459,0.000010149382,0.0000050558397,0.99818474,0.0001915501,0.0010613922,0.00048190352,0.0000038933335],"about_ca_topic_score_codex":0.015586779,"about_ca_topic_score_gemma":0.006761705,"teacher_disagreement_score":0.015586779,"about_ca_system_score_codex":0.001192655,"about_ca_system_score_gemma":0.0018690892,"threshold_uncertainty_score":0.03099215},"labels":[],"label_agreement":null},{"id":"W3015068370","doi":"10.3390/w12041037","title":"A Direct Approach for the Near-Optimal Design of Water Distribution Networks Based on Power Use","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Computer science; Optimal design; Engineering design process; Power flow; Process (computing); Hydraulic machinery; Power (physics); Metaheuristic; Distribution (mathematics); Electric power system; Mathematics; Engineering; Mechanical engineering","score_opus":0.02045200949830347,"score_gpt":0.1765153250283703,"score_spread":0.15606331553006683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015068370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037501322,0.00021139857,0.9902395,0.00010023343,0.000020843625,0.000046663,0.000024946601,0.000060256283,0.005545968],"genre_scores_gemma":[0.4614452,0.0014724308,0.5271037,0.00016791,0.00008386973,0.0006658621,0.0001258142,0.00012417776,0.008811075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970406,0.00009786975,0.000010023219,0.00004892151,0.00011800405,0.000021114067],"domain_scores_gemma":[0.9997693,0.00013398843,0.000029388531,0.000017384767,0.00004013543,0.000009702407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051418267,0.00090169796,0.0006600894,0.00069574977,0.00032522337,0.0009659588,0.00069413445,0.00081379956,0.0037765384],"category_scores_gemma":[0.0012534466,0.0005086256,0.00079969334,0.000727601,0.00085609936,0.0007057489,0.001008246,0.00085399894,0.00036485694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016458309,0.000028059445,0.00018616645,0.0001581091,0.000028606211,0.000043898868,0.000057061083,0.91687375,0.002791207,0.034778327,0.0006731785,0.044365227],"study_design_scores_gemma":[0.000010354584,0.00007297372,0.00008256225,0.000020132067,0.000013265849,0.000030101895,0.000017729843,0.9749073,0.0010242915,0.018478617,0.005335997,0.000006761319],"about_ca_topic_score_codex":0.0016031761,"about_ca_topic_score_gemma":0.0018007795,"teacher_disagreement_score":0.0037765384,"about_ca_system_score_codex":0.00052192947,"about_ca_system_score_gemma":0.0010880657,"threshold_uncertainty_score":0.0126338005},"labels":[],"label_agreement":null},{"id":"W3018786341","doi":"10.1117/12.2558886","title":"Water pipe valve detection by using deep neural networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"BI Pure Water (Canada); Okanagan University College; University of British Columbia","funders":"","keywords":"Pipeline transport; Computer science; Robot; Artificial neural network; Process (computing); Artificial intelligence; Real-time computing; Pipe network analysis; Set (abstract data type); Water pipe; Marine engineering; Engineering; Mechanical engineering","score_opus":0.0095425103758106,"score_gpt":0.17154073969400377,"score_spread":0.16199822931819316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018786341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22024697,0.0014677676,0.76863885,0.00031915188,0.00013073838,0.0000627487,0.0003758447,0.0041387537,0.004619124],"genre_scores_gemma":[0.9391416,0.00033769276,0.056091826,0.000081440085,0.000030817744,0.000029671302,0.00042742668,0.000059933383,0.0037996527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998424,0.000022331207,0.000009380532,0.000046293226,0.00004529245,0.000034228433],"domain_scores_gemma":[0.9997216,0.00010078429,0.00004873164,0.000021030333,0.0000900445,0.000017749118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033440738,0.00078391744,0.0004360876,0.0005515965,0.00014004571,0.00052877795,0.00065901614,0.00053773774,0.0012595046],"category_scores_gemma":[0.0009457222,0.00035573286,0.00042087503,0.00034064444,0.00021936571,0.0007405247,0.00044156265,0.00055743626,0.00031321993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030797638,0.0001372758,0.0031197397,0.00013343348,0.000082078295,0.00012151609,0.00003112386,0.62820333,0.028973205,0.0010450033,0.0025279233,0.33531746],"study_design_scores_gemma":[0.0000020335272,0.000014437818,0.00029726973,0.0000030600486,0.000004142224,0.0000051371535,0.000002512676,0.99716115,0.002198651,0.00017387926,0.0001350035,0.000002780897],"about_ca_topic_score_codex":0.012698067,"about_ca_topic_score_gemma":0.014039416,"teacher_disagreement_score":0.012698067,"about_ca_system_score_codex":0.0007224108,"about_ca_system_score_gemma":0.0006271444,"threshold_uncertainty_score":0.02524829},"labels":[],"label_agreement":null},{"id":"W3018878657","doi":"","title":"An orifice flow model for laminar and turbulent conditions","year":2001,"lang":"en","type":"article","venue":"Publication Server of Bonn-Rhein-Sieg University of Applied Sciences (Bonn-Rhein-Sieg University of Applied Sciences)","topic":"Water Systems and 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":"Pressure drop; Turbulence; Flow coefficient; Laminar flow; Mathematics; Reynolds number; Mechanics; Body orifice; Pipe flow; Root mean square; Flow (mathematics); Physics; Geometry; Engineering; Mechanical engineering","score_opus":0.019515858878618116,"score_gpt":0.2014763041938278,"score_spread":0.1819604453152097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018878657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07078489,0.000358012,0.91612595,0.00023652523,0.000075505995,0.00012522038,0.00035730767,0.00083945977,0.011097139],"genre_scores_gemma":[0.91652673,0.0005251995,0.060323346,0.000055890483,0.000045170425,0.00034205188,0.00040033282,0.00019379455,0.021587472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997391,0.000048831673,0.000012347924,0.00006271857,0.00010520357,0.000031679763],"domain_scores_gemma":[0.9996656,0.00014037211,0.000052334555,0.000025792258,0.00009697909,0.000018879038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049666024,0.0006265694,0.00078662817,0.0009131516,0.00043456466,0.0013906307,0.0014740889,0.0015259971,0.0028844445],"category_scores_gemma":[0.0012543466,0.00038049105,0.0006782319,0.0007292305,0.0007313643,0.0013232523,0.00051015336,0.00082195044,0.0007207401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002646985,0.000022323735,0.00023315926,0.000020669328,0.000005293026,0.000045659577,0.000033182678,0.9765033,0.0019228488,0.016237743,0.0002811708,0.004668082],"study_design_scores_gemma":[0.0000034809652,0.0000059103536,0.00003122605,0.0000011594573,0.0000015618148,0.0000044662524,0.0000014418345,0.9985098,0.00015328574,0.0009421141,0.00034282389,0.0000026474463],"about_ca_topic_score_codex":0.0074162567,"about_ca_topic_score_gemma":0.002433346,"teacher_disagreement_score":0.0074162567,"about_ca_system_score_codex":0.00096042606,"about_ca_system_score_gemma":0.0013635312,"threshold_uncertainty_score":0.014746189},"labels":[],"label_agreement":null},{"id":"W3020932405","doi":"10.1061/(asce)hy.1943-7900.0001773","title":"Expulsion of Entrapped Air in a Rapidly Filling Horizontal Pipe","year":2020,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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 Toronto","funders":"Fundamental Research Funds for the Central Universities; Javna Agencija za Raziskovalno Dejavnost RS; Fok Ying Tong Education Foundation; National Natural Science Foundation of China","keywords":"Body orifice; Water hammer; Mechanics; Volume (thermodynamics); Secondary air injection; Airflow; Outflow; Inlet; Oscillation (cell signaling); Materials science; Geology; Meteorology; Thermodynamics; Chemistry; Physics; Engineering","score_opus":0.006533078731049024,"score_gpt":0.17105311303328663,"score_spread":0.1645200343022376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020932405","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":[0.9960573,0.000074985895,0.0034917959,0.000029552315,0.000008127933,0.000010425302,0.000026978041,0.00010912341,0.00019188391],"genre_scores_gemma":[0.9985398,0.000035379213,0.001079619,0.00000809698,0.0000030560145,0.000007816283,0.00002167966,0.0000067547267,0.00029773125],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985945,0.000014529334,0.0000061138794,0.0000308969,0.000046543737,0.000042526124],"domain_scores_gemma":[0.99973696,0.00010757181,0.000064018306,0.000022606107,0.000031826956,0.000037048765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023207709,0.00022256633,0.00038575975,0.0001409465,0.00024796894,0.00027852497,0.0004148394,0.00035674116,0.0005791056],"category_scores_gemma":[0.00048243243,0.00017994906,0.00020778301,0.00010359123,0.0005185187,0.00038877927,0.0004358187,0.00032618473,0.00013862397],"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":[0.00013744239,0.00003585344,0.0022105565,0.00009279883,0.00000994244,0.00063045905,0.00018732868,0.004739249,0.98726976,0.00021348077,0.000089440924,0.0043837954],"study_design_scores_gemma":[0.000056005436,0.001558471,0.016489632,0.000021302665,0.000021885806,0.00028522717,0.00025357914,0.20731784,0.77235204,0.00026016188,0.0013415321,0.000042298954],"about_ca_topic_score_codex":0.00070640695,"about_ca_topic_score_gemma":0.0005051782,"teacher_disagreement_score":0.00070640695,"about_ca_system_score_codex":0.00030012697,"about_ca_system_score_gemma":0.00022879837,"threshold_uncertainty_score":0.0021775365},"labels":[],"label_agreement":null},{"id":"W3022226787","doi":"10.1061/(asce)wr.1943-5452.0001239","title":"Battle of Postdisaster Response and Restoration","year":2020,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":true,"ca_institutions":"Queen's University","funders":"","keywords":"Firefighting; Damages; Schedule; Resilience (materials science); Computer science; Operations research; Service (business); Battle; Duration (music); Engineering; Operations management; Business","score_opus":0.011779066282606379,"score_gpt":0.19250418462798852,"score_spread":0.18072511834538213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022226787","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":[0.4557184,0.008910944,0.32982942,0.047719106,0.010823496,0.0027704474,0.0029425435,0.0032925347,0.13799319],"genre_scores_gemma":[0.8582034,0.0011272491,0.11080638,0.0018055071,0.00076264,0.0005040988,0.0015731896,0.00044387902,0.02477354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9861801,0.007276692,0.000342808,0.0010972384,0.0033023444,0.0018008387],"domain_scores_gemma":[0.9726093,0.01722956,0.0011518257,0.0014748044,0.004748041,0.0027865462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017744659,0.0016482885,0.0014918064,0.001110905,0.0026655684,0.0036850418,0.0021128573,0.003154291,0.01027262],"category_scores_gemma":[0.025877703,0.00042531762,0.0010529803,0.0009897607,0.002209506,0.0035775735,0.003640507,0.0036585843,0.0013924092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040503903,0.0016153555,0.007115201,0.0017434804,0.00033844647,0.0014627618,0.0025134457,0.43809828,0.0093165655,0.059888266,0.11605824,0.35779956],"study_design_scores_gemma":[0.00062753307,0.0036316162,0.006987344,0.00047284347,0.00015068745,0.0009120253,0.011334664,0.70118636,0.015898459,0.062847964,0.19563498,0.00031556824],"about_ca_topic_score_codex":0.009078464,"about_ca_topic_score_gemma":0.009728889,"teacher_disagreement_score":0.017744659,"about_ca_system_score_codex":0.0038504659,"about_ca_system_score_gemma":0.0040202932,"threshold_uncertainty_score":0.09384388},"labels":[],"label_agreement":null},{"id":"W3023969749","doi":"10.1061/(asce)ps.1949-1204.0000470","title":"Level of Service-Based Asset Management Framework for Water Supply Systems","year":2020,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Asset management; Water supply; Mains electricity; Asset (computer security); Water supply network; Service (business); Network information system; Service quality; IT service continuity; Electricity; Environmental economics; Computer science; Business; Engineering; Environmental engineering; Computer security; Marketing; Finance","score_opus":0.05218598687273881,"score_gpt":0.2536077313227446,"score_spread":0.2014217444500058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023969749","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":[0.008257553,0.0003051164,0.9831323,0.00034786153,0.000032491727,0.00018068294,0.00022512919,0.0004357431,0.0070831366],"genre_scores_gemma":[0.4438562,0.0006624841,0.5499407,0.000107091575,0.00006039144,0.00076264766,0.00077763427,0.000077284116,0.0037555108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984499,0.00065560755,0.00012937249,0.00021401721,0.00040262437,0.00014835232],"domain_scores_gemma":[0.999161,0.00037978753,0.0001012603,0.000044995864,0.00025658283,0.00005636092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020015265,0.0010237327,0.0005464408,0.0019907544,0.000626076,0.0027392206,0.0017021989,0.0009918805,0.0032569172],"category_scores_gemma":[0.0029384606,0.00033810656,0.0011743333,0.0015817469,0.00083039195,0.0020451564,0.0014970446,0.0012340271,0.0005940987],"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":[0.00003375137,0.00010869141,0.0028546897,0.00019504038,0.00006928965,0.00027112555,0.0005685588,0.6925452,0.0010710732,0.24439727,0.0025505316,0.05533479],"study_design_scores_gemma":[0.0000057096604,0.000022849883,0.0002959651,0.000027721151,0.000010029361,0.000033275188,0.000110775,0.96801937,0.00014725098,0.02685427,0.0044642603,0.0000085442825],"about_ca_topic_score_codex":0.02332813,"about_ca_topic_score_gemma":0.01831523,"teacher_disagreement_score":0.02332813,"about_ca_system_score_codex":0.0027692267,"about_ca_system_score_gemma":0.0022346803,"threshold_uncertainty_score":0.046384692},"labels":[],"label_agreement":null},{"id":"W3024298046","doi":"10.1016/j.aei.2020.101103","title":"A field implementation of linear prediction for leak-monitoring in water distribution networks","year":2020,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":36,"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":"Leak; Leak detection; Field (mathematics); Software deployment; Data acquisition; Computer science; Real-time computing; Scale (ratio); Software; Transmission (telecommunications); Engineering","score_opus":0.006104989818183875,"score_gpt":0.21049930435399367,"score_spread":0.20439431453580978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024298046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27583855,0.00007651617,0.6962873,0.0005785079,0.00018146534,0.00044074954,0.00045873027,0.017145924,0.00899222],"genre_scores_gemma":[0.88728416,0.000019677924,0.109825864,0.00008700543,0.000017062433,0.000099490186,0.00018047227,0.000083742874,0.00240255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989748,0.00037876773,0.000037378362,0.0002703634,0.00022675567,0.00011200005],"domain_scores_gemma":[0.99803656,0.0007209392,0.000077902805,0.0003171363,0.0007335946,0.00011378362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016149753,0.0006268343,0.0005956948,0.00034754636,0.0005999634,0.0006896657,0.0018241379,0.00095263135,0.00527533],"category_scores_gemma":[0.0027534633,0.00031349118,0.00028765295,0.00043072028,0.0004816038,0.0009200326,0.00067867315,0.0010134025,0.0008623501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001936097,0.0031484258,0.01061219,0.00017828576,0.00010390186,0.00020093247,0.00022478339,0.5757373,0.03407574,0.004580618,0.008285601,0.36091617],"study_design_scores_gemma":[0.00006701811,0.00018501718,0.00050441566,0.0000031785062,0.0000074593495,0.000007182109,0.000021557786,0.99308175,0.0052533685,0.00036782515,0.00049133284,0.000009893999],"about_ca_topic_score_codex":0.025458965,"about_ca_topic_score_gemma":0.015542729,"teacher_disagreement_score":0.025458965,"about_ca_system_score_codex":0.000811717,"about_ca_system_score_gemma":0.0015192261,"threshold_uncertainty_score":0.05062157},"labels":[],"label_agreement":null},{"id":"W3031520772","doi":"10.1002/aws2.1179","title":"Validation of water main failure predictions: A <scp>2‐year</scp> case study","year":2020,"lang":"en","type":"article","venue":"AWWA Water Science","topic":"Water Systems and 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":"American Water (Canada)","funders":"","keywords":"Mains electricity; Robustness (evolution); Damages; Water infrastructure; Environmental science; Computer science; Electricity; Water supply; Reliability engineering; Environmental economics; Water resource management; Civil engineering; Environmental engineering; Engineering; Economics","score_opus":0.012991814395080946,"score_gpt":0.20166431266540635,"score_spread":0.1886724982703254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031520772","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":[0.99066216,0.000036723108,0.003658301,0.000232118,0.000024263236,0.00011368637,0.0022329523,0.00025634526,0.0027835278],"genre_scores_gemma":[0.9951173,0.000016255719,0.0025875873,0.000025182706,0.0000041833027,0.0000628028,0.0014706296,0.00001600962,0.0007000428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986098,0.0005351274,0.000060648614,0.00028460438,0.00030041815,0.00020939791],"domain_scores_gemma":[0.99037313,0.0061672432,0.0006400054,0.00097004976,0.001495656,0.0003539767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041319174,0.001081111,0.00071128114,0.0012157104,0.00078510353,0.0009771883,0.002030757,0.002481475,0.0025651348],"category_scores_gemma":[0.0074882135,0.0005355212,0.0011456945,0.00094748405,0.000900092,0.00096988777,0.0007451618,0.001615662,0.00039673154],"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":[0.00021711082,0.0006489723,0.026078917,0.000049584276,0.00007356551,0.00056635094,0.000066019464,0.96451026,0.0010864334,0.0005412793,0.0021369644,0.0040245317],"study_design_scores_gemma":[0.0000826405,0.00031218134,0.011741519,0.00001091108,0.000014185338,0.000053652362,0.00013071642,0.9847142,0.0019894768,0.0003208687,0.000605565,0.000024029237],"about_ca_topic_score_codex":0.097808346,"about_ca_topic_score_gemma":0.07179737,"teacher_disagreement_score":0.097808346,"about_ca_system_score_codex":0.0035931382,"about_ca_system_score_gemma":0.0020169348,"threshold_uncertainty_score":0.1944781},"labels":[],"label_agreement":null},{"id":"W3035135995","doi":"10.1002/eng2.12179","title":"Optimized planning of repair works for pipelines in water distribution networks using genetic algorithm","year":2020,"lang":"en","type":"article","venue":"Engineering Reports","topic":"Water Systems and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University; University of Ottawa","funders":"Qatar National Research Fund","keywords":"Time horizon; Pipeline transport; Pipeline (software); Reliability engineering; Scheduling (production processes); Total cost; Computer science; Water supply; Risk analysis (engineering); Preventive maintenance; Genetic algorithm; Operations research; Engineering; Mathematical optimization; Operations management; Business","score_opus":0.011000075204463385,"score_gpt":0.19898486006275184,"score_spread":0.18798478485828846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035135995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32874954,0.0007756153,0.65842295,0.00052376156,0.0000733045,0.00025406465,0.00039932298,0.0004249002,0.010376589],"genre_scores_gemma":[0.9280464,0.00022959552,0.06747621,0.000057212033,0.000011727089,0.00023097957,0.0002093294,0.00004442181,0.0036940628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997476,0.00010205775,0.000008669399,0.00004518397,0.00003434869,0.000062160674],"domain_scores_gemma":[0.9991578,0.0005974137,0.00008828195,0.000018432565,0.00009279784,0.000045287474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007738682,0.0009004505,0.0010989399,0.0010856439,0.00046218376,0.0010173091,0.00083977997,0.0013841775,0.0024496664],"category_scores_gemma":[0.001670817,0.00072744535,0.0007775075,0.0008876209,0.00063536444,0.00058881385,0.0004957091,0.0007507438,0.00015137326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006196565,0.000005456796,0.0000841689,0.000004244673,0.0000028734898,0.0000052886357,0.0000027130363,0.9988237,0.000044246124,0.00016250988,0.000029452664,0.00082914304],"study_design_scores_gemma":[0.0000031169855,0.000008972677,0.000043439857,0.000001700058,0.0000024734352,0.0000010140604,0.00000398494,0.9996673,0.00004052555,0.0001816082,0.000044727836,0.0000011022323],"about_ca_topic_score_codex":0.030861536,"about_ca_topic_score_gemma":0.018297931,"teacher_disagreement_score":0.030861536,"about_ca_system_score_codex":0.0022508148,"about_ca_system_score_gemma":0.0018787384,"threshold_uncertainty_score":0.061363757},"labels":[],"label_agreement":null},{"id":"W3035491673","doi":"10.1109/access.2020.3000960","title":"A Novel PPA Method for Fluid Pipeline Leak Detection Based on OPELM and Bidirectional LSTM","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Water Systems and Optimization","field":"Engineering","cited_by":36,"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":"Computer science; Leak; Pipeline (software); Leak detection; Exploit; Constant false alarm rate; False alarm; False positive paradox; Pipeline transport; Feature extraction; Artificial intelligence; Data mining; Pattern recognition (psychology); Real-time computing; Engineering; Computer security","score_opus":0.03822156162543188,"score_gpt":0.2767265161450011,"score_spread":0.2385049545195692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035491673","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":[0.009930761,0.00045672807,0.9857771,0.0001533834,0.00012677727,0.00003621901,0.00006360303,0.0019962876,0.0014591455],"genre_scores_gemma":[0.47988182,0.0005908601,0.5107053,0.0004376293,0.00013790144,0.00022106028,0.0003272432,0.00026526907,0.0074328827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997569,0.000031900858,0.000017544491,0.00008212065,0.00007680255,0.000034887198],"domain_scores_gemma":[0.9997569,0.00006909233,0.00003222664,0.000025700789,0.000099655445,0.000016540418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003967474,0.00090239156,0.0006200203,0.0006401019,0.0003712286,0.00064393866,0.0012427671,0.0009758538,0.0028882788],"category_scores_gemma":[0.0010150606,0.00040652775,0.000643785,0.000526154,0.00039979635,0.001364397,0.0008836817,0.0013762487,0.00095394655],"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":[0.00017424749,0.00011961553,0.001136979,0.00024275278,0.00010188601,0.00021800159,0.000095287636,0.11950933,0.054366317,0.004524557,0.0044298116,0.81508124],"study_design_scores_gemma":[0.000006588637,0.000044112796,0.00030662373,0.000010634793,0.000014933083,0.00007059559,0.000011219723,0.98914963,0.007166585,0.0017012588,0.0015049746,0.000012841119],"about_ca_topic_score_codex":0.0027981848,"about_ca_topic_score_gemma":0.0037038322,"teacher_disagreement_score":0.0028882788,"about_ca_system_score_codex":0.00040530207,"about_ca_system_score_gemma":0.00084406714,"threshold_uncertainty_score":0.009662271},"labels":[],"label_agreement":null},{"id":"W3036218685","doi":"","title":"Integrated Asset Management Framework and Model for Water Distribution Networks","year":2020,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Water Systems and 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":"Asset management; Asset (computer security); Distribution (mathematics); Computer science; Business; Mathematics; Finance; Computer security","score_opus":0.007011083497985452,"score_gpt":0.17215645247277667,"score_spread":0.1651453689747912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036218685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009307286,0.0010317381,0.92465776,0.001521253,0.00015610123,0.00020338189,0.0014334708,0.000491382,0.061197575],"genre_scores_gemma":[0.59716946,0.0032485577,0.31538406,0.00040303875,0.00028935622,0.0015199214,0.0027073098,0.0003398513,0.078938395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992824,0.00022013634,0.000034368546,0.00012393313,0.00018675282,0.00015233777],"domain_scores_gemma":[0.99945766,0.00022215165,0.000064021726,0.000028317041,0.0001764653,0.000051423674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015047953,0.0011226519,0.00083463406,0.0013158927,0.0009202518,0.003409255,0.0031648853,0.0025449488,0.015742792],"category_scores_gemma":[0.0028643135,0.00063581904,0.0012561806,0.0018451342,0.00086799386,0.00320352,0.0020366644,0.0023055288,0.0023888713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016242262,0.00003235481,0.00043888149,0.000033372122,0.00001763471,0.00011189562,0.00008010092,0.7275822,0.000108630884,0.25742608,0.0038762619,0.010276305],"study_design_scores_gemma":[0.000010651349,0.000009456031,0.0000810028,0.00001680931,0.0000073504575,0.000030155934,0.00003247377,0.9367948,0.00003681073,0.055349376,0.007623848,0.000007319957],"about_ca_topic_score_codex":0.048265785,"about_ca_topic_score_gemma":0.02746112,"teacher_disagreement_score":0.048265785,"about_ca_system_score_codex":0.0031818512,"about_ca_system_score_gemma":0.0032110615,"threshold_uncertainty_score":0.09596968},"labels":[],"label_agreement":null},{"id":"W3037349638","doi":"10.1061/(asce)ps.1949-1204.0000482","title":"Closure to “New Approach for the Computation of the Water Surface Angle in Partially Filled Pipes: Pipes Arranged in Parallel” by Lotfi Zeghadnia and Jean Loup Robert","year":2020,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and 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 Laval","funders":"","keywords":"Closure (psychology); Computation; Surface (topology); Geology; Computer science; Materials science; Mathematics; Geometry; Algorithm; Law; Political science","score_opus":0.014832014096570428,"score_gpt":0.219344211918947,"score_spread":0.2045121978223766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037349638","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067590917,0.00068484613,0.985927,0.0003571233,0.0006428277,0.000054687433,0.00010450152,0.00022386269,0.0052461275],"genre_scores_gemma":[0.2832856,0.0018892123,0.68382525,0.00043997314,0.001446389,0.00046068704,0.0005086324,0.0014102436,0.026734043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993486,0.00022154815,0.000033779463,0.00013886041,0.0002006019,0.00005651323],"domain_scores_gemma":[0.9990754,0.00048630315,0.00006380722,0.00009359611,0.00021108448,0.00006982522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010528498,0.0010628155,0.0016389654,0.00087630353,0.0007701016,0.0015935738,0.0021921562,0.0017294432,0.006198948],"category_scores_gemma":[0.0047625396,0.001018794,0.0019011362,0.0009041987,0.0014425267,0.002349346,0.002547565,0.0027522983,0.0013262666],"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":[0.0002614554,0.00013156135,0.00086633343,0.0004314952,0.00011814673,0.00040370383,0.00025226627,0.6955313,0.0067897784,0.18371843,0.015249515,0.09624599],"study_design_scores_gemma":[0.000012372177,0.0000194185,0.00006709117,0.000017118422,0.0000070228743,0.000024765044,0.0000129846885,0.97782475,0.0005499542,0.015754072,0.005698199,0.000012273788],"about_ca_topic_score_codex":0.0046147513,"about_ca_topic_score_gemma":0.0028247477,"teacher_disagreement_score":0.006198948,"about_ca_system_score_codex":0.0006760455,"about_ca_system_score_gemma":0.0009769878,"threshold_uncertainty_score":0.020737529},"labels":[],"label_agreement":null},{"id":"W3041544221","doi":"10.1016/j.petlm.2020.07.001","title":"Numerical modeling of the critical pipeline inclination for the elimination of the water accumulation on the pipe floor in oil-water fluid flow","year":2020,"lang":"en","type":"article","venue":"Petroleum","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Inclination angle; Pipeline transport; Water flow; Computational fluid dynamics; Flow (mathematics); Viscosity; Geotechnical engineering; Mechanics; Fluid dynamics; Petroleum engineering; Plug flow; Pipe flow; Flow velocity; Work (physics); Geology; Materials science; Environmental science; Engineering; Structural engineering; Mechanical engineering; Turbulence; Environmental engineering; Composite material; Physics","score_opus":0.031075366390435705,"score_gpt":0.24342073680424647,"score_spread":0.21234537041381077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041544221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34204957,0.00040101388,0.6451079,0.00016298184,0.000059113176,0.00010772062,0.00010292211,0.00028005906,0.011728796],"genre_scores_gemma":[0.96831995,0.00018911,0.030017573,0.00000948034,0.0000049489745,0.000061068655,0.00004148799,0.000020795782,0.0013355544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999879,0.000025293339,0.0000066625375,0.000017045411,0.000043830943,0.000028051027],"domain_scores_gemma":[0.9997899,0.00008266996,0.000050665618,0.00001565592,0.000047681177,0.000013481428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031527097,0.00036647916,0.00040344696,0.0003752062,0.0004372623,0.0004923177,0.00036888773,0.00053841446,0.0007425316],"category_scores_gemma":[0.0008405806,0.0002661978,0.0004357527,0.0002873791,0.0005566993,0.00039418516,0.00033034667,0.00029473787,0.00011063882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018344728,0.000014147697,0.0009125533,0.000026812071,0.000002519795,0.000044701002,0.00003213971,0.9869027,0.0070669074,0.0022411908,0.000064346605,0.0026736823],"study_design_scores_gemma":[0.0000021688509,0.000010220808,0.00015191674,0.0000032118319,0.0000018270838,0.000006747963,0.000008569273,0.99811184,0.001277776,0.00024802645,0.00017517873,0.0000025137167],"about_ca_topic_score_codex":0.006210111,"about_ca_topic_score_gemma":0.0037526584,"teacher_disagreement_score":0.006210111,"about_ca_system_score_codex":0.0005234426,"about_ca_system_score_gemma":0.0013682703,"threshold_uncertainty_score":0.012347937},"labels":[],"label_agreement":null},{"id":"W3042468863","doi":"10.1061/(asce)ps.1949-1204.0000485","title":"Intelligent Approaches for Predicting Failure of Water Mains","year":2020,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and Optimization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Mains electricity; Decision tree; Robustness (evolution); Artificial neural network; Pipeline transport; Reliability engineering; Computer science; Engineering; Data mining; Machine learning; Environmental engineering","score_opus":0.03235580842469791,"score_gpt":0.21703399653254546,"score_spread":0.18467818810784756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042468863","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":[0.501902,0.0008329897,0.4930553,0.0003979038,0.00004743576,0.00006214487,0.00030316715,0.00055882405,0.0028402156],"genre_scores_gemma":[0.9855539,0.00015606734,0.013442804,0.000026696716,0.000013757765,0.000024766457,0.000121748046,0.000010014038,0.00065018603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972457,0.000081208906,0.000019054796,0.000072835675,0.00006073757,0.000041513544],"domain_scores_gemma":[0.9992105,0.000425193,0.00015683292,0.000036135167,0.00014343005,0.000027917233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010018331,0.0008116637,0.00057906203,0.0009153181,0.00019913755,0.0005748554,0.00057621783,0.0007710867,0.00060353905],"category_scores_gemma":[0.0024244343,0.00033377385,0.000546113,0.00049094745,0.00028439454,0.0006629302,0.0003554251,0.0005260422,0.00011680512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013346512,0.00001477685,0.002015368,0.000009086794,0.000019787267,0.000017258613,0.000006770711,0.99132806,0.00045649137,0.00021036807,0.000083121166,0.0058256104],"study_design_scores_gemma":[6.326254e-7,0.000007795431,0.0004925476,0.0000013027025,0.000002325237,0.000002531134,0.000001980473,0.9991322,0.000105551815,0.0002256144,0.000025688332,0.0000018010086],"about_ca_topic_score_codex":0.010391325,"about_ca_topic_score_gemma":0.00857019,"teacher_disagreement_score":0.010391325,"about_ca_system_score_codex":0.0006962031,"about_ca_system_score_gemma":0.00056124147,"threshold_uncertainty_score":0.020661652},"labels":[],"label_agreement":null},{"id":"W3045154667","doi":"10.2166/wp.2020.029","title":"Evaluating the use and intent of drinking water advisories in Atlantic Canada","year":2020,"lang":"en","type":"article","venue":"Water Policy","topic":"Water Systems and 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":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Nova scotia; Duration (music); Business; Water safety; Process (computing); Environmental planning; Environmental resource management; Risk analysis (engineering); Environmental science; Geography; Computer science; Water quality","score_opus":0.04843788308215535,"score_gpt":0.24060769033844956,"score_spread":0.1921698072562942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045154667","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":[0.98623055,0.00028242572,0.0005339204,0.00080297154,0.000018141895,0.00015708386,0.0033088308,0.00002446694,0.008641687],"genre_scores_gemma":[0.9953778,0.00026402526,0.0009150729,0.000094079536,0.000002791789,0.00003216305,0.0010065208,0.000006579016,0.0023010306],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962559,0.00046123101,0.00028270629,0.00024659024,0.0021036665,0.0006499694],"domain_scores_gemma":[0.9711918,0.0034490314,0.0036072517,0.00045290272,0.01853619,0.0027628536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032649564,0.00022429466,0.00026138363,0.0020433238,0.002009729,0.002420095,0.0012756791,0.00036539993,0.001999906],"category_scores_gemma":[0.015894387,0.00018875263,0.00034259786,0.0039496385,0.0009162273,0.0006282266,0.001159872,0.00064570556,0.00020249673],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010164631,0.000071938266,0.9739536,0.00008017812,0.000045691482,0.000093368275,0.0025025697,0.0008613646,0.00025068416,0.00039719453,0.0018527991,0.019789038],"study_design_scores_gemma":[0.0000057291954,0.000066833025,0.9764341,0.00010293928,0.00003322707,0.000027020165,0.0135433655,0.0031939521,0.0005816405,0.00009809972,0.0058839656,0.000028980388],"about_ca_topic_score_codex":0.9951656,"about_ca_topic_score_gemma":0.99783933,"teacher_disagreement_score":0.052988473,"about_ca_system_score_codex":0.052988473,"about_ca_system_score_gemma":0.079945505,"threshold_uncertainty_score":0.38446003},"labels":[],"label_agreement":null},{"id":"W3045504902","doi":"10.1007/s40999-020-00546-z","title":"Unified Matrix Frameworks for Water Hammer Analysis in Pipe Networks","year":2020,"lang":"en","type":"article","venue":"International Journal of Civil Engineering","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Matrix (chemical analysis); Water hammer; System of linear equations; Linear equation; Flow (mathematics); Computer science; Applied mathematics; Network topology; Topology (electrical circuits); Flow network; Mathematics; Mathematical optimization; Algorithm; Control theory (sociology); Mathematical analysis; Engineering; Geometry; Mechanical engineering; Materials science","score_opus":0.00664706051891017,"score_gpt":0.21166767597372826,"score_spread":0.2050206154548181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045504902","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":[0.0015808236,0.00024083987,0.9937471,0.00012985895,0.000049823982,0.000017278451,0.00008147752,0.00007401342,0.0040787454],"genre_scores_gemma":[0.39808494,0.002970624,0.56738985,0.0004306611,0.0006259709,0.00072109303,0.00077782635,0.0008259264,0.028173143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993411,0.0002867544,0.000035142984,0.00006350016,0.00020300935,0.00007046143],"domain_scores_gemma":[0.9983601,0.0008192908,0.00011148157,0.00016937741,0.00043860858,0.000101110556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017100037,0.0013236003,0.0014057938,0.0014711735,0.0007421022,0.0017933719,0.002496089,0.0016478498,0.0076166424],"category_scores_gemma":[0.0042480305,0.0007396554,0.0013701767,0.0018880985,0.0012095568,0.0027163299,0.0020213549,0.0021134315,0.0016388687],"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":[0.000017205433,0.000043066266,0.00016146424,0.00007517326,0.00003389455,0.00006988666,0.000058855232,0.5927314,0.00091352407,0.38974985,0.0023777385,0.0137679055],"study_design_scores_gemma":[0.000003089076,0.000006607285,0.000026302285,0.000008411069,0.000004068693,0.000006903447,0.000012378928,0.930746,0.000081965096,0.06784286,0.0012552362,0.000006128557],"about_ca_topic_score_codex":0.008639334,"about_ca_topic_score_gemma":0.012465938,"teacher_disagreement_score":0.008639334,"about_ca_system_score_codex":0.0009926345,"about_ca_system_score_gemma":0.001345005,"threshold_uncertainty_score":0.02548021},"labels":[],"label_agreement":null},{"id":"W3046971621","doi":"10.1080/1573062x.2020.1800759","title":"Dynamics of material detachment from drinking water pipes under flushing conditions in a full-scale drinking water laboratory system","year":2020,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and 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":"Queen's University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Flushing; Environmental science; Geotechnical engineering; Flow (mathematics); Phase (matter); Materials science; Mechanics; Geology; Chemistry","score_opus":0.006174662019821729,"score_gpt":0.1778698099909459,"score_spread":0.17169514797112417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046971621","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":[0.99951243,0.000034949127,0.00025177866,0.0000075192247,0.0000020514271,0.000011755559,0.00004940763,0.000020089852,0.000109937326],"genre_scores_gemma":[0.9991947,0.000038488997,0.00039546168,0.00000914943,0.0000013161684,0.000014899621,0.000075692646,0.0000044678027,0.0002657968],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998042,0.000021487675,0.000010239408,0.000048474878,0.000058815313,0.000056859157],"domain_scores_gemma":[0.99976236,0.0000699395,0.000048275542,0.000018716057,0.00006287233,0.00003777338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002651322,0.00025796582,0.0004920376,0.00030759288,0.00051679957,0.0004130723,0.0002847888,0.0004243321,0.0009834957],"category_scores_gemma":[0.00040147238,0.00016852768,0.00029328364,0.0001931071,0.00038066666,0.0003878209,0.0003964217,0.00042875044,0.00019211243],"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":[0.0006337263,0.0002163018,0.016139606,0.0001414202,0.000018836685,0.00032254762,0.00036161326,0.0025974964,0.972757,0.00003071811,0.00012730615,0.006653437],"study_design_scores_gemma":[0.000057340047,0.0044490676,0.21550503,0.000025628166,0.000077013174,0.00022476251,0.0015116904,0.039405964,0.73727417,0.000090891546,0.0013080708,0.00007049396],"about_ca_topic_score_codex":0.0036950938,"about_ca_topic_score_gemma":0.0044382107,"teacher_disagreement_score":0.0036950938,"about_ca_system_score_codex":0.00040884342,"about_ca_system_score_gemma":0.00026838813,"threshold_uncertainty_score":0.0073471665},"labels":[],"label_agreement":null},{"id":"W3048129067","doi":"10.3390/w12082235","title":"A Novel Lazy Serpent Algorithm for the Prioritization of Leak Repairs in Water Networks","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"Computer science; Prioritization; Genetic algorithm; Computation; Scheduling (production processes); Serpent (symbolism); Algorithm; Operations research; Mathematical optimization; Machine learning; Engineering; Mathematics; Management science","score_opus":0.011791337519971735,"score_gpt":0.18223308633112156,"score_spread":0.17044174881114982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048129067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02019115,0.00013554939,0.97673655,0.00013150847,0.00003911152,0.00007903676,0.000044415803,0.000532063,0.0021106591],"genre_scores_gemma":[0.37468395,0.00021059634,0.6204497,0.00016226312,0.00004205465,0.00020327081,0.00019066482,0.00016599533,0.0038914268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999516,0.0001588972,0.00003225847,0.00009356043,0.00010878755,0.00009050961],"domain_scores_gemma":[0.9992175,0.00041840077,0.00011022556,0.000054995307,0.00013328965,0.000065572036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011080914,0.0009381018,0.0011261808,0.00083683437,0.00060983363,0.0010529867,0.0013014445,0.0012815503,0.0028304025],"category_scores_gemma":[0.0024313603,0.00046275198,0.0006676902,0.0006708381,0.000681103,0.0011853317,0.00076355814,0.00085235067,0.00051010086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001271688,0.00008667494,0.0005202947,0.000055724933,0.000024638457,0.000055918554,0.000041568856,0.9359383,0.0019216662,0.0066995285,0.0011879809,0.0533406],"study_design_scores_gemma":[0.000024392411,0.000041336447,0.00005023595,0.000005067906,0.00000572725,0.000014586072,0.0000084355015,0.99725085,0.000502905,0.0016018193,0.0004906672,0.000004010741],"about_ca_topic_score_codex":0.00539972,"about_ca_topic_score_gemma":0.005494895,"teacher_disagreement_score":0.00539972,"about_ca_system_score_codex":0.0009642517,"about_ca_system_score_gemma":0.0027964092,"threshold_uncertainty_score":0.010736585},"labels":[],"label_agreement":null},{"id":"W3048154247","doi":"10.1061/9780784483190.033","title":"Developing a Decision-Support System to Optimize Rehabilitation and Replacement Programs for Ferrous Distribution Mains in Municipal Water Systems","year":2020,"lang":"en","type":"article","venue":"Pipelines 2020","topic":"Water Systems and 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":"Aecom (Canada)","funders":"","keywords":"Mains electricity; Rehabilitation; Decision support system; Electricity; Ferrous; Distribution (mathematics); Computer science; Environmental science; Engineering; Electrical engineering; Materials science; Artificial intelligence; Voltage","score_opus":0.020324506506429824,"score_gpt":0.24251928186757354,"score_spread":0.2221947753611437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048154247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21216711,0.00017372341,0.7685354,0.0007021243,0.00008639572,0.0009789615,0.0010953668,0.007246433,0.009014513],"genre_scores_gemma":[0.7107551,0.000120234625,0.285571,0.00011825153,0.000018783876,0.0006038868,0.0006154986,0.00008705778,0.0021102056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996278,0.00012689056,0.000033981436,0.00009946798,0.000055552664,0.000056340865],"domain_scores_gemma":[0.99876666,0.0007783688,0.00011734298,0.00003832146,0.0002129562,0.00008625167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011804946,0.0010835541,0.00082695275,0.0008318904,0.0007044134,0.0018162256,0.0013707868,0.0015123255,0.004678368],"category_scores_gemma":[0.0028663245,0.0004784947,0.0007026879,0.0005929298,0.00035293624,0.0008619874,0.0007416529,0.0008829239,0.0005754486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016425582,0.0002200847,0.0024899356,0.00012969738,0.000062700085,0.00020005678,0.00011199651,0.95411503,0.00236076,0.0015192059,0.0010957676,0.037530437],"study_design_scores_gemma":[0.000031727683,0.00004908693,0.00017613207,0.000011373411,0.000014969526,0.000010996681,0.000041673167,0.99761707,0.001032315,0.00051872106,0.0004895886,0.000006364512],"about_ca_topic_score_codex":0.01220316,"about_ca_topic_score_gemma":0.0086406525,"teacher_disagreement_score":0.01220316,"about_ca_system_score_codex":0.0011934948,"about_ca_system_score_gemma":0.0027267274,"threshold_uncertainty_score":0.024264276},"labels":[],"label_agreement":null},{"id":"W3048302834","doi":"10.1061/9780784483213.025","title":"Keeping the Water Moving: Solving Pressure Problems with Multiple Constraints in Stillwater, Oklahoma","year":2020,"lang":"en","type":"article","venue":"Pipelines 2020","topic":"Water Systems and 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":"Stillwater (Canada)","funders":"","keywords":"Computer science; Mathematical optimization; Environmental science; Mathematics","score_opus":0.01168158512085955,"score_gpt":0.17480916357172238,"score_spread":0.16312757845086284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048302834","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":[0.76098853,0.0044654105,0.16555177,0.009890219,0.0005608965,0.00094092963,0.002563799,0.00073173037,0.054306675],"genre_scores_gemma":[0.9021727,0.0009827031,0.08534842,0.00058975926,0.00010271386,0.00037226136,0.0010234507,0.00012695702,0.00928115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934155,0.00021046602,0.000032999877,0.00012120918,0.000090396694,0.00020332196],"domain_scores_gemma":[0.99699855,0.002385497,0.00015336057,0.00004813134,0.0002530319,0.00016148838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015313625,0.001539493,0.0010935841,0.0008678822,0.0017597877,0.0022413544,0.0014943136,0.0022791352,0.005968397],"category_scores_gemma":[0.004715047,0.00094517274,0.00085776905,0.0011643686,0.0010850648,0.0018386382,0.0016075477,0.0018749278,0.00021652877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008837163,0.00017119592,0.0037056906,0.00017909432,0.000057998433,0.0004491431,0.00015807734,0.973333,0.00029201864,0.0037509778,0.0037229357,0.014091477],"study_design_scores_gemma":[0.00005117257,0.0000657065,0.0012243333,0.00004955158,0.000025113315,0.000028587026,0.00052980985,0.99235356,0.00018823873,0.002896516,0.0025696116,0.000017783756],"about_ca_topic_score_codex":0.18265733,"about_ca_topic_score_gemma":0.18380459,"teacher_disagreement_score":0.18265733,"about_ca_system_score_codex":0.0024337857,"about_ca_system_score_gemma":0.0040927124,"threshold_uncertainty_score":0.36318827},"labels":[],"label_agreement":null},{"id":"W3048347349","doi":"10.1061/9780784483206.001","title":"A Component-Based Approach in Assessing Sewer Manholes","year":2020,"lang":"en","type":"article","venue":"Pipelines 2020","topic":"Water Systems and 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":"Aecom (Canada)","funders":"","keywords":"Component (thermodynamics); Computer science; Environmental science; Physics","score_opus":0.02101851547984161,"score_gpt":0.21389162111295495,"score_spread":0.19287310563311336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048347349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025544163,0.00016042526,0.9711365,0.00009807039,0.000024437879,0.00017432142,0.000110109664,0.0001672616,0.0025848153],"genre_scores_gemma":[0.73973143,0.00039568087,0.2570091,0.000038817463,0.000038317798,0.00030859007,0.00019688539,0.000047874055,0.0022332442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99841213,0.0006574234,0.00008131799,0.00027590603,0.0004708201,0.00010233331],"domain_scores_gemma":[0.9986792,0.00077745464,0.000121538236,0.000048337195,0.0003201306,0.000053290976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014681114,0.0016965896,0.0010279798,0.004196868,0.00080768095,0.0021704372,0.001727485,0.0015218178,0.0021930013],"category_scores_gemma":[0.0043428834,0.00069313496,0.0010541923,0.0027221462,0.00094634056,0.0018502763,0.001360702,0.0009861642,0.00021173368],"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":[0.000050870032,0.00006369765,0.0026449182,0.000092941875,0.00009083877,0.00011624561,0.00008757207,0.95403355,0.0017674168,0.011220493,0.00032302274,0.029508417],"study_design_scores_gemma":[0.0000025158492,0.00002777467,0.0005357641,0.000006593169,0.000022189071,0.000019014828,0.000033179345,0.9947272,0.00025776832,0.004054053,0.0003046176,0.00000934017],"about_ca_topic_score_codex":0.016762605,"about_ca_topic_score_gemma":0.014334006,"teacher_disagreement_score":0.016762605,"about_ca_system_score_codex":0.0014434905,"about_ca_system_score_gemma":0.0015935495,"threshold_uncertainty_score":0.033330083},"labels":[],"label_agreement":null},{"id":"W3080872213","doi":"10.1016/j.jenvman.2020.111277","title":"Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty","year":2020,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water Systems and 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":"Université du Québec à Montréal","funders":"","keywords":"Supply chain; Water supply; Sustainability; Environmental economics; Sustainable development; Pareto principle; Water scarcity; Supply chain network; Business; Wastewater; Supply chain management; Environmental resource management; Environmental science; Engineering; Environmental engineering; Operations management; Economics; Water resources; Ecology; Marketing","score_opus":0.005961559596458523,"score_gpt":0.16773476625447567,"score_spread":0.16177320665801714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080872213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34315553,0.00038701502,0.6443169,0.001087167,0.00010379124,0.00029261567,0.00066366896,0.0005332964,0.009459926],"genre_scores_gemma":[0.9850515,0.00009283683,0.011320368,0.000030176405,0.000013855561,0.00010072567,0.00014204814,0.000018296354,0.0032302453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866545,0.00037208243,0.000054011045,0.0004024481,0.00022235248,0.0002835712],"domain_scores_gemma":[0.9976422,0.0011630916,0.00038628443,0.000082146005,0.00057606073,0.00015020577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020518044,0.0009613818,0.0013672312,0.00089721195,0.001625155,0.002484195,0.0017369996,0.002554765,0.0044865357],"category_scores_gemma":[0.0036990335,0.0007688104,0.00077237585,0.0011918854,0.0015200003,0.002164531,0.0018357752,0.0011064427,0.00033850374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011939319,0.0000360534,0.00047735873,0.000038117978,0.00002920821,0.00014309431,0.000046718807,0.9886177,0.00090973586,0.004589095,0.0003292351,0.0046643214],"study_design_scores_gemma":[0.000013048083,0.00003868188,0.00013256715,0.0000036789245,0.000010994725,0.000013926804,0.000017748947,0.9960271,0.00017459923,0.0034331682,0.00012693706,0.0000075577536],"about_ca_topic_score_codex":0.016892085,"about_ca_topic_score_gemma":0.012488876,"teacher_disagreement_score":0.016892085,"about_ca_system_score_codex":0.0028742892,"about_ca_system_score_gemma":0.0028950984,"threshold_uncertainty_score":0.033587515},"labels":[],"label_agreement":null},{"id":"W3081618283","doi":"","title":"Innovative Water Planning and Management Tool: Estimating Streamflow Depletion Caused by Groundwater Pumping Using Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Groundwater; Streamflow; Environmental science; Hydrology (agriculture); Water resource management; Artificial neural network; Computer science; Geology; Geography; Drainage basin; Artificial intelligence; Geotechnical engineering; Cartography","score_opus":0.015443560606647672,"score_gpt":0.22202062540737685,"score_spread":0.20657706480072918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081618283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23547986,0.0002729502,0.7592027,0.00030733077,0.00006997551,0.00006297541,0.00049922324,0.0016127157,0.0024922406],"genre_scores_gemma":[0.9285425,0.000102220736,0.069413215,0.00003995142,0.00003078269,0.00005823284,0.00019131764,0.000026247648,0.001595529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998784,0.000024001514,0.000009598935,0.000035236397,0.000035119985,0.000017568827],"domain_scores_gemma":[0.99973327,0.00012073087,0.000039520135,0.00001657053,0.00007251569,0.000017418044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033366823,0.00063615333,0.0004684611,0.0008122923,0.00025569176,0.0006543707,0.00055242993,0.00085449294,0.0010288056],"category_scores_gemma":[0.0009664177,0.00036545185,0.00036913046,0.0005428003,0.00018105122,0.00069382257,0.0004516724,0.0004502184,0.000119241464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014282145,0.00011699379,0.0095143225,0.00006323783,0.00009313656,0.00009766588,0.00002629905,0.88975346,0.0047277906,0.0008054143,0.0007367368,0.093922235],"study_design_scores_gemma":[0.0000030257638,0.0000063242715,0.00048558088,0.0000012271962,0.0000063468383,0.0000033043168,0.0000019481379,0.9986338,0.0006481009,0.0001587185,0.000049197308,0.0000025037548],"about_ca_topic_score_codex":0.009576404,"about_ca_topic_score_gemma":0.013196586,"teacher_disagreement_score":0.009576404,"about_ca_system_score_codex":0.0004993939,"about_ca_system_score_gemma":0.0006522599,"threshold_uncertainty_score":0.01904136},"labels":[],"label_agreement":null},{"id":"W3083633621","doi":"10.1016/j.ijepes.2020.106474","title":"Dynamic behaviour of a Francis turbine during voltage regulation in the electrical power system","year":2020,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"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":"FP7 Energy; BC Hydro; European Commission","keywords":"Draft tube; Francis turbine; Turbine; Engineering; Marine engineering; Torque; Vibration; Stator; Power (physics); Mechanical engineering; Acoustics; Physics","score_opus":0.004008234395011299,"score_gpt":0.18942731571320875,"score_spread":0.18541908131819745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083633621","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":[0.99521494,0.000052860192,0.003293141,0.000031740972,0.000012534435,0.000016123831,0.00014537462,0.00014419077,0.001089154],"genre_scores_gemma":[0.99923646,0.000019998164,0.0003161352,0.0000059690997,0.0000014889655,0.000005794229,0.00008236458,0.000007335812,0.00032457084],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980146,0.00003082847,0.000010719455,0.00004092281,0.000084860934,0.0000312035],"domain_scores_gemma":[0.9997098,0.000111199406,0.000026246851,0.000038427508,0.00008266925,0.000031635358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001855501,0.0003460261,0.00059586833,0.00035475596,0.00033941356,0.00037830128,0.00044736534,0.0005016881,0.0015971502],"category_scores_gemma":[0.0005595469,0.00016306182,0.00027439528,0.00021356638,0.00041658324,0.0002819209,0.00019801062,0.0004091267,0.00034480964],"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":[0.0013131468,0.00031037486,0.017978717,0.0002893155,0.00009781329,0.0020012883,0.0009863438,0.08545506,0.8491468,0.0005139023,0.00178023,0.04012708],"study_design_scores_gemma":[0.00008526822,0.005094661,0.18856977,0.000056391324,0.00012112345,0.0008256197,0.0012538088,0.5447163,0.25444528,0.00031325303,0.004325451,0.00019306967],"about_ca_topic_score_codex":0.0038688432,"about_ca_topic_score_gemma":0.0034276845,"teacher_disagreement_score":0.0038688432,"about_ca_system_score_codex":0.0003257245,"about_ca_system_score_gemma":0.00013550282,"threshold_uncertainty_score":0.007692635},"labels":[],"label_agreement":null},{"id":"W3083967330","doi":"10.2166/wst.2020.430","title":"Air flow model development and application in a complex combined sewer system","year":2020,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Body orifice; Flow (mathematics); Volumetric flow rate; Airflow; Leak; Environmental science; Calibration; Momentum (technical analysis); Mechanics; Control theory (sociology); Engineering; Environmental engineering; Mathematics; Computer science; Mechanical engineering; Physics","score_opus":0.010973639750227837,"score_gpt":0.18270586829452523,"score_spread":0.1717322285442974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083967330","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":[0.7841916,0.00025460828,0.19009982,0.00024073204,0.000061857536,0.000510028,0.0015261163,0.0013597399,0.021755403],"genre_scores_gemma":[0.9734161,0.00013401342,0.019468013,0.000019167952,0.000007758228,0.00024039028,0.0003809126,0.000058280457,0.0062753586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984705,0.000021726848,0.000007711196,0.00003873488,0.000051071223,0.000033732653],"domain_scores_gemma":[0.9996513,0.00011934889,0.000026535374,0.000019067107,0.00016161369,0.000022073978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035432013,0.0006921982,0.0009066154,0.00053121644,0.0009042417,0.0011218399,0.0013446267,0.0014739688,0.002416428],"category_scores_gemma":[0.000599166,0.00037915405,0.00074388605,0.0005899559,0.0006717735,0.00044395358,0.0006117302,0.0007543079,0.00026921977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015559155,0.000023721326,0.00063672866,0.000016530237,0.000003836326,0.000025773235,0.000018933693,0.99661034,0.00077969255,0.00035457133,0.000082553444,0.0014316902],"study_design_scores_gemma":[0.000007964623,0.000015269772,0.00029938322,0.0000016608243,0.0000029132425,0.00000314835,0.000011296286,0.99890256,0.0004305655,0.00007357643,0.00024757895,0.000004078922],"about_ca_topic_score_codex":0.29185462,"about_ca_topic_score_gemma":0.15723363,"teacher_disagreement_score":0.29185462,"about_ca_system_score_codex":0.0024078565,"about_ca_system_score_gemma":0.0034599362,"threshold_uncertainty_score":0.58031166},"labels":[],"label_agreement":null},{"id":"W3086663172","doi":"10.5194/egusphere-egu2020-4661","title":"Uncertainty of discharge measurement using salt dilution","year":2020,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Telus (Canada)","funders":"","keywords":"Dilution; Measurement uncertainty; Conductivity; TRACER; Uncertainty analysis; Mixing (physics); Observational error; Range (aeronautics); Propagation of uncertainty; Mechanics; Chemistry; Materials science; Thermodynamics; Physics; Mathematics; Statistics","score_opus":0.04854453720264133,"score_gpt":0.20161549472075777,"score_spread":0.15307095751811645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086663172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069575496,0.00073208346,0.9143587,0.00060460455,0.00035523265,0.00008119965,0.0007412265,0.0012440796,0.012307454],"genre_scores_gemma":[0.9174122,0.0005768654,0.07678658,0.00028180773,0.00015897512,0.00011478077,0.0010440773,0.0003769016,0.0032477346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9891662,0.0022071004,0.00063588016,0.0017336216,0.00584247,0.00041474518],"domain_scores_gemma":[0.9904019,0.005046222,0.0007170151,0.0014985065,0.0021853517,0.00015101505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053670597,0.0011242202,0.001150471,0.002440188,0.00077403995,0.0034865458,0.0016472829,0.0013797435,0.0017697815],"category_scores_gemma":[0.025777869,0.0005235955,0.0017926013,0.0019413983,0.00191621,0.0034728074,0.0033349362,0.0015170482,0.0007417801],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048877875,0.00008061086,0.019683717,0.0003860468,0.00024879337,0.000545256,0.00053031597,0.7208018,0.021776754,0.081007645,0.0032488024,0.15120155],"study_design_scores_gemma":[0.000037074544,0.00018672814,0.0068997857,0.0001158156,0.000097330645,0.0003771199,0.00013549955,0.8766634,0.043716397,0.05948732,0.012060146,0.00022342912],"about_ca_topic_score_codex":0.0051342333,"about_ca_topic_score_gemma":0.00158207,"teacher_disagreement_score":0.0053670597,"about_ca_system_score_codex":0.0021607792,"about_ca_system_score_gemma":0.001484644,"threshold_uncertainty_score":0.02838403},"labels":[],"label_agreement":null},{"id":"W3091263495","doi":"10.1061/(asce)ps.1949-1204.0000511","title":"Prediction of Breaks in Municipal Drinking Water Linear Assets","year":2020,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and 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":"Concordia University; Sumitomo Precision Products (Canada); SNC-Lavalin (Canada)","funders":"","keywords":"Asset management; Pipeline transport; Pipeline (software); Metric (unit); Predictive modelling; Linear regression; Regression analysis; Asset (computer security); Sensitivity (control systems); Computer science; Environmental science; Data mining; Econometrics; Mathematics; Engineering; Machine learning; Environmental engineering; Operations management; Business","score_opus":0.02225635866992046,"score_gpt":0.22185368886503073,"score_spread":0.19959733019511028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091263495","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":[0.9855825,0.00006643557,0.012846145,0.00008409928,0.00000880687,0.0000282064,0.0006496622,0.00015466072,0.00057946495],"genre_scores_gemma":[0.99648017,0.00003422533,0.0024686446,0.0000041244175,0.0000020017017,0.000008053499,0.0005519544,0.0000062817567,0.00044453025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99977845,0.000044180466,0.000011208603,0.00006539545,0.000050631857,0.000050162045],"domain_scores_gemma":[0.99909484,0.00042577798,0.00017446218,0.00006251626,0.00016943677,0.000072967174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063055434,0.0006245861,0.0003196162,0.00097511016,0.00021344423,0.0006269902,0.0005254701,0.0005238261,0.0008621764],"category_scores_gemma":[0.0028847833,0.00024845716,0.00040766675,0.00066953554,0.0001944722,0.00059296476,0.0003934456,0.00064402586,0.00021860693],"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":[0.00014110988,0.00015231477,0.16793776,0.000032846645,0.00005606923,0.00018664452,0.00006157496,0.8003289,0.0013720474,0.0003823453,0.0006478732,0.02870062],"study_design_scores_gemma":[0.0000023881028,0.00004111312,0.022411304,0.0000037022808,0.00000594259,0.000009299903,0.000039742583,0.976576,0.00063084834,0.00014066232,0.00013452822,0.000004554843],"about_ca_topic_score_codex":0.06865032,"about_ca_topic_score_gemma":0.056668136,"teacher_disagreement_score":0.06865032,"about_ca_system_score_codex":0.0012098189,"about_ca_system_score_gemma":0.0007231027,"threshold_uncertainty_score":0.13650143},"labels":[],"label_agreement":null},{"id":"W3092111695","doi":"10.2166/aqua.2020.147","title":"Lifecycle cost optimization of pipeline projects","year":2020,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":"HydraTek (Canada)","funders":"National Plan for Science,Technology and Innovation","keywords":"Pipeline (software); Nominal Pipe Size; Range (aeronautics); Pipeline transport; Total cost; Genetic algorithm; Reliability engineering; Engineering; Component (thermodynamics); Operational costs; Flow (mathematics); Computer science; Operations research; Mechanical engineering; Mathematics","score_opus":0.03469921188672674,"score_gpt":0.26283856854188964,"score_spread":0.22813935665516288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092111695","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":[0.80903333,0.00061197026,0.17606992,0.0003132257,0.000020247462,0.00017540334,0.00026964812,0.00012708946,0.013379171],"genre_scores_gemma":[0.9830116,0.000119302014,0.015672393,0.0000062010968,0.0000026192745,0.00007464697,0.000097889046,0.000019753934,0.000995649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993567,0.00033810374,0.000015278318,0.000037333895,0.00014866091,0.0001039781],"domain_scores_gemma":[0.99905187,0.0006179608,0.000095103525,0.000038478687,0.0001444557,0.000052123563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013055869,0.00056402816,0.000525986,0.0012855259,0.00032960362,0.0007310968,0.00043736363,0.00056766346,0.0016939265],"category_scores_gemma":[0.002885638,0.00039209728,0.00050468504,0.0008902874,0.0004308073,0.0006943227,0.00039820757,0.00038250553,0.000100786056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047461097,0.00003265256,0.0006513249,0.000030597515,0.000008476955,0.000028075076,0.000009254954,0.988618,0.00064572826,0.0015383271,0.00014133772,0.008248776],"study_design_scores_gemma":[0.000011376205,0.00012899788,0.00087418593,0.000007760029,0.000009262647,0.000014919894,0.00002488389,0.99641764,0.00082298776,0.0013762938,0.00030511548,0.0000065635854],"about_ca_topic_score_codex":0.0033113745,"about_ca_topic_score_gemma":0.0031400304,"teacher_disagreement_score":0.0033113745,"about_ca_system_score_codex":0.0014736068,"about_ca_system_score_gemma":0.0011345494,"threshold_uncertainty_score":0.010691822},"labels":[],"label_agreement":null},{"id":"W3093224599","doi":"10.1115/fedsm2020-20236","title":"Experimental Comparison of Newtonian and Non-Newtonian Multiphase Flow in Horizontal Pipes","year":2020,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"","keywords":"Non-Newtonian fluid; Newtonian fluid; Mechanics; Pressure drop; Thermodynamics; Materials science; Flow (mathematics); Turbulence; Physics","score_opus":0.013339039202548917,"score_gpt":0.22916361601443289,"score_spread":0.21582457681188397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093224599","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":[0.9953159,0.00011552076,0.0038611689,0.000013299623,0.000010715387,0.000038817278,0.000103120496,0.0000714369,0.0004701337],"genre_scores_gemma":[0.99631715,0.00012001246,0.0029156886,0.000007317917,0.0000080938335,0.000048406804,0.0000993678,0.000010819112,0.0004730513],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950576,0.00010523812,0.00003555957,0.00008776318,0.00019466138,0.000070984126],"domain_scores_gemma":[0.99866724,0.00069644203,0.00020160704,0.00008266925,0.00029076575,0.00006137443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005490549,0.00034014758,0.00023583793,0.0004522626,0.0003117524,0.00026373094,0.00035570186,0.00041089262,0.0017879232],"category_scores_gemma":[0.0011616461,0.00020496392,0.00016430479,0.000443078,0.00056753226,0.0005339849,0.00028322698,0.000389123,0.00013453042],"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":[0.0004400177,0.00023951675,0.0025624158,0.00014388977,0.000007995747,0.00007007967,0.00025221935,0.0010477718,0.98714983,0.00019043672,0.00006915214,0.0078266105],"study_design_scores_gemma":[0.000028768805,0.0021460415,0.015625637,0.0000118707085,0.000016087328,0.000051478222,0.00014889454,0.008319407,0.97300804,0.00006852902,0.00055980054,0.000015444777],"about_ca_topic_score_codex":0.0005142363,"about_ca_topic_score_gemma":0.0004142942,"teacher_disagreement_score":0.0017879232,"about_ca_system_score_codex":0.00019526298,"about_ca_system_score_gemma":0.0001615817,"threshold_uncertainty_score":0.005981207},"labels":[],"label_agreement":null},{"id":"W3093640155","doi":"10.3390/su12208733","title":"Geoadditive Quantile Regression Model for Sewer Pipes Deterioration Using Boosting Optimization Algorithm","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Water Systems and 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":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Categorical variable; Quantile regression; Quantile; Econometrics; Kriging; Boosting (machine learning); Feature selection; Computer science; Regression analysis; Statistics; Data mining; Mathematics; Machine learning","score_opus":0.02374483196381861,"score_gpt":0.2550643073166679,"score_spread":0.2313194753528493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093640155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024151772,0.0005559875,0.97339547,0.00027842072,0.000033534197,0.0000493019,0.00014384257,0.00023828851,0.0011533665],"genre_scores_gemma":[0.8415531,0.0010566426,0.14930892,0.00025079315,0.00009044014,0.00046349684,0.00078814523,0.000128917,0.006359461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878615,0.0006663111,0.000038007376,0.00020309698,0.00013664419,0.00016977303],"domain_scores_gemma":[0.9975744,0.0017493451,0.00018695173,0.000059371527,0.0003752688,0.000054692122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004158307,0.0009878357,0.0025175703,0.0006921699,0.0003950186,0.0011307693,0.0019150113,0.0015768793,0.0025053024],"category_scores_gemma":[0.004933613,0.00081096555,0.0013780757,0.0009768715,0.00074091216,0.00067632867,0.0011067168,0.0016796892,0.00046649043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021154809,0.000014444857,0.00049591926,0.000023978238,0.00002264188,0.000018236551,0.000014976734,0.9925587,0.00018511276,0.002572877,0.0002442724,0.0038277034],"study_design_scores_gemma":[0.000002280177,0.0000048460506,0.00008633473,0.00000194197,0.0000031582126,0.0000017139329,0.0000014345268,0.99925476,0.000020691818,0.0005630923,0.00005807859,0.0000015942372],"about_ca_topic_score_codex":0.016711237,"about_ca_topic_score_gemma":0.0078112455,"teacher_disagreement_score":0.016711237,"about_ca_system_score_codex":0.0011094185,"about_ca_system_score_gemma":0.001565434,"threshold_uncertainty_score":0.03322792},"labels":[],"label_agreement":null},{"id":"W3094827670","doi":"10.2166/hydro.2020.119","title":"Numerical solutions of hyperbolic systems of conservation laws combining unsteady friction and viscoelastic pipes","year":2020,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"Cegep de Thetford; Cegep de Saint Hyacinthe","funders":"","keywords":"Conservation law; Viscoelasticity; Finite volume method; Term (time); Conservation of mass; Mathematics; Numerical analysis; Mechanics; Hyperbolic partial differential equation; Mathematical analysis; Applied mathematics; Physics; Partial differential equation; Thermodynamics","score_opus":0.01741167327166106,"score_gpt":0.18498881689533278,"score_spread":0.1675771436236717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094827670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17196871,0.00032424164,0.816472,0.00030880971,0.00008590581,0.000129055,0.00008863259,0.00022994763,0.010392677],"genre_scores_gemma":[0.87489456,0.00018050532,0.11910137,0.000052136813,0.000032043212,0.000105731466,0.00008405436,0.000063904095,0.005485766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964,0.00014817277,0.000026062293,0.000037086742,0.00010845038,0.00004022887],"domain_scores_gemma":[0.9989291,0.0006631529,0.00012831236,0.000068496,0.00015976773,0.000051067076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001309577,0.000567851,0.0007816397,0.00068718084,0.00036722355,0.0010596531,0.0008398107,0.0015127186,0.0017439266],"category_scores_gemma":[0.003023639,0.00042130632,0.00049507443,0.00057604624,0.0013879318,0.00063178997,0.0013658693,0.00065444276,0.0002115551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037121375,0.0000435664,0.0008307344,0.00009708365,0.000020881569,0.0001737477,0.00012165944,0.9717463,0.00310804,0.015243128,0.00020062843,0.008377171],"study_design_scores_gemma":[0.0000052868627,0.0000108488175,0.00006728957,0.0000054600673,0.0000014875379,0.0000122431975,0.000012770748,0.9977616,0.00051791524,0.0013757569,0.00022635593,0.0000029565513],"about_ca_topic_score_codex":0.0032684556,"about_ca_topic_score_gemma":0.0029391353,"teacher_disagreement_score":0.0032684556,"about_ca_system_score_codex":0.0005868535,"about_ca_system_score_gemma":0.0008384938,"threshold_uncertainty_score":0.0069257617},"labels":[],"label_agreement":null},{"id":"W3095279048","doi":"10.3390/w12113101","title":"Use of Energy-Based Domain Knowledge as Feedback to Evolutionary Algorithms for the Optimization of Water Distribution Networks","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"","keywords":"Benchmark (surveying); Evolutionary algorithm; Mathematical optimization; Sorting; Computer science; Multi-objective optimization; Reliability (semiconductor); Domain (mathematical analysis); Algorithm; Mathematics; Power (physics)","score_opus":0.01833096864221389,"score_gpt":0.20045636645880524,"score_spread":0.18212539781659134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095279048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07416645,0.00023735131,0.92071354,0.00014808426,0.000029127086,0.000066682354,0.000037452563,0.0004685093,0.0041328687],"genre_scores_gemma":[0.8643303,0.00017783816,0.1343518,0.000055449487,0.00001686263,0.00012298826,0.00006590343,0.000048624643,0.00083017006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997228,0.00012479133,0.000011971132,0.000032202985,0.000087236614,0.000021063279],"domain_scores_gemma":[0.9987851,0.00088239386,0.000084234314,0.000070713366,0.00015953505,0.000018092409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009815651,0.00074804114,0.00053788075,0.0005646177,0.00025680655,0.000551866,0.0006288988,0.0006849856,0.001043992],"category_scores_gemma":[0.004375788,0.0003148135,0.00030375604,0.00046786448,0.0004528711,0.00073267374,0.00070560287,0.00054914225,0.00010315917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019656502,0.00002866823,0.00015976168,0.000022649063,0.000008605803,0.000019367912,0.000023652003,0.9684134,0.001112175,0.0009825634,0.00009021311,0.029119274],"study_design_scores_gemma":[0.0000057648463,0.000015516503,0.00006445559,0.000004120217,0.0000029572702,0.0000041544813,0.00000408983,0.9984964,0.0006250736,0.00060972647,0.00016537336,0.0000023510838],"about_ca_topic_score_codex":0.0021058617,"about_ca_topic_score_gemma":0.0022843732,"teacher_disagreement_score":0.0021058617,"about_ca_system_score_codex":0.0004253415,"about_ca_system_score_gemma":0.000491911,"threshold_uncertainty_score":0.0051910877},"labels":[],"label_agreement":null},{"id":"W3099044167","doi":"10.1061/(asce)ps.1949-1204.0000515","title":"Design–Build Project Administration Practices for the Water Industry","year":2020,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"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":"Procurement; Integrated project delivery; Design–build; Pipeline (software); Process (computing); Project team; Construction management; Request for proposal; Engineering design process; Administration (probate law); Best practice; Protocol (science); Project planning; Project management; Business; Engineering; Computer science; Civil engineering; Systems engineering; Marketing","score_opus":0.057483141868083544,"score_gpt":0.28526816078394385,"score_spread":0.2277850189158603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099044167","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":[0.6598205,0.002120413,0.2508929,0.006129839,0.00008701983,0.0015287194,0.00012015413,0.0006950425,0.078605406],"genre_scores_gemma":[0.9015777,0.0014568769,0.089456156,0.0003179169,0.000016124915,0.00046078785,0.00006646231,0.000108258246,0.0065396787],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9696017,0.023707008,0.0014101929,0.0007862784,0.0035843113,0.00091061357],"domain_scores_gemma":[0.96912056,0.0141103845,0.004554297,0.005515308,0.0050690654,0.0016303734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0202167,0.00039828883,0.00017279878,0.0012697835,0.0032738335,0.0026185813,0.0010232925,0.0006568281,0.002753394],"category_scores_gemma":[0.0324405,0.00051793037,0.00017333312,0.0018300768,0.0022111372,0.0019862708,0.0030327027,0.0012189848,0.0008469195],"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":[0.00015349773,0.0012084004,0.06287406,0.0012217058,0.000051543393,0.0006148541,0.14506449,0.007928765,0.016528128,0.03272988,0.013435887,0.7181888],"study_design_scores_gemma":[0.0001007769,0.001498105,0.09894932,0.0013196318,0.000059733626,0.0019552207,0.14517549,0.008680307,0.02217653,0.025950583,0.69392884,0.00020549198],"about_ca_topic_score_codex":0.0032427316,"about_ca_topic_score_gemma":0.011277637,"teacher_disagreement_score":0.0202167,"about_ca_system_score_codex":0.004996056,"about_ca_system_score_gemma":0.009491001,"threshold_uncertainty_score":0.10691744},"labels":[],"label_agreement":null},{"id":"W3105186209","doi":"10.1007/s40891-020-00237-8","title":"An Approach to Predict the Failure of Water Mains Under Climatic Variations","year":2020,"lang":"en","type":"article","venue":"International Journal of Geosynthetics and Ground Engineering","topic":"Water Systems and 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":"McGill University","funders":"Environment and Climate Change Canada; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Mains electricity; Environmental science; Water supply; Precipitation; Reliability (semiconductor); Electricity; Flood myth; Monte Carlo method; Hydrology (agriculture); Meteorology; Environmental engineering; Engineering; Statistics; Geotechnical engineering; Mathematics; Power (physics)","score_opus":0.008302793653493687,"score_gpt":0.18559098596170379,"score_spread":0.1772881923082101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105186209","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":[0.76699317,0.00030592276,0.22584188,0.00032761024,0.00008041913,0.00011081161,0.0012667859,0.00070349313,0.004369838],"genre_scores_gemma":[0.98909295,0.000069330636,0.00942969,0.00001561728,0.000019850095,0.000037235437,0.0002838728,0.000021971653,0.0010294471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998976,0.000021241183,0.000005366158,0.00003375634,0.00002001277,0.000022007122],"domain_scores_gemma":[0.9997664,0.00010807143,0.000033329496,0.000014272797,0.00004949584,0.000028457976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033142423,0.00070191146,0.0005453712,0.0009375629,0.00042692883,0.00057278323,0.00066727184,0.0010649337,0.0015407709],"category_scores_gemma":[0.0007396833,0.00042116822,0.00072317297,0.00059413805,0.00037333692,0.00040314283,0.00043028238,0.00061058655,0.00024613403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024347517,0.000027696702,0.0033423468,0.00000649013,0.000023726217,0.000019332905,0.000007854871,0.9920237,0.0007136485,0.00029405387,0.000116879346,0.0033999512],"study_design_scores_gemma":[0.000002205617,0.000008541558,0.0008460721,5.7351406e-7,0.0000029375637,0.0000024101807,0.0000031953866,0.99881005,0.00009571161,0.00019086787,0.000035386962,0.0000019875813],"about_ca_topic_score_codex":0.027446415,"about_ca_topic_score_gemma":0.018796328,"teacher_disagreement_score":0.027446415,"about_ca_system_score_codex":0.00073296175,"about_ca_system_score_gemma":0.0007312647,"threshold_uncertainty_score":0.054573298},"labels":[],"label_agreement":null},{"id":"W3107158303","doi":"10.1121/1.5147288","title":"Acoustical case studies of large high power hydroelectric generators: Correlation between acoustic and vibration measurements","year":2020,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Water Systems and 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":"Hydro-Québec","funders":"","keywords":"Acoustics; Vibration; Microphone; Hydroelectricity; Multiphysics; Accelerometer; Generator (circuit theory); Sound pressure; Sound power; Computer science; Power (physics); Engineering; Physics; Structural engineering; Electrical engineering; Finite element method","score_opus":0.021687453072358344,"score_gpt":0.23860769948974378,"score_spread":0.21692024641738544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107158303","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":[0.99379385,0.000069884256,0.004683621,0.000048460475,0.000006857051,0.000048278456,0.0000745212,0.00003359193,0.0012410053],"genre_scores_gemma":[0.9972614,0.000036800266,0.0021513796,0.000007230601,0.0000074351524,0.0000140879265,0.00003472079,0.0000058583005,0.00048103073],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99913234,0.00020041624,0.000046120316,0.00016172108,0.0003579108,0.00010150385],"domain_scores_gemma":[0.99674124,0.0019460908,0.00035087694,0.00029050463,0.00044679907,0.00022459592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083642063,0.00049502443,0.00040133428,0.0012029862,0.0006397917,0.0004570355,0.0008210831,0.0012164627,0.0013853902],"category_scores_gemma":[0.002509467,0.00027873696,0.00023396891,0.00071747473,0.0011058347,0.00047986687,0.00060311466,0.00041176192,0.00026743513],"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":[0.0024367173,0.003079289,0.48825696,0.0008431344,0.00026951134,0.091575414,0.008326744,0.08258994,0.22325961,0.0026687796,0.0026050636,0.094088875],"study_design_scores_gemma":[0.00024649082,0.004551932,0.60631835,0.00008828906,0.0001997629,0.034031253,0.011563,0.19393341,0.13862269,0.0027428803,0.007388635,0.00031326246],"about_ca_topic_score_codex":0.00296629,"about_ca_topic_score_gemma":0.0058426303,"teacher_disagreement_score":0.00296629,"about_ca_system_score_codex":0.0004108278,"about_ca_system_score_gemma":0.00022564556,"threshold_uncertainty_score":0.0058980584},"labels":[],"label_agreement":null},{"id":"W3109150949","doi":"10.1139/cjce-2020-0287","title":"Managing sewerage networks using both failure modes, effects and criticality analysis (FMECA) and analytic hierarchy process (AHP) methods","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Failure mode, effects, and criticality analysis; Sewerage; Analytic hierarchy process; Reliability engineering; Failure mode and effects analysis; Process (computing); Consistency (knowledge bases); Criticality; Engineering; Computer science; Risk analysis (engineering); Operations research; Environmental engineering; Business","score_opus":0.008846108482527414,"score_gpt":0.22402833892216276,"score_spread":0.21518223043963533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109150949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012599164,0.0004654776,0.9841339,0.00019412083,0.000016700642,0.00033346325,0.00007017943,0.00015461304,0.0020323521],"genre_scores_gemma":[0.29582,0.0010203314,0.7013409,0.000050681483,0.000049729566,0.00067864027,0.00017353386,0.000044069388,0.000822184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950376,0.0020112004,0.00027515055,0.00035828198,0.0021100943,0.00020758722],"domain_scores_gemma":[0.994586,0.0033085616,0.00094557356,0.00016572991,0.0008829809,0.00011121021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051128534,0.0014999798,0.00090461184,0.0054737013,0.001069324,0.0023358369,0.0013321718,0.0008117151,0.0008736195],"category_scores_gemma":[0.006933554,0.00046651263,0.0009651419,0.002438357,0.001031398,0.002180244,0.0018117669,0.001039818,0.00011483303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061681814,0.00018191349,0.004963712,0.0010571905,0.0002681887,0.00022185333,0.001278188,0.61963886,0.0077383267,0.029840153,0.0009246857,0.3338252],"study_design_scores_gemma":[0.00004197287,0.0002965908,0.002816252,0.0003195914,0.00014951032,0.00014670832,0.0010500312,0.92532563,0.0051404075,0.056624897,0.007980164,0.000108288434],"about_ca_topic_score_codex":0.006755642,"about_ca_topic_score_gemma":0.008096023,"teacher_disagreement_score":0.006755642,"about_ca_system_score_codex":0.0016178633,"about_ca_system_score_gemma":0.003654316,"threshold_uncertainty_score":0.027039707},"labels":[],"label_agreement":null},{"id":"W3109668423","doi":"10.1139/cjce-2020-0334","title":"State of watermain infrastructure: a Canadian case study using historic pipe break datasets","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Forensic engineering; Environmental science; Engineering","score_opus":0.009516688171580065,"score_gpt":0.1751579837381608,"score_spread":0.16564129556658075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109668423","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":[0.9854255,0.00022350058,0.000791042,0.00023912391,0.000005073964,0.0001015091,0.008734186,0.00004965479,0.004430482],"genre_scores_gemma":[0.98712224,0.00050981867,0.0024257207,0.00003916791,0.0000034197722,0.000033898392,0.007953462,0.000014939971,0.0018973957],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99916065,0.00008101115,0.00003910262,0.00012271402,0.00031957525,0.00027703817],"domain_scores_gemma":[0.99779874,0.00035273156,0.00026531031,0.00015460885,0.0012170883,0.00021152868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093556364,0.000373021,0.0002945432,0.003172505,0.001887908,0.001325432,0.0012406914,0.00055404246,0.0009291237],"category_scores_gemma":[0.0028961406,0.00029481092,0.0005488401,0.009179349,0.0008650528,0.00058213354,0.00073427294,0.0006291071,0.00013321255],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019563577,0.00025805997,0.90150136,0.00021729856,0.00020083344,0.0028176426,0.003690336,0.041761212,0.001234067,0.0040191635,0.0076120333,0.036492404],"study_design_scores_gemma":[0.000022480139,0.000073598196,0.93057007,0.000075929755,0.00009999718,0.00034733367,0.011381407,0.043966297,0.0008115031,0.0003547968,0.012222484,0.00007419857],"about_ca_topic_score_codex":0.99483395,"about_ca_topic_score_gemma":0.99764603,"teacher_disagreement_score":0.027367808,"about_ca_system_score_codex":0.027367808,"about_ca_system_score_gemma":0.0172925,"threshold_uncertainty_score":0.19856828},"labels":[],"label_agreement":null},{"id":"W3113184402","doi":"10.1061/(asce)hy.1943-7900.0001846","title":"Closure to “Theoretical Investigation of Leak’s Impact on Normal Modes of a Water-Filled Pipe: Small to Large Leak Impedance” by Jingrong Lin, Xun Wang, and Mohamed S. Ghidaoui","year":2020,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Leak; Closure (psychology); Electrical impedance; Mechanics; Water pipe; Engineering; Physics; Electrical engineering; Mechanical engineering; Inlet","score_opus":0.005523688521948955,"score_gpt":0.18839197169179106,"score_spread":0.1828682831698421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113184402","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15084986,0.009305012,0.66094345,0.01166903,0.009600065,0.00023516806,0.00030423,0.00066797994,0.15642527],"genre_scores_gemma":[0.93166417,0.0032880537,0.017509323,0.0011687673,0.0021242807,0.0001353024,0.0001402183,0.00043886813,0.043530874],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99949324,0.00019855997,0.000016626347,0.000102459846,0.00012997426,0.00005907034],"domain_scores_gemma":[0.998895,0.00060821127,0.00007442195,0.0000774681,0.00027042636,0.00007447555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085547916,0.0008547714,0.00090107875,0.00065661623,0.000812911,0.0015944677,0.0011643354,0.0016299534,0.008416308],"category_scores_gemma":[0.0053915055,0.0003441123,0.0015231821,0.0004088937,0.0022716788,0.0019183599,0.0015520635,0.0018375419,0.00094900996],"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":[0.0005012954,0.00025878777,0.0015597369,0.0007914247,0.00011024671,0.0019614121,0.000997858,0.32329404,0.021437965,0.5799641,0.03919118,0.02993198],"study_design_scores_gemma":[0.000028626459,0.00009437722,0.00058291195,0.00006402243,0.000034962195,0.00028985078,0.00015967713,0.9224464,0.0026555369,0.06333387,0.010261169,0.00004862077],"about_ca_topic_score_codex":0.002333235,"about_ca_topic_score_gemma":0.00079901767,"teacher_disagreement_score":0.008416308,"about_ca_system_score_codex":0.0005817575,"about_ca_system_score_gemma":0.0006917063,"threshold_uncertainty_score":0.028155327},"labels":[],"label_agreement":null},{"id":"W3113774513","doi":"10.1115/omae2020-18704","title":"Experimental Investigation of Pressure Drop of Gas/Non-Newtonian Flow in Horizontal Pipes","year":2020,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"","keywords":"Pressure drop; Mechanics; Newtonian fluid; Non-Newtonian fluid; Materials science; Flow coefficient; Thermodynamics; Flow (mathematics); Flow measurement; Volumetric flow rate; Physics","score_opus":0.008563879547932248,"score_gpt":0.1779915647560571,"score_spread":0.16942768520812485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113774513","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":[0.9931623,0.00027720226,0.005052241,0.000042156164,0.000030868825,0.000042201133,0.0003167694,0.00016601053,0.0009101569],"genre_scores_gemma":[0.99462885,0.00026374205,0.004014,0.000016465136,0.000015059713,0.00007635732,0.00015417469,0.000018790242,0.00081249553],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953306,0.00006553658,0.000027853554,0.00011159147,0.00018230476,0.00007967511],"domain_scores_gemma":[0.99930656,0.00027046425,0.00014361509,0.00005791865,0.00017012445,0.00005131259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038822874,0.00040798946,0.00030756416,0.00034554626,0.00038314692,0.00023289146,0.00036454332,0.00050556596,0.001972762],"category_scores_gemma":[0.0005423113,0.00018718763,0.00021364358,0.00047997685,0.0004571573,0.00043677658,0.00027436728,0.00068211893,0.00017926113],"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":[0.00017754696,0.00015413165,0.0010553809,0.00012902338,0.000005492314,0.000116597366,0.0001585228,0.00053843786,0.99404484,0.00016445389,0.00010300158,0.0033527547],"study_design_scores_gemma":[0.00001740577,0.0015403534,0.008097484,0.000010772275,0.000011836515,0.00006387401,0.00011167971,0.0059952145,0.9833582,0.00007328214,0.0007041439,0.000015722402],"about_ca_topic_score_codex":0.00033122083,"about_ca_topic_score_gemma":0.00022988513,"teacher_disagreement_score":0.001972762,"about_ca_system_score_codex":0.00015850044,"about_ca_system_score_gemma":0.0001601764,"threshold_uncertainty_score":0.0065995455},"labels":[],"label_agreement":null},{"id":"W3115336099","doi":"10.18280/mmep.070410","title":"A Novel Algorithm to Evaluate Definite Integrals by the Gauss-Legendre Integration Rule Based on the Stochastic Arithmetic: Application in the Model of Osmosis System","year":2020,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Water Systems and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gaussian quadrature; Mathematics; Applied mathematics; Numerical integration; Gauss; Legendre polynomials; Gaussian; Algorithm; Quadrature (astronomy); Mathematical optimization; Integral equation; Mathematical analysis; Nyström method","score_opus":0.03531600727864281,"score_gpt":0.2015148047424002,"score_spread":0.16619879746375738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115336099","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":[0.0016011389,0.000064423126,0.99757475,0.000023341789,0.000021655775,0.000012627198,0.0000061666824,0.000108080385,0.0005878288],"genre_scores_gemma":[0.09249209,0.00022485817,0.90527904,0.00006685888,0.000045575358,0.00010898254,0.000048099817,0.00011537512,0.0016191124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952817,0.000120546305,0.00003584043,0.00006652846,0.00021375262,0.000035092686],"domain_scores_gemma":[0.99960905,0.0001533869,0.00003544737,0.000046794994,0.00013381998,0.000021630636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010636856,0.00057955424,0.0008770667,0.00074134587,0.0004776965,0.000746863,0.0010575751,0.0008517303,0.0015744624],"category_scores_gemma":[0.0015047151,0.00027921467,0.0006942539,0.0007402492,0.0007870676,0.00088556774,0.0008266538,0.001125051,0.0005015944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011780616,0.00015546402,0.0011797732,0.00033632485,0.00010453563,0.0002907743,0.0003148567,0.43539724,0.030561212,0.23204824,0.0037476136,0.29574603],"study_design_scores_gemma":[0.000012814429,0.00003912075,0.00008534496,0.000010479439,0.000008430904,0.00009159278,0.000009036478,0.9833889,0.0020552156,0.012015064,0.0022715842,0.000012516832],"about_ca_topic_score_codex":0.0017825502,"about_ca_topic_score_gemma":0.0019067326,"teacher_disagreement_score":0.0017825502,"about_ca_system_score_codex":0.00043868742,"about_ca_system_score_gemma":0.001168395,"threshold_uncertainty_score":0.005625367},"labels":[],"label_agreement":null},{"id":"W3118232654","doi":"10.1115/omae2020-18203","title":"Four-Phase Flow of Oil, Gas, Water, and Sand Mixtures in Pipelines","year":2020,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"","keywords":"Pipeline transport; Petroleum engineering; Slug flow; Flow (mathematics); Two-phase flow; Multiphase flow; Environmental science; Geotechnical engineering; Geology; Materials science; Mechanics; Environmental engineering","score_opus":0.014555998716506039,"score_gpt":0.20244028830011718,"score_spread":0.18788428958361114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118232654","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":[0.9937401,0.000089034096,0.0052366513,0.000038909773,0.0000075212165,0.000019721974,0.00005658666,0.000033479268,0.0007780393],"genre_scores_gemma":[0.99846494,0.000041524592,0.0010450633,0.000003393765,0.0000016148656,0.000006378213,0.00002838523,0.0000028182105,0.00040575908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99989665,0.000021799055,0.0000059858453,0.000015760723,0.00003000974,0.000029801375],"domain_scores_gemma":[0.9998387,0.00008472064,0.000033117365,0.000005312065,0.00002407688,0.0000140007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018662428,0.00030328552,0.00028580418,0.000589697,0.0002742755,0.00037295773,0.00017608189,0.00045097174,0.0009556317],"category_scores_gemma":[0.0004437285,0.00013647978,0.00023233722,0.0002843482,0.0004267779,0.00040583717,0.00029154375,0.00021927453,0.00008899118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011900244,0.00049491145,0.021627324,0.00027671628,0.000043110536,0.000698426,0.0002043666,0.51937807,0.43254635,0.0023624972,0.00026473159,0.020913534],"study_design_scores_gemma":[0.000048965507,0.00095055945,0.01210755,0.00002074202,0.000021522837,0.000095518626,0.000255906,0.8585417,0.12637451,0.00084466825,0.0007091238,0.000029232253],"about_ca_topic_score_codex":0.0034897728,"about_ca_topic_score_gemma":0.0019433033,"teacher_disagreement_score":0.0034897728,"about_ca_system_score_codex":0.00040015992,"about_ca_system_score_gemma":0.00024834587,"threshold_uncertainty_score":0.0069388747},"labels":[],"label_agreement":null},{"id":"W3118562502","doi":"10.1007/s11081-020-09575-y","title":"Pump scheduling in drinking water distribution networks with an LP/NLP-based branch and bound","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Water Systems and 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":"Polytechnique Montréal","funders":"","keywords":"Integer programming; Branch and bound; Mathematical optimization; Linear programming relaxation; Scheduling (production processes); Computer science; Regular polygon; Heuristic; Relaxation (psychology); Linear programming; Integer (computer science); Upper and lower bounds; Branch and cut; Mathematics","score_opus":0.004095670486794863,"score_gpt":0.1627631810331442,"score_spread":0.15866751054634934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118562502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04199507,0.00058306317,0.95024467,0.00040903487,0.00010171289,0.000091348484,0.00016084763,0.0003708662,0.006043429],"genre_scores_gemma":[0.8089339,0.00045846833,0.18387875,0.00016947452,0.00013601696,0.00021879404,0.00022978892,0.00018050036,0.005794458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993537,0.00031767233,0.000020686804,0.000093689705,0.00009562927,0.00011857356],"domain_scores_gemma":[0.99866295,0.0010315363,0.000071323506,0.000036176927,0.00012876205,0.00006924729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017037716,0.0007752133,0.0018159093,0.0005778689,0.0006676163,0.0014895266,0.0010085193,0.0009733758,0.003761209],"category_scores_gemma":[0.0028158512,0.0007086367,0.00052621297,0.00095458754,0.0006728477,0.0012113502,0.00084204035,0.0012396601,0.00043658944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012220386,0.000048990067,0.00013326142,0.000054133074,0.000015336196,0.000011515543,0.000015465046,0.9807649,0.00050469727,0.002894254,0.00087766326,0.014557643],"study_design_scores_gemma":[0.0000037073878,0.000006804203,0.000014074108,0.0000011777262,0.0000022243,6.2668704e-7,0.0000017024367,0.9992786,0.00008149193,0.00054058275,0.00006820742,8.3593454e-7],"about_ca_topic_score_codex":0.0134553695,"about_ca_topic_score_gemma":0.010556827,"teacher_disagreement_score":0.0134553695,"about_ca_system_score_codex":0.0014399552,"about_ca_system_score_gemma":0.0022079875,"threshold_uncertainty_score":0.026754081},"labels":[],"label_agreement":null},{"id":"W3118651642","doi":"","title":"Condition assessment of sewer pipelines using multi attribute utility theory (MAUT)","year":2016,"lang":"en","type":"article","venue":"Qatar University QSpace (Qatar University)","topic":"Water Systems and 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":"Pipeline transport; Computer science; Forensic engineering; Environmental science; Engineering; Environmental engineering","score_opus":0.019327974107294782,"score_gpt":0.20989255392251524,"score_spread":0.19056457981522046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118651642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14251423,0.00022662268,0.8536351,0.00020126734,0.000021700087,0.00012940333,0.00018545736,0.00016329091,0.0029230306],"genre_scores_gemma":[0.97126895,0.000121476805,0.027488967,0.00001871601,0.000008985454,0.00009396235,0.00011887348,0.000011398936,0.00086857553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983967,0.0008541184,0.00007752877,0.00022030712,0.00032270336,0.00012875033],"domain_scores_gemma":[0.9980404,0.0012164209,0.00021941468,0.000089960646,0.00037230196,0.00006162155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022911634,0.00072166114,0.000637556,0.0016532746,0.00035886865,0.0017850703,0.00082927855,0.00072469906,0.0012365014],"category_scores_gemma":[0.0063055144,0.00029312065,0.0009584589,0.0015053283,0.0007134284,0.0016744918,0.0007854964,0.0006572789,0.00012233313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056972,0.00008774051,0.009521745,0.00006970696,0.0000773283,0.00009552751,0.000107926375,0.9469929,0.0007072691,0.013757668,0.00050313084,0.028022053],"study_design_scores_gemma":[0.0000019269767,0.00003757489,0.0014295809,0.0000058288297,0.000007611955,0.000012986077,0.000029148876,0.99469084,0.00018543593,0.0034595067,0.00013201371,0.0000073935025],"about_ca_topic_score_codex":0.009558488,"about_ca_topic_score_gemma":0.0048020054,"teacher_disagreement_score":0.009558488,"about_ca_system_score_codex":0.0018398471,"about_ca_system_score_gemma":0.00095761236,"threshold_uncertainty_score":0.019005716},"labels":[],"label_agreement":null},{"id":"W3118764694","doi":"","title":"Automated detection of anomalies in sewer closed circuit television videos using proportional data modeling","year":2016,"lang":"en","type":"article","venue":"Qatar University QSpace (Qatar University)","topic":"Water Systems and 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":"Closed circuit; Computer science; Artificial intelligence; Real-time computing; Computer vision; Telecommunications","score_opus":0.03122354547054465,"score_gpt":0.18921526070620032,"score_spread":0.15799171523565567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118764694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20491865,0.00007726708,0.7927117,0.00008408054,0.000017549719,0.000044357555,0.000115282106,0.0013913774,0.00063972815],"genre_scores_gemma":[0.8993825,0.000059122936,0.099686675,0.000015356098,0.00000828554,0.00003483968,0.00024486432,0.000028928484,0.00053951284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996666,0.000068185494,0.000019882056,0.00010110962,0.000103046805,0.00004119359],"domain_scores_gemma":[0.9993905,0.0003020405,0.000105496365,0.00004913627,0.00013170268,0.000021150083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045804976,0.00042622897,0.00046760432,0.0007580825,0.00018792533,0.00046128323,0.00046304805,0.00039890214,0.00040970254],"category_scores_gemma":[0.0015724368,0.00016123094,0.00037255563,0.0004246461,0.00022471076,0.00050612696,0.00036453968,0.00037266416,0.00012453408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004468543,0.00033429117,0.016942503,0.00011354577,0.000065730295,0.0002471405,0.0001893826,0.51765496,0.06411602,0.0027534058,0.001043802,0.3960924],"study_design_scores_gemma":[0.0000016168666,0.000018215409,0.0013349378,0.0000011325308,0.0000022778916,0.000014134278,0.000007962627,0.99505097,0.0032807316,0.00020431334,0.00008064386,0.0000031446295],"about_ca_topic_score_codex":0.009063483,"about_ca_topic_score_gemma":0.0057224254,"teacher_disagreement_score":0.009063483,"about_ca_system_score_codex":0.00049636327,"about_ca_system_score_gemma":0.0005865789,"threshold_uncertainty_score":0.018021464},"labels":[],"label_agreement":null},{"id":"W3119003233","doi":"10.5267/j.dsl.2020.12.001","title":"Ions motion optimization algorithm for multiobjective optimization problems","year":2021,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Water Systems and 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":"Multi-objective optimization; Benchmark (surveying); Mathematical optimization; Convergence (economics); Selection (genetic algorithm); Pareto principle; Computer science; Pareto optimal; Optimization problem; Mathematics; Algorithm; Artificial intelligence; Economics","score_opus":0.013083393087069515,"score_gpt":0.23455646211674613,"score_spread":0.22147306902967662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119003233","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":[0.005724978,0.00028370213,0.9906227,0.00010894541,0.0000410952,0.0000786372,0.000028984237,0.00011305703,0.002998007],"genre_scores_gemma":[0.1812809,0.0005475368,0.81079113,0.00024566773,0.00007419779,0.0010720547,0.000219568,0.00012891657,0.005640005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956435,0.0001825102,0.000024761343,0.00006544333,0.0001262498,0.000036605084],"domain_scores_gemma":[0.9995621,0.0002693443,0.000054923057,0.000019595076,0.000077622964,0.000016446915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011874246,0.0015439388,0.00084612577,0.00092395756,0.00044852935,0.00068490393,0.0011013687,0.0011462474,0.0024675461],"category_scores_gemma":[0.0014147897,0.00044652304,0.0009564223,0.00069949345,0.0005474537,0.0005322754,0.0010596521,0.001041202,0.00051608286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003870669,0.000032399952,0.00034035477,0.00012002645,0.00005285474,0.00003923413,0.000044157998,0.9424607,0.0015517097,0.0090077855,0.00088091573,0.045431092],"study_design_scores_gemma":[0.000014428733,0.00003934728,0.00006727891,0.000014587542,0.000007496702,0.000012966957,0.000010200192,0.995187,0.00040642434,0.0026095933,0.0016267017,0.000004045795],"about_ca_topic_score_codex":0.002061256,"about_ca_topic_score_gemma":0.0016813284,"teacher_disagreement_score":0.0024675461,"about_ca_system_score_codex":0.0007173428,"about_ca_system_score_gemma":0.0010209525,"threshold_uncertainty_score":0.008254766},"labels":[],"label_agreement":null},{"id":"W3119964470","doi":"10.1139/cjce-2019-0680","title":"Pipe resistance coefficients identification of water networks considering solvable conditions","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"Linearization; Identification (biology); Matrix (chemical analysis); Node (physics); Process (computing); Computer science; Mathematical optimization; Applied mathematics; Mathematics; Control theory (sociology); Engineering; Structural engineering; Nonlinear system; Artificial intelligence","score_opus":0.00767343059374695,"score_gpt":0.16374960795492205,"score_spread":0.1560761773611751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119964470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03696254,0.000032759417,0.96096504,0.00003451515,0.000006106071,0.000027808122,0.00003285622,0.0001955284,0.0017428079],"genre_scores_gemma":[0.8845839,0.00013305339,0.11331992,0.00001893415,0.000012522039,0.00011017298,0.00012387826,0.000052304047,0.0016453397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999401,0.00018236587,0.00002498814,0.00015299591,0.00017685455,0.000061874445],"domain_scores_gemma":[0.99914324,0.00044274298,0.0001416741,0.000082224506,0.00017045227,0.000019742594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061942294,0.00072709605,0.00050545705,0.0006128031,0.00040966706,0.00081662176,0.0006330171,0.0005944777,0.0010734536],"category_scores_gemma":[0.0030006818,0.00040760534,0.00055429176,0.0003681033,0.0007578496,0.0016785805,0.0007067517,0.00067154557,0.00026794302],"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":[0.00003097556,0.00001836015,0.0010018194,0.00009062582,0.0000125560355,0.00016155984,0.00015807399,0.946705,0.013349532,0.011687151,0.00028785507,0.026496584],"study_design_scores_gemma":[0.000002461633,0.000008284237,0.00012918007,0.00000277493,0.0000020691364,0.000016180476,0.00001852016,0.99613816,0.0014197375,0.002088657,0.00016857035,0.00000547124],"about_ca_topic_score_codex":0.0070954375,"about_ca_topic_score_gemma":0.0048440835,"teacher_disagreement_score":0.0070954375,"about_ca_system_score_codex":0.00045225723,"about_ca_system_score_gemma":0.0010422673,"threshold_uncertainty_score":0.014108241},"labels":[],"label_agreement":null},{"id":"W3120176518","doi":"10.3390/mca26010006","title":"Prediction of Maximum Pressure at the Roofs of Rectangular Water Tanks Subjected to Harmonic Base Excitation Using the Multi-Gene Genetic Programming Method","year":2021,"lang":"en","type":"article","venue":"Mathematical and Computational Applications","topic":"Water Systems and 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":"Toronto Metropolitan University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionless quantity; Genetic programming; Genetic algorithm; Excited state; Excitation; Symbolic regression; Base (topology); Roof; Harmonic; Structural engineering; Mathematics; Mechanics; Engineering; Physics; Computer science; Acoustics; Mathematical analysis; Mathematical optimization; Atomic physics","score_opus":0.024793704992839942,"score_gpt":0.24071981569490444,"score_spread":0.2159261107020645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120176518","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":[0.7974097,0.00013640201,0.19873112,0.00018928084,0.000025002864,0.000047443435,0.00012180689,0.00025574915,0.0030834165],"genre_scores_gemma":[0.9858693,0.000054739015,0.013552431,0.00000952594,0.0000028601762,0.00003151099,0.00003601277,0.000012134882,0.000431546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999008,0.000025581165,0.0000046612095,0.000025519417,0.000024372825,0.000019119067],"domain_scores_gemma":[0.9996132,0.00027046443,0.00004903958,0.000011271748,0.000044676606,0.000011238452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003079835,0.0004458253,0.00040072284,0.0003595774,0.00024251892,0.0005160499,0.0004304196,0.00089384237,0.00055131246],"category_scores_gemma":[0.00096782146,0.00022444098,0.00037969067,0.0003134389,0.00042493118,0.00024819933,0.00028722628,0.00044509195,0.00006182316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023812034,0.000018691224,0.0013708953,0.000022657898,0.0000057871302,0.0000510721,0.000020808877,0.9912642,0.0026114173,0.00024339334,0.00004850794,0.0043186964],"study_design_scores_gemma":[0.000001412693,0.000008889403,0.0002506965,0.0000010314234,0.0000012594214,0.0000023874152,0.0000057555176,0.9990429,0.00063516526,0.0000349474,0.000013909152,0.0000016385368],"about_ca_topic_score_codex":0.009881983,"about_ca_topic_score_gemma":0.004800504,"teacher_disagreement_score":0.009881983,"about_ca_system_score_codex":0.00038841343,"about_ca_system_score_gemma":0.00066793576,"threshold_uncertainty_score":0.01964891},"labels":[],"label_agreement":null},{"id":"W3124705364","doi":"10.1002/prs.12230","title":"Undesired effects of relief systems concerns mitigations","year":2021,"lang":"en","type":"article","venue":"Process Safety Progress","topic":"Water Systems and 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":"Smiths Detection (Canada)","funders":"","keywords":"Piping; Upstream (networking); Relief valve; Documentation; Downstream (manufacturing); Process (computing); Computer science; Forensic engineering; Risk analysis (engineering); Engineering; Operations management; Environmental science; Business; Mechanical engineering; Telecommunications","score_opus":0.007739683930128041,"score_gpt":0.22327369790962817,"score_spread":0.21553401397950012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124705364","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":[0.61415786,0.0021177372,0.23556599,0.005212075,0.0011503198,0.0007453376,0.00093378365,0.0049624625,0.13515452],"genre_scores_gemma":[0.9771224,0.00025574066,0.012910918,0.0007434604,0.00007249856,0.0000616716,0.00015707222,0.000272657,0.008403574],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99437994,0.0016028149,0.00031449317,0.0004777399,0.002618674,0.000606339],"domain_scores_gemma":[0.9791222,0.009820075,0.0023013386,0.0031719983,0.0052921246,0.00029228008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032827007,0.0008802221,0.00040695115,0.00090517267,0.00086911686,0.0011884092,0.0010526377,0.0011392311,0.009518439],"category_scores_gemma":[0.014370281,0.000334697,0.00087049184,0.00044451636,0.001133467,0.0013131722,0.0013466515,0.0013778782,0.0007987246],"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":[0.0020434645,0.0007527507,0.045117866,0.0036167302,0.0006215135,0.006653004,0.0025583275,0.24730384,0.25553584,0.038835507,0.023370389,0.37359074],"study_design_scores_gemma":[0.00022420601,0.0033157773,0.09271814,0.001037351,0.0009983499,0.004467848,0.004493978,0.13366106,0.50439894,0.047368053,0.20685732,0.00045897247],"about_ca_topic_score_codex":0.0021229258,"about_ca_topic_score_gemma":0.0018249376,"teacher_disagreement_score":0.009518439,"about_ca_system_score_codex":0.0010077318,"about_ca_system_score_gemma":0.0006881993,"threshold_uncertainty_score":0.03184235},"labels":[],"label_agreement":null},{"id":"W3125966219","doi":"10.1115/ipc2020-9767","title":"What Is the Leak Rate for a Liquid Slug Flowing Past a Side Branch?","year":2020,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Nova Chemicals (Canada)","funders":"","keywords":"Leak; Pipeline transport; Volumetric flow rate; Petroleum engineering; Volume (thermodynamics); Debris; Pipeline (software); Flow (mathematics); Gas leak; Environmental science; Mechanics; Materials science; Chemistry; Environmental engineering; Engineering; Geology; Mechanical engineering; Thermodynamics; Physics","score_opus":0.014758685808517963,"score_gpt":0.19673713175454743,"score_spread":0.18197844594602947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125966219","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":[0.9803239,0.0013051869,0.016217649,0.00015314198,0.00003085308,0.0000538783,0.0003083374,0.00031560424,0.001291431],"genre_scores_gemma":[0.997494,0.00030431664,0.0016170352,0.0000147917635,0.0000068660934,0.000011241739,0.00013221805,0.000025835874,0.00039366874],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911445,0.0001982148,0.00009121589,0.00014322519,0.0003210297,0.00013189454],"domain_scores_gemma":[0.9898846,0.0050643478,0.002805431,0.0004285035,0.0015877509,0.00022941061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015686123,0.0003863027,0.00076613907,0.0014178015,0.00023910915,0.0009009869,0.0004276712,0.00079681264,0.001674983],"category_scores_gemma":[0.007739027,0.00025483264,0.00059025537,0.0006900688,0.0005468138,0.0015716343,0.00035591095,0.00050779554,0.00059287663],"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":[0.0064370576,0.0007095889,0.23052411,0.001691079,0.00028396482,0.0012486335,0.00056068844,0.104152374,0.46004987,0.0013487248,0.0018529879,0.19114092],"study_design_scores_gemma":[0.00005780068,0.0061582723,0.11925119,0.0002136484,0.00021362293,0.0011758963,0.0011773601,0.3109085,0.5566532,0.0011445491,0.0028399732,0.00020603543],"about_ca_topic_score_codex":0.0016542329,"about_ca_topic_score_gemma":0.0008499576,"teacher_disagreement_score":0.001674983,"about_ca_system_score_codex":0.0006207967,"about_ca_system_score_gemma":0.00022244733,"threshold_uncertainty_score":0.008295715},"labels":[],"label_agreement":null},{"id":"W3126478590","doi":"10.1115/1.4049194","title":"Operational Stability of a Hydropower Plant With a Pipe-Shaped Air-Cushion Surge Chamber","year":2020,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Water Systems and 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":"National Natural Science Foundation of China","keywords":"Hydropower; Volume (thermodynamics); Surge; Environmental science; Marine engineering; Engineering; Electrical engineering; Physics","score_opus":0.009571553968527735,"score_gpt":0.1882545021262362,"score_spread":0.17868294815770847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126478590","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":[0.84692436,0.00020434996,0.14690736,0.00012829571,0.000029413526,0.00011619105,0.00012807909,0.00040594174,0.005156112],"genre_scores_gemma":[0.99832386,0.000024566956,0.0013523947,0.0000026708985,0.0000016585661,0.000011574435,0.000011419402,0.000003865188,0.00026785204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997242,0.000055301483,0.000009768312,0.00005909695,0.000099496734,0.000052039497],"domain_scores_gemma":[0.9995865,0.00016747744,0.00008618797,0.000023348588,0.00010061069,0.000035978654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033595462,0.00042546194,0.00035114825,0.0002966164,0.00035915463,0.0005291515,0.0004591794,0.00043656217,0.0011367812],"category_scores_gemma":[0.0006434096,0.00025255128,0.00037001056,0.0001299017,0.000502307,0.00041622997,0.00036249173,0.00030221354,0.00014825519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053742627,0.0000892341,0.0057835863,0.00032315293,0.00004658066,0.00056273874,0.00013071172,0.77257663,0.20163354,0.00183186,0.00044389776,0.016040565],"study_design_scores_gemma":[0.000021945321,0.00038342117,0.0026092015,0.000008795462,0.000025442578,0.00006352549,0.00005083299,0.9745636,0.02160579,0.00018203873,0.00046795642,0.000017452974],"about_ca_topic_score_codex":0.0035448675,"about_ca_topic_score_gemma":0.001653144,"teacher_disagreement_score":0.0035448675,"about_ca_system_score_codex":0.0005467667,"about_ca_system_score_gemma":0.0008139101,"threshold_uncertainty_score":0.0070484877},"labels":[],"label_agreement":null},{"id":"W3128744684","doi":"10.2166/hydro.2021.093","title":"Ensemble-based machine learning approach for improved leak detection in water mains","year":2021,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":84,"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":"FedDev Ontario","keywords":"False positive paradox; Gradient boosting; Ensemble learning; Boosting (machine learning); Computer science; Artificial intelligence; Leak; Classifier (UML); Binary classification; Decision tree; Machine learning; Pattern recognition (psychology); Leak detection; Support vector machine; Random forest; Engineering","score_opus":0.0075888272262635255,"score_gpt":0.1823657255013359,"score_spread":0.17477689827507237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128744684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09044963,0.0004886856,0.90562755,0.0002164426,0.00007624932,0.000046921712,0.00011162228,0.001543597,0.0014392626],"genre_scores_gemma":[0.8387553,0.0001708885,0.15899156,0.0001017295,0.00007645814,0.000063578176,0.0003039087,0.000058302237,0.0014782997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931073,0.00020948455,0.000045224006,0.00014200574,0.00020855087,0.000083953426],"domain_scores_gemma":[0.9986926,0.0004888838,0.0001134882,0.000117513904,0.00053752813,0.00005002525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001888139,0.000787973,0.001363376,0.0012850772,0.0004342261,0.0006639463,0.00083637284,0.00069824007,0.0008896967],"category_scores_gemma":[0.0025339178,0.00031874317,0.0007814115,0.00085828087,0.00020662708,0.000825672,0.00075174606,0.00097171677,0.00034442637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018468517,0.00025744535,0.0066041565,0.00005757553,0.00020618827,0.000100298465,0.00006858743,0.59635174,0.010323055,0.0012744499,0.0020023657,0.38256946],"study_design_scores_gemma":[0.0000016987888,0.000020126989,0.00040757464,0.0000020643986,0.000008618774,0.0000075553116,0.0000043689647,0.99823344,0.0008796696,0.00027286494,0.0001591284,0.0000027693393],"about_ca_topic_score_codex":0.0034474898,"about_ca_topic_score_gemma":0.003919428,"teacher_disagreement_score":0.0034474898,"about_ca_system_score_codex":0.0004339282,"about_ca_system_score_gemma":0.00053745694,"threshold_uncertainty_score":0.009985566},"labels":[],"label_agreement":null},{"id":"W3131967682","doi":"10.1029/2020wr028714","title":"Discharge Estimation From Dense Arrays of Pressure Transducers","year":2021,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water Systems and 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 Saskatchewan; University of Victoria; Environment and Climate Change Canada","funders":"Nuclear Safety and Security Commission","keywords":"Rating curve; Sensitivity (control systems); Environmental science; Computer science; Marine engineering; Geology; Engineering; Electronic engineering","score_opus":0.026748501995451472,"score_gpt":0.26370964099725713,"score_spread":0.23696113900180565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131967682","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":[0.71182996,0.00011763229,0.28379577,0.00016654565,0.00003472498,0.000041597476,0.0007950421,0.0013558673,0.0018629144],"genre_scores_gemma":[0.9712596,0.000039169034,0.027927285,0.000016464322,0.000012836131,0.000021854576,0.0004019883,0.00002186304,0.00029889887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996855,0.000085691616,0.00002074572,0.00010296144,0.00008054575,0.000024517882],"domain_scores_gemma":[0.99901426,0.0004181717,0.00019269985,0.00014116694,0.00019126442,0.000042488755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059784146,0.00053606636,0.0004706853,0.0006566779,0.00024319298,0.0006269311,0.0006363965,0.00046790327,0.00071985606],"category_scores_gemma":[0.0028452221,0.00043002982,0.00048556688,0.00089631864,0.00024811627,0.00086250337,0.00068685215,0.0006387831,0.00019686866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014203282,0.00009399801,0.06452205,0.00007004545,0.000139319,0.000129954,0.00007708204,0.84009516,0.011638476,0.000769276,0.00088206516,0.081440546],"study_design_scores_gemma":[0.000011198648,0.000025962012,0.0120637035,0.0000029119822,0.000010399418,0.000016041613,0.000013520998,0.9855053,0.0016542735,0.00053192995,0.0001543682,0.000010482561],"about_ca_topic_score_codex":0.011760207,"about_ca_topic_score_gemma":0.00985455,"teacher_disagreement_score":0.011760207,"about_ca_system_score_codex":0.0004901122,"about_ca_system_score_gemma":0.0005758096,"threshold_uncertainty_score":0.023383498},"labels":[],"label_agreement":null},{"id":"W3132069837","doi":"10.2166/wst.2021.061","title":"Numerical study on effects of chamber design and multi-inlet on storm geyser","year":2021,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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 Alberta","funders":"Key Research and Development Program of Zhejiang Province; Fundamental Research Funds for the Provincial Universities of Zhejiang; Natural Science Foundation of Ningbo; National Natural Science Foundation of China","keywords":"Inlet; Inflow; Pressure drop; Environmental science; Body orifice; Storm; Computational fluid dynamics; Marine engineering; Geology; Mechanics; Engineering; Geomorphology; Mechanical engineering","score_opus":0.011679811340906843,"score_gpt":0.2220136903800707,"score_spread":0.21033387903916387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132069837","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":[0.9733744,0.00023907068,0.020797785,0.00015362883,0.000058931473,0.0000449947,0.0002608566,0.00024485192,0.0048254337],"genre_scores_gemma":[0.9945754,0.00008037856,0.004637286,0.000011864581,0.0000042345155,0.00001681104,0.000074886484,0.000022559552,0.00057654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964345,0.00007497629,0.000021138432,0.00006421311,0.00008452068,0.00011176047],"domain_scores_gemma":[0.9991449,0.0004579573,0.00013057228,0.0000638773,0.00013731979,0.00006536728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006111006,0.00066048646,0.0007135855,0.00044519082,0.00083000114,0.00082504563,0.00060241617,0.0009413133,0.0019724465],"category_scores_gemma":[0.001411084,0.00033999584,0.000667471,0.00039068618,0.0005239115,0.00064362,0.00059375487,0.00056640554,0.00015348394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022040104,0.00012366893,0.008310281,0.00013314626,0.000025014982,0.0002581713,0.000083980856,0.96428174,0.019835988,0.0008978022,0.00039793822,0.005431916],"study_design_scores_gemma":[0.000016772658,0.00010423596,0.0015461603,0.0000060253697,0.000014306301,0.000020940664,0.000041143892,0.9923954,0.0055694347,0.00005397731,0.00022001844,0.000011601119],"about_ca_topic_score_codex":0.013193264,"about_ca_topic_score_gemma":0.00815144,"teacher_disagreement_score":0.013193264,"about_ca_system_score_codex":0.0006639783,"about_ca_system_score_gemma":0.00092179276,"threshold_uncertainty_score":0.026232898},"labels":[],"label_agreement":null},{"id":"W3134038900","doi":"10.1109/access.2021.3064445","title":"Water Leak Detection Survey: Challenges &amp; Research Opportunities Using Data Fusion &amp; Federated Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Water Systems and Optimization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Leak detection; Leak; Process (computing); Sensor fusion; Pipeline (software); Systems engineering; Risk analysis (engineering); Artificial intelligence; Engineering; Environmental engineering","score_opus":0.6035264437768418,"score_gpt":0.40477153454812653,"score_spread":0.1987549092287153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134038900","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015857536,0.5419232,0.39901218,0.020762222,0.0016984333,0.000178058,0.00039654577,0.0006624901,0.019509355],"genre_scores_gemma":[0.32379737,0.506336,0.15394688,0.004250524,0.0033079826,0.00019456298,0.0012291542,0.00015478236,0.0067827804],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976514,0.000624318,0.00020492174,0.0005946735,0.0007783187,0.00014642108],"domain_scores_gemma":[0.9940586,0.0033494344,0.00026054576,0.00045320025,0.0017187358,0.00015950557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038260554,0.0011111383,0.0013121739,0.0027154929,0.00067646755,0.0032773179,0.0019771943,0.0021716459,0.0020711638],"category_scores_gemma":[0.0067494474,0.00053879,0.0013665591,0.004922097,0.0012425451,0.0054670186,0.0017874236,0.0022821603,0.001006626],"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":[0.0001012639,0.00018731019,0.0031073058,0.0030710534,0.00013535676,0.00009486364,0.00017978364,0.018756032,0.0016856261,0.034589846,0.012887793,0.92520356],"study_design_scores_gemma":[0.000044631506,0.00072570593,0.0070687947,0.0043076277,0.00036015472,0.0014366484,0.002581175,0.40610337,0.015575345,0.16873306,0.39275563,0.00030781556],"about_ca_topic_score_codex":0.0024234082,"about_ca_topic_score_gemma":0.0015883483,"teacher_disagreement_score":0.0038260554,"about_ca_system_score_codex":0.0012269199,"about_ca_system_score_gemma":0.0018476194,"threshold_uncertainty_score":0.020234346},"labels":[],"label_agreement":null},{"id":"W3134203175","doi":"10.1007/s10726-021-09727-0","title":"A Hybrid Fuzzy-Probabilistic Bargaining Approach for Multi-objective Optimization of Contamination Warning Sensors in Water Distribution Systems","year":2021,"lang":"en","type":"article","venue":"Group Decision and Negotiation","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Computer science; Stakeholder; Fuzzy logic; Probabilistic logic; Mathematical optimization; Operations research; Bargaining problem; Compromise; Negotiation; Engineering; Artificial intelligence; Mathematics; Economics","score_opus":0.015672703585622335,"score_gpt":0.21904281033706346,"score_spread":0.20337010675144113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134203175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00912876,0.0001904856,0.9875915,0.00018249387,0.000053088188,0.00004657636,0.000013042641,0.000029825702,0.0027641675],"genre_scores_gemma":[0.806048,0.00038838873,0.1874113,0.00018604693,0.000118632976,0.00032555833,0.000047535148,0.00005756802,0.0054169707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840975,0.00086852384,0.000053977397,0.00015927995,0.0003380471,0.00017040838],"domain_scores_gemma":[0.9978415,0.0016950631,0.00011972348,0.000043534375,0.00021760215,0.00008272158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048053316,0.0010995913,0.00219647,0.0010347663,0.0010589926,0.0020644325,0.0026084275,0.0028513165,0.0036316742],"category_scores_gemma":[0.0047969352,0.0010086587,0.0015423659,0.0011768932,0.0014984517,0.0022179806,0.0021084554,0.0017573995,0.00028171804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038886865,0.000025761028,0.00006948217,0.00003529574,0.000031494787,0.00003992184,0.000033907956,0.9823046,0.0004994144,0.010921236,0.0001738499,0.0058262977],"study_design_scores_gemma":[0.0000051913908,0.000014691746,0.000014748643,0.0000031833545,0.000004368026,0.0000041887483,0.0000062777385,0.99726355,0.00006558395,0.0025382494,0.00007621981,0.0000037617629],"about_ca_topic_score_codex":0.004124092,"about_ca_topic_score_gemma":0.0028711462,"teacher_disagreement_score":0.0048053316,"about_ca_system_score_codex":0.001482661,"about_ca_system_score_gemma":0.001577969,"threshold_uncertainty_score":0.025413275},"labels":[],"label_agreement":null},{"id":"W3137759940","doi":"10.3390/w13060785","title":"Investigation of Air Pocket Behavior in Pipelines Using Rigid Column Model and Contributions of Time Integration Schemes","year":2021,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"Dissipation; Mechanics; Attenuation; Euler's formula; Runge–Kutta methods; Backward Euler method; Euler equations; Mathematics; Applied mathematics; Control theory (sociology); Physics; Numerical analysis; Mathematical analysis; Computer science; Thermodynamics","score_opus":0.015359465248328614,"score_gpt":0.2181093504110501,"score_spread":0.20274988516272147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137759940","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":[0.779937,0.00034226166,0.21646363,0.0001232477,0.000017472623,0.00003093945,0.00005995298,0.0003453094,0.0026800875],"genre_scores_gemma":[0.9928895,0.00009683041,0.0062508695,0.000006257069,0.000002757221,0.000008113651,0.000020908237,0.00002005664,0.0007047556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990964,0.000021187743,0.0000037002949,0.000014321067,0.00003152247,0.000019721256],"domain_scores_gemma":[0.9996214,0.00022174603,0.00006526021,0.000027197135,0.00004600106,0.000018376057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002252973,0.00040019216,0.0003454417,0.00035292766,0.00024581345,0.00037080562,0.00035098376,0.00056104944,0.00070797757],"category_scores_gemma":[0.0010177711,0.0002198364,0.00030809772,0.00025507767,0.00041999147,0.00059602055,0.0003266696,0.000368134,0.000077614815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012729317,0.00006586938,0.004042149,0.000098941084,0.000013812478,0.00026551387,0.00015992047,0.93981403,0.04116469,0.0024922309,0.00013963152,0.011615932],"study_design_scores_gemma":[0.0000012409437,0.000018992105,0.00024182672,0.0000011643988,0.0000014351102,0.000007136529,0.000008352037,0.9985454,0.0010607964,0.00006890524,0.00004289757,0.0000019367976],"about_ca_topic_score_codex":0.009228682,"about_ca_topic_score_gemma":0.0042441194,"teacher_disagreement_score":0.009228682,"about_ca_system_score_codex":0.00028739884,"about_ca_system_score_gemma":0.00042646716,"threshold_uncertainty_score":0.018349946},"labels":[],"label_agreement":null},{"id":"W3144322637","doi":"10.3968/11298","title":"DEVELOPMENT OF A NEW MODEL FOR LEAK DETECTION IN PIPELINES","year":2019,"lang":"en","type":"article","venue":"Advances in petroleum exploration and development","topic":"Water Systems and 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":"Pipeline transport; Pipeline (software); Leak; Niger delta; Leak detection; Damages; ALARM; Petroleum engineering; Engineering; Product (mathematics); Forensic engineering; Environmental engineering; Delta; Mechanical engineering; Electrical engineering","score_opus":0.020927311403221106,"score_gpt":0.23473743607212197,"score_spread":0.21381012466890087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144322637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011633914,0.0003744433,0.9777353,0.00038593687,0.00008655242,0.000060183793,0.0002509371,0.000279789,0.009192955],"genre_scores_gemma":[0.8391396,0.0012553555,0.123557575,0.00025936303,0.00010788562,0.00059872185,0.0006522047,0.00021719838,0.034212187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958795,0.000089176225,0.00002720676,0.00011821053,0.00011363448,0.00006382578],"domain_scores_gemma":[0.99912196,0.00044737768,0.00009819335,0.000030413195,0.0002738603,0.00002833951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072726025,0.0008994824,0.0010712463,0.0007920925,0.0005621411,0.0015093883,0.0015685218,0.001972529,0.0036985101],"category_scores_gemma":[0.002287998,0.0006111128,0.0014561192,0.0005976255,0.00072471495,0.001590891,0.0010908564,0.0019149636,0.00058983226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014295721,0.000016650485,0.00037668642,0.000057714642,0.000011956466,0.00007553541,0.00003266474,0.9861481,0.0012977371,0.007437953,0.00038945925,0.004141242],"study_design_scores_gemma":[0.0000020819032,0.000006737806,0.0000416258,0.00000385737,0.0000030957233,0.00000946354,0.000004630402,0.9983151,0.00017984772,0.0009966639,0.00043324044,0.0000036610675],"about_ca_topic_score_codex":0.015459408,"about_ca_topic_score_gemma":0.006728539,"teacher_disagreement_score":0.015459408,"about_ca_system_score_codex":0.0011954653,"about_ca_system_score_gemma":0.0018550529,"threshold_uncertainty_score":0.03073889},"labels":[],"label_agreement":null},{"id":"W3145450798","doi":"10.1002/9781119300762.wsts0194","title":"Water Distribution System","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Water supply; Key (lock); Population; Distribution (mathematics); Computer science; Environmental science; Engineering; Environmental engineering; Computer security","score_opus":0.003142291701345112,"score_gpt":0.16153485420662986,"score_spread":0.15839256250528475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145450798","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":[0.027519286,0.0030568587,0.16401248,0.004676146,0.0017955482,0.0014931085,0.02156177,0.017664267,0.75822055],"genre_scores_gemma":[0.44165218,0.0034593302,0.035848465,0.001334365,0.0004565754,0.0010133157,0.023495756,0.0013564262,0.4913836],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999199,0.00008515358,0.0000579753,0.00023054438,0.00030797484,0.00011934712],"domain_scores_gemma":[0.9994566,0.000040797422,0.000034201134,0.00007066784,0.00035994552,0.000037769754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037535824,0.0007330397,0.00049912813,0.001425011,0.001396254,0.002504249,0.0013963212,0.00080980104,0.11762684],"category_scores_gemma":[0.0007951285,0.00025005013,0.00044944373,0.0026758856,0.0003798306,0.0022791233,0.0018816484,0.0006825579,0.044149987],"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":[0.0003555908,0.00019855793,0.004370612,0.0010041313,0.0000529005,0.00042112195,0.00017275708,0.027500337,0.012274072,0.046870176,0.381185,0.52559465],"study_design_scores_gemma":[0.00007559273,0.000095905554,0.0014760455,0.00013087848,0.00002627767,0.00019123274,0.0001665798,0.035233412,0.008194413,0.00977554,0.9445886,0.000045581055],"about_ca_topic_score_codex":0.010266223,"about_ca_topic_score_gemma":0.005106604,"teacher_disagreement_score":0.11762684,"about_ca_system_score_codex":0.002103411,"about_ca_system_score_gemma":0.002492334,"threshold_uncertainty_score":0.39350098},"labels":[],"label_agreement":null},{"id":"W3150561630","doi":"10.1016/j.psep.2021.03.045","title":"A programmable logic controller based remote pipeline monitoring system","year":2021,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Water Systems and 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":"University of Alberta","funders":"Higher Education Commission, Pakistan","keywords":"SCADA; Programmable logic controller; Pipeline transport; Pipeline (software); Reliability (semiconductor); Sensitivity (control systems); Leak; Real-time computing; Reliability engineering; Computer science; Controller (irrigation); ALARM; Embedded system; False alarm; Engineering; Automotive engineering; Electronic engineering; Artificial intelligence; Electrical engineering","score_opus":0.008134617152855947,"score_gpt":0.17586378883891463,"score_spread":0.16772917168605866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150561630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16000248,0.00090443867,0.72809565,0.00056035013,0.0007345886,0.0007114992,0.0010703878,0.059541944,0.04837869],"genre_scores_gemma":[0.88710654,0.00017859644,0.08617146,0.0006705704,0.00014503182,0.00021725436,0.0005271963,0.0003179152,0.024665538],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994106,0.000045121942,0.000023134071,0.00017397494,0.00029926279,0.000047835674],"domain_scores_gemma":[0.99970156,0.00006040155,0.00004180599,0.00004919536,0.000115160095,0.000031882304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002358611,0.000510857,0.00060395483,0.000613179,0.0005121019,0.0006800772,0.0017660065,0.00049499236,0.010744697],"category_scores_gemma":[0.0004035402,0.0002680452,0.00022734576,0.00037361623,0.00021084546,0.0005812138,0.00043869083,0.00056203024,0.0017523668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014226802,0.000567983,0.002966642,0.00053148594,0.00009876893,0.0005208543,0.0001772474,0.014259497,0.4743626,0.0037149463,0.024702955,0.47667438],"study_design_scores_gemma":[0.0006975771,0.0027764335,0.010181607,0.00007733268,0.00029653584,0.0015155033,0.000082642335,0.4861134,0.41819617,0.0023640513,0.07745285,0.00024583965],"about_ca_topic_score_codex":0.0019523844,"about_ca_topic_score_gemma":0.0022548886,"teacher_disagreement_score":0.010744697,"about_ca_system_score_codex":0.00043287093,"about_ca_system_score_gemma":0.0006689079,"threshold_uncertainty_score":0.03594458},"labels":[],"label_agreement":null},{"id":"W3152237627","doi":"","title":"Une méthode hybride pour l'identification des vannes partiellement ou complètement fermées dans un réseau d'alimentation en eau potable","year":2018,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Water Systems and 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 Saskatchewan","funders":"","keywords":"Reliability (semiconductor); Safety valve; Calibration; Pipeline transport; Identification (biology); Genetic algorithm; Computer science; Isolation (microbiology); Sensitivity (control systems); Pressure regulator; Engineering; Reliability engineering; Control engineering; Mechanical engineering; Mathematics","score_opus":0.024177664468862107,"score_gpt":0.23571977719667772,"score_spread":0.21154211272781562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152237627","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":[0.0048155705,0.00010891984,0.9939148,0.00006523039,0.00005562747,0.000024532232,0.000035601035,0.00021475702,0.00076507364],"genre_scores_gemma":[0.16493003,0.00028414943,0.8166707,0.00014917429,0.000115119146,0.00037282813,0.000286453,0.0002675927,0.016923986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921775,0.00020133168,0.000040305098,0.00025689663,0.00021676399,0.000066992485],"domain_scores_gemma":[0.99835736,0.0011674426,0.00006561863,0.000105865285,0.00025888375,0.000044917677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014177226,0.0015100511,0.0015033145,0.00079980347,0.0006195289,0.00211025,0.0014074442,0.0038939368,0.004193344],"category_scores_gemma":[0.003117909,0.0012543931,0.0017998153,0.00072641,0.0011873721,0.0015649347,0.0013478012,0.0023871912,0.0010114681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020597871,0.00007562712,0.00061673776,0.00016068909,0.00014538926,0.00013640862,0.00015370948,0.86361486,0.011954594,0.00515275,0.0011444403,0.116638854],"study_design_scores_gemma":[0.000010651194,0.00001960731,0.00010880248,0.000008070047,0.0000065472955,0.000016817628,0.00001217485,0.9971727,0.0011823577,0.0005333811,0.00091976795,0.000009219343],"about_ca_topic_score_codex":0.0142817935,"about_ca_topic_score_gemma":0.011029413,"teacher_disagreement_score":0.0142817935,"about_ca_system_score_codex":0.00074869907,"about_ca_system_score_gemma":0.0011509955,"threshold_uncertainty_score":0.028397322},"labels":[],"label_agreement":null},{"id":"W3152764743","doi":"10.2166/wst.2021.139","title":"Uncertainty analysis of rising sewer models with respect to input parameters and model structure using Monte Carlo simulations and computational fluid dynamics","year":2021,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sanitary sewer; Sensitivity (control systems); Computational fluid dynamics; Monte Carlo method; Computer science; Mathematics; Mechanics; Environmental science; Engineering; Environmental engineering; Physics","score_opus":0.011512797657533328,"score_gpt":0.2234506431513304,"score_spread":0.21193784549379707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152764743","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":[0.5735995,0.00036334145,0.41918653,0.00026109949,0.000025016081,0.000115788964,0.00043216965,0.00031057146,0.0057060025],"genre_scores_gemma":[0.9836675,0.00011397254,0.015380463,0.000017246182,0.000004900928,0.00007310601,0.00012490794,0.00003169337,0.00058623374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995608,0.00018561484,0.000025626394,0.000056863697,0.00012422708,0.000046775778],"domain_scores_gemma":[0.99747795,0.0019925705,0.00020735917,0.00010802043,0.00018868028,0.00002534344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001201968,0.0006922916,0.00069237174,0.0007346023,0.00044706973,0.0009843174,0.00056450657,0.0009790768,0.0006692267],"category_scores_gemma":[0.003776513,0.0005806437,0.00091560977,0.00042604844,0.00055698585,0.0007553912,0.0004861375,0.0008568154,0.00007297616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005958257,0.0000044420453,0.00029372398,0.0000061962273,0.0000058279547,0.000005933613,0.0000056731574,0.9983693,0.00019543408,0.00044645337,0.000010509274,0.0006507043],"study_design_scores_gemma":[0.0000012266545,0.0000067849915,0.00014426037,0.0000022170786,0.0000027014944,0.0000021426536,0.0000036174683,0.9990539,0.0004063883,0.00032452517,0.000049595983,0.0000026948453],"about_ca_topic_score_codex":0.017457306,"about_ca_topic_score_gemma":0.009698703,"teacher_disagreement_score":0.017457306,"about_ca_system_score_codex":0.0012547857,"about_ca_system_score_gemma":0.0009853297,"threshold_uncertainty_score":0.03471136},"labels":[],"label_agreement":null},{"id":"W3157135542","doi":"10.24908/iqurcp.7734","title":"Impact of Climate Change on Hydraulic Performance in Water Distribution Networks","year":2017,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Water Systems and 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":true,"ca_institutions":"","funders":"","keywords":"Climate change; Environmental science; Latitude; Climatology; Hydrology (agriculture); Engineering; Geography; Geology","score_opus":0.0865629345492622,"score_gpt":0.34103620242542215,"score_spread":0.25447326787616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157135542","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":[0.9843975,0.00016863947,0.003001867,0.00049864163,0.000021179489,0.000015515028,0.0004571095,0.0000702833,0.011369392],"genre_scores_gemma":[0.9990428,0.000081138365,0.00022447846,0.000014230539,0.0000033977408,0.0000027927365,0.00008861981,0.0000072602256,0.0005352724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993205,0.0002287419,0.00002127351,0.000077296194,0.00018326771,0.00016887406],"domain_scores_gemma":[0.9979705,0.000976274,0.00026413248,0.00014108644,0.0005124572,0.00013554274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008183971,0.00024425655,0.00019533747,0.00033124324,0.0004849371,0.0010250679,0.00031264234,0.00033151117,0.0022291404],"category_scores_gemma":[0.0034420092,0.00012712981,0.00022835989,0.0008910329,0.0005687081,0.0010903686,0.00069150067,0.0003622192,0.0002650354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034029892,0.00015183866,0.25311032,0.00011657917,0.00010674059,0.0005206814,0.000570866,0.67059153,0.011757875,0.005101199,0.0027724279,0.054859653],"study_design_scores_gemma":[0.00002901265,0.00060164277,0.5295803,0.00005497155,0.00010297636,0.0002456044,0.0033080098,0.43242356,0.016687034,0.007816126,0.009043669,0.00010713363],"about_ca_topic_score_codex":0.04316522,"about_ca_topic_score_gemma":0.05867475,"teacher_disagreement_score":0.04316522,"about_ca_system_score_codex":0.0025394992,"about_ca_system_score_gemma":0.0008047409,"threshold_uncertainty_score":0.08582789},"labels":[],"label_agreement":null},{"id":"W3158889732","doi":"10.3390/fluids6050170","title":"An Analytical Solution for Unsteady Laminar Flow in Tubes with a Tapered Wall Thickness","year":2021,"lang":"en","type":"article","venue":"Fluids","topic":"Water Systems and 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 Saskatchewan","funders":"","keywords":"Water hammer; Laminar flow; Mechanics; Pipeline (software); Transient (computer programming); Tube (container); Flow (mathematics); Pipe flow; Computational fluid dynamics; Frequency domain; Computer science; Physics; Mechanical engineering; Engineering","score_opus":0.012872992892051153,"score_gpt":0.22229941311358953,"score_spread":0.20942642022153837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158889732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03118384,0.0010237832,0.945509,0.0008573271,0.00023345038,0.00015592107,0.00019624308,0.00034148231,0.020499025],"genre_scores_gemma":[0.47005773,0.002553331,0.4861437,0.00044049515,0.00028205253,0.00058273814,0.00028030394,0.00039329502,0.039266333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998062,0.000036006306,0.000007857433,0.000024559691,0.0001023997,0.000022947515],"domain_scores_gemma":[0.999363,0.00028625657,0.00007486609,0.0000344744,0.00022149853,0.000019958074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005286081,0.0005967788,0.0004901743,0.0006557276,0.00046946044,0.00083264423,0.0006462833,0.0018503817,0.0017411015],"category_scores_gemma":[0.0027409485,0.0003890297,0.0006682612,0.0005206895,0.00075952,0.0007905757,0.00048303528,0.00094730087,0.0006270691],"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":[0.000037703416,0.000054596876,0.00082927826,0.00035070913,0.000013281369,0.00041346694,0.0002560509,0.8808478,0.019945983,0.056251477,0.003991409,0.037008297],"study_design_scores_gemma":[0.000006117197,0.000018803788,0.00009666515,0.000024856983,0.000002975949,0.000060886225,0.000032838547,0.9916101,0.0014877007,0.0029409712,0.0037103281,0.000007595736],"about_ca_topic_score_codex":0.0028979187,"about_ca_topic_score_gemma":0.002888684,"teacher_disagreement_score":0.0028979187,"about_ca_system_score_codex":0.00067423517,"about_ca_system_score_gemma":0.0019895553,"threshold_uncertainty_score":0.0058246255},"labels":[],"label_agreement":null},{"id":"W3158961531","doi":"10.1061/(asce)hy.1943-7900.0001895","title":"Godunov-Type Solutions for Transient Pipe Flow Implicitly Incorporating Brunone Unsteady Friction","year":2021,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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":"Degree Rankine; Mechanics; Discretization; Transient (computer programming); Riemann solver; Courant–Friedrichs–Lewy condition; Flow (mathematics); Boundary value problem; Dissipation; Momentum (technical analysis); Boundary (topology); Type (biology); Pipe flow; Roe solver; Mathematics; Applied mathematics; Physics; Mathematical analysis; Geology; Computer science; Turbulence; Thermodynamics; Finite volume method","score_opus":0.01308922254848597,"score_gpt":0.19963813002692612,"score_spread":0.18654890747844016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158961531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07122974,0.0002861682,0.9210209,0.0001320302,0.000107935266,0.00007684811,0.000092867296,0.0005853317,0.00646816],"genre_scores_gemma":[0.8454514,0.00023470161,0.14944518,0.0000575432,0.000029740746,0.000107559514,0.00018938952,0.0001502474,0.0043341443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980503,0.000042114374,0.000011099379,0.000018329336,0.00008989088,0.00003365158],"domain_scores_gemma":[0.99967885,0.00009461284,0.00004548778,0.000054643675,0.00008849021,0.000037797538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004730034,0.0004964938,0.00087692874,0.0005588945,0.00045105687,0.0006678222,0.0010983386,0.00091032713,0.0011100332],"category_scores_gemma":[0.0013102732,0.0002997212,0.0006453894,0.00045896802,0.0008126744,0.0006601819,0.0009445777,0.00078270707,0.00024795288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009395184,0.000049633192,0.0018399836,0.000191275,0.000020804318,0.00024452084,0.00019678836,0.90598536,0.023519814,0.04275871,0.0011095214,0.023989545],"study_design_scores_gemma":[0.0000049347263,0.000010876196,0.00006994693,0.000004527841,0.0000012544149,0.000017891996,0.000005098504,0.9968856,0.0012628888,0.0010076812,0.0007244891,0.0000046870996],"about_ca_topic_score_codex":0.005441063,"about_ca_topic_score_gemma":0.003114459,"teacher_disagreement_score":0.005441063,"about_ca_system_score_codex":0.0007210136,"about_ca_system_score_gemma":0.0015300697,"threshold_uncertainty_score":0.0108187795},"labels":[],"label_agreement":null},{"id":"W3159982739","doi":"10.1007/s10661-021-09068-0","title":"Real-time contamination zoning in water distribution networks for contamination emergencies: a case study","year":2021,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Systems and 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":"Toronto Metropolitan University","funders":"","keywords":"Contamination; Environmental science; Zoning; Water contamination; Environmental engineering; Computer science; Civil engineering; Engineering","score_opus":0.008077867170892334,"score_gpt":0.2343070681673926,"score_spread":0.22622920099650026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159982739","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":[0.98604244,0.000118433454,0.010984566,0.00018717398,0.000016323042,0.00010676463,0.000295239,0.000162116,0.0020869889],"genre_scores_gemma":[0.99430275,0.00005074358,0.0047961203,0.000008210283,0.0000040070395,0.000019327823,0.0001263965,0.00001210522,0.0006802236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991627,0.00032813588,0.000035798843,0.00014303009,0.00015879619,0.00017155508],"domain_scores_gemma":[0.99693215,0.0019328989,0.00034218054,0.00020498717,0.00034091235,0.00024686917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014366638,0.000543921,0.00039094174,0.00079423375,0.00082855386,0.0011207248,0.0009021084,0.0015308217,0.0014734574],"category_scores_gemma":[0.0030923046,0.0002818467,0.00051476056,0.0012376255,0.0007571598,0.0009935697,0.0006082875,0.00066016364,0.00013092639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001122627,0.0008915244,0.047168165,0.0002365931,0.00013344787,0.0034690031,0.0008826997,0.89419657,0.01224325,0.0042953105,0.0020707203,0.03329013],"study_design_scores_gemma":[0.000116591444,0.0005742582,0.031331975,0.000019664583,0.00006947971,0.00043970824,0.0020261416,0.95631164,0.004787708,0.0020192782,0.0022526416,0.000050908802],"about_ca_topic_score_codex":0.028309207,"about_ca_topic_score_gemma":0.031488393,"teacher_disagreement_score":0.028309207,"about_ca_system_score_codex":0.0017489884,"about_ca_system_score_gemma":0.0009291534,"threshold_uncertainty_score":0.05628884},"labels":[],"label_agreement":null},{"id":"W3159990944","doi":"10.1007/978-981-19-1065-4_42","title":"Comparison of Machine Learning Classifiers for Predicting Water Main Failure","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Decision tree; Machine learning; Reliability (semiconductor); Computer science; Artificial neural network; Random forest; Artificial intelligence; Logistic regression; Predictive modelling; Data mining","score_opus":0.011137724978862083,"score_gpt":0.20515948375010518,"score_spread":0.19402175877124309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159990944","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":[0.70326513,0.05525663,0.20317224,0.001811834,0.0030516745,0.0005740333,0.0066300933,0.0042727813,0.021965446],"genre_scores_gemma":[0.9020184,0.0068794554,0.07004193,0.000347731,0.0007841865,0.0003326418,0.012084471,0.0003010842,0.0072099958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723744,0.0009283867,0.00035584,0.00045935667,0.00079459726,0.0002243773],"domain_scores_gemma":[0.9762906,0.019489089,0.00039929288,0.00061538053,0.002935339,0.00027029042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007444119,0.0022949807,0.0031856694,0.0046452023,0.00067603355,0.0020823334,0.0022575133,0.0024086575,0.003377619],"category_scores_gemma":[0.01218614,0.0004290886,0.0013406198,0.0027600192,0.00039187382,0.0021472566,0.000917713,0.0015177304,0.0015670223],"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":[0.0059826807,0.0011797157,0.01836292,0.00088478724,0.0012284395,0.00013708383,0.00010462177,0.23813966,0.0022513014,0.0014312593,0.016378772,0.7139188],"study_design_scores_gemma":[0.00015511375,0.0008928139,0.008628673,0.000107750355,0.00036180296,0.00007151361,0.00012886144,0.9839512,0.0024822806,0.0016299365,0.0015456709,0.000044418466],"about_ca_topic_score_codex":0.008263322,"about_ca_topic_score_gemma":0.0054620486,"teacher_disagreement_score":0.008263322,"about_ca_system_score_codex":0.0014082849,"about_ca_system_score_gemma":0.0009637521,"threshold_uncertainty_score":0.03936869},"labels":[],"label_agreement":null},{"id":"W3161365708","doi":"10.3233/ajw210025","title":"Design of Collection System Parameters Using Known Reference Pipe Method (KRPM)","year":2021,"lang":"en","type":"article","venue":"Asian Journal of Water Environment and Pollution","topic":"Water Systems and 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":"Drainage; Coursework; Computation; Work (physics); RADIUS; Flow (mathematics); Water flow; Mathematics; Geotechnical engineering; Engineering; Computer science; Mechanical engineering; Geometry; Algorithm","score_opus":0.02225462403791725,"score_gpt":0.20311472629676489,"score_spread":0.18086010225884763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161365708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009567614,0.000116487434,0.98770964,0.00004142202,0.0000207126,0.00007348242,0.00003741522,0.0003571007,0.0020760687],"genre_scores_gemma":[0.46414036,0.00029215802,0.53227305,0.00003495148,0.000024228015,0.00047150496,0.00013620406,0.00013344765,0.0024940495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948835,0.00015381337,0.00002928494,0.00013180517,0.00015429083,0.000042418735],"domain_scores_gemma":[0.9995943,0.00010681505,0.000079101395,0.00004292113,0.00016142041,0.000015417092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062312995,0.0007064096,0.000664126,0.00045469956,0.0005626284,0.00076575624,0.0010260288,0.0006542625,0.0028352423],"category_scores_gemma":[0.0012474773,0.0004124956,0.0005636195,0.00055468053,0.0004427244,0.0009285812,0.00054931606,0.0005439371,0.00056264503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014823572,0.0001027559,0.0019467099,0.00068054145,0.00004849024,0.00017578666,0.00033346185,0.72777355,0.052064825,0.012891141,0.0029701141,0.20086445],"study_design_scores_gemma":[0.000021036216,0.0001427914,0.00065591896,0.000023743047,0.000024309482,0.00006399194,0.000053262786,0.98240614,0.009918955,0.0012734841,0.005384529,0.000031766645],"about_ca_topic_score_codex":0.00341929,"about_ca_topic_score_gemma":0.003297084,"teacher_disagreement_score":0.00341929,"about_ca_system_score_codex":0.0004989761,"about_ca_system_score_gemma":0.0015266729,"threshold_uncertainty_score":0.0094848275},"labels":[],"label_agreement":null},{"id":"W3163587973","doi":"","title":"Preparing for the Replacement Era: Understanding North America's Aging Water Distribution Systems","year":2021,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Water Systems and 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":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Distribution (mathematics); Mathematics","score_opus":0.01422995657339082,"score_gpt":0.1937906625505142,"score_spread":0.17956070597712337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163587973","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":[0.8982056,0.00633843,0.004960797,0.044218,0.00010846438,0.000061211525,0.001257419,0.000065710476,0.044784293],"genre_scores_gemma":[0.98600835,0.0063486835,0.0019211719,0.0011355581,0.000050677478,0.000019399567,0.00034055373,0.000015006258,0.004160605],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975306,0.000040007566,0.000008585492,0.000040494106,0.00007017784,0.00008765122],"domain_scores_gemma":[0.9987509,0.00027901132,0.00031208983,0.000039979164,0.0004237412,0.00019424668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007230855,0.00025498576,0.00016650809,0.0009545516,0.0015809323,0.0020359813,0.00085032504,0.000872762,0.0018899137],"category_scores_gemma":[0.002330834,0.00019014844,0.00024284194,0.0010751797,0.0011765033,0.003204926,0.0008251046,0.0012427766,0.00020992273],"study_design_candidate":"simulation_or_modeling","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":[0.00008119983,0.0002974448,0.4721382,0.0006494677,0.000073777774,0.0013567474,0.10610742,0.0368141,0.0028213742,0.10568769,0.048428066,0.22554448],"study_design_scores_gemma":[0.000008312796,0.00012662254,0.6087312,0.0006418794,0.000060045204,0.00027469484,0.13545209,0.061227612,0.00063061825,0.042439498,0.150318,0.000089458044],"about_ca_topic_score_codex":0.7492079,"about_ca_topic_score_gemma":0.8235784,"teacher_disagreement_score":0.2507921,"about_ca_system_score_codex":0.01216413,"about_ca_system_score_gemma":0.010741156,"threshold_uncertainty_score":0.50453806},"labels":[],"label_agreement":null},{"id":"W3164441817","doi":"10.1002/cjce.24202","title":"Neural network identification of water pipe blockage from smart embedded passive acoustic measurements","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Representation (politics); Fourier transform; Computer science; Acoustics; Set (abstract data type); Amplitude; Data set; Identification (biology); Flow (mathematics); Process (computing); Artificial intelligence; Mathematics; Physics; Mechanics","score_opus":0.009972029192553173,"score_gpt":0.16843959325143462,"score_spread":0.15846756405888146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164441817","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":[0.64182734,0.00019139833,0.3556657,0.000055518292,0.000031791602,0.000041801588,0.00013197401,0.00046378977,0.0015907063],"genre_scores_gemma":[0.9870314,0.000048098922,0.011943506,0.000008367327,0.000005670159,0.000021464055,0.00009026799,0.0000065417426,0.0008447418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988973,0.0000201283,0.0000070086594,0.0000336398,0.000032462158,0.000017028291],"domain_scores_gemma":[0.99972564,0.00014159211,0.00004110012,0.000016562344,0.000067213194,0.000007802218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033057164,0.00043796588,0.00028145817,0.0003760572,0.00009498534,0.00026722942,0.0002644763,0.00034315276,0.0006064568],"category_scores_gemma":[0.0010218556,0.00013333531,0.00019768445,0.00022125496,0.00015855122,0.00031493328,0.00021019651,0.00026737293,0.0001392704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048224998,0.0002173957,0.011096977,0.00012679189,0.00008659865,0.00013233171,0.000076258584,0.71300393,0.06508429,0.00047121503,0.00030092977,0.208921],"study_design_scores_gemma":[0.0000017541057,0.00002589703,0.002454041,0.0000021447486,0.0000046726395,0.000008124187,0.0000047691633,0.9940726,0.0033182409,0.000063120235,0.000041862808,0.0000026726495],"about_ca_topic_score_codex":0.002561542,"about_ca_topic_score_gemma":0.0021490096,"teacher_disagreement_score":0.002561542,"about_ca_system_score_codex":0.00021697207,"about_ca_system_score_gemma":0.00016957916,"threshold_uncertainty_score":0.005093336},"labels":[],"label_agreement":null},{"id":"W3166341592","doi":"10.1061/(asce)is.1943-555x.0000620","title":"Accounting for Source Location on the Vulnerability Assessment of Water Distribution Network","year":2021,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"","keywords":"Centrality; Ranking (information retrieval); Robustness (evolution); Vulnerability (computing); Metric (unit); Computer science; Network analysis; Graph theory; Graph; Reliability engineering; Data mining; Engineering; Environmental science; Mathematics; Statistics; Theoretical computer science; Machine learning; Computer security; Operations management","score_opus":0.00640424304630699,"score_gpt":0.2192224532327789,"score_spread":0.2128182101864719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166341592","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":[0.8531677,0.00037593866,0.14009549,0.0003067099,0.000031617245,0.00004512247,0.00029471345,0.0001594404,0.005523235],"genre_scores_gemma":[0.9964869,0.0000705705,0.0031050164,0.000004659893,0.0000066789357,0.000005197445,0.00004582066,0.0000054089014,0.0002697626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934477,0.00033800234,0.00002477749,0.00006151905,0.00015325513,0.00007767147],"domain_scores_gemma":[0.99499124,0.003435719,0.00066157355,0.0003196448,0.0004644679,0.00012732102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012228894,0.00061219453,0.0002917481,0.0017385392,0.00031686347,0.0007556785,0.00032791862,0.0004584896,0.0007930793],"category_scores_gemma":[0.006662399,0.00013790044,0.00033098948,0.0012946517,0.00049673003,0.0018403797,0.0009986231,0.00032482302,0.00008016571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005176246,0.000017034208,0.026168039,0.000040667357,0.0000608603,0.00015424234,0.0000423403,0.94096935,0.0020022052,0.0055553275,0.00020545718,0.024732707],"study_design_scores_gemma":[0.0000018875851,0.00006177627,0.010544498,0.00001238744,0.000030231415,0.00006286099,0.00007410608,0.9830148,0.0012148389,0.0046573025,0.00030909176,0.000016258187],"about_ca_topic_score_codex":0.0068267933,"about_ca_topic_score_gemma":0.009453308,"teacher_disagreement_score":0.0068267933,"about_ca_system_score_codex":0.00071390695,"about_ca_system_score_gemma":0.0005177058,"threshold_uncertainty_score":0.013574123},"labels":[],"label_agreement":null},{"id":"W3166457679","doi":"10.1061/(asce)wr.1943-5452.0001428","title":"Discussion of “Dynamic Pressure-Dependent Simulation of Water Distribution Networks Considering Volume-Driven Demands Based on Noniterative Application of EPANET 2” by P. Sivakumar, Nikolai B. Gorev, Tiku T. Tanyimboh, Inna F. Kodzhespirova, C. R. Suribabu, and T. R. Neelakantan","year":2021,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"St. Stephen's University; University of Toronto","funders":"","keywords":"Volume (thermodynamics); Distribution (mathematics); Mathematics; Thermodynamics; Physics; Mathematical analysis","score_opus":0.004276745654979211,"score_gpt":0.19862115127659777,"score_spread":0.19434440562161856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166457679","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18630004,0.0012966663,0.7469483,0.0152452495,0.0018647223,0.00018456845,0.0007097127,0.000647369,0.046803396],"genre_scores_gemma":[0.9351968,0.000636555,0.05252282,0.0008707374,0.00044319395,0.00017873452,0.00023137225,0.0002059876,0.00971389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962807,0.00023329984,0.000011762235,0.000034798904,0.00006539915,0.000026559896],"domain_scores_gemma":[0.99863005,0.0010918003,0.000040759714,0.00004805536,0.00014667714,0.000042800224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010605187,0.0004259065,0.0006274691,0.00027498556,0.0006985518,0.0010132128,0.0016997164,0.0013128984,0.005281061],"category_scores_gemma":[0.0034201955,0.00027059214,0.0008893234,0.00031131908,0.00074997835,0.00126561,0.0008792124,0.0013546131,0.00022879052],"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":[0.0000359558,0.000025281206,0.00043335106,0.00003267889,0.00001552461,0.000111696,0.000035955727,0.9812944,0.0004872999,0.013147119,0.0012887476,0.0030919828],"study_design_scores_gemma":[0.0000043722425,0.000007970343,0.000044540513,0.0000032785206,0.0000022738752,0.0000050103727,0.000007630862,0.99720424,0.00017080523,0.0019232421,0.0006233007,0.00000326931],"about_ca_topic_score_codex":0.012098983,"about_ca_topic_score_gemma":0.008247553,"teacher_disagreement_score":0.012098983,"about_ca_system_score_codex":0.0006063412,"about_ca_system_score_gemma":0.0007617101,"threshold_uncertainty_score":0.02405709},"labels":[],"label_agreement":null},{"id":"W3167410629","doi":"10.1061/9780784483466.046","title":"Numerical Study of Storm Geyser Mechanism in the Multi-Inlet System at Laboratory and Prototype Scale","year":2021,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2021","topic":"Water Systems and 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":"Inlet; Inflow; Storm; Scale (ratio); Computer simulation; Environmental science; Scale model; Mechanism (biology); Geology; Mechanics; Marine engineering; Meteorology; Hydrology (agriculture); Geotechnical engineering; Geomorphology; Engineering; Physics; Aerospace engineering","score_opus":0.005391717188795832,"score_gpt":0.16905062930833487,"score_spread":0.16365891211953904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167410629","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":[0.98526555,0.00011311431,0.010135412,0.00016867345,0.00003664014,0.000038790044,0.00019082661,0.00016416368,0.0038869218],"genre_scores_gemma":[0.99560285,0.00005871048,0.00346148,0.00001071165,0.000003713746,0.000026641508,0.00008666741,0.000012419097,0.00073695055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981314,0.00003791635,0.0000142833505,0.000040893712,0.00004399758,0.000049751296],"domain_scores_gemma":[0.9996332,0.00015631947,0.000059923637,0.000033913308,0.00007105251,0.00004559863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003899974,0.00049578375,0.0007505861,0.0004443791,0.00082929677,0.0007344644,0.00065701874,0.0011110653,0.0015687235],"category_scores_gemma":[0.00071456743,0.00025362967,0.0007905,0.00041032932,0.0007915029,0.00065791316,0.0005408187,0.0006294541,0.0000946646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016826636,0.00016318688,0.008218439,0.0000936747,0.000029033185,0.00046312035,0.00012682842,0.969127,0.015971651,0.0018555402,0.00048080992,0.0033025767],"study_design_scores_gemma":[0.00002037898,0.000057681085,0.0013675389,0.0000030829488,0.000007142072,0.000018775716,0.00005870464,0.99614584,0.0020281777,0.00013810514,0.00014471736,0.000009896305],"about_ca_topic_score_codex":0.018850287,"about_ca_topic_score_gemma":0.0077344184,"teacher_disagreement_score":0.018850287,"about_ca_system_score_codex":0.00075807126,"about_ca_system_score_gemma":0.0008123554,"threshold_uncertainty_score":0.03748113},"labels":[],"label_agreement":null},{"id":"W3168220565","doi":"10.71781/17061","title":"Décharge à courant alternatif (AC) dans l’air et en contact avec l’eau : caractérisation fondamentale et application au traitement des eaux","year":2020,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Water Systems and 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","keywords":"Physics; Humanities; Environmental science; Philosophy","score_opus":0.005626516342412788,"score_gpt":0.18038346510933057,"score_spread":0.17475694876691777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168220565","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":[0.96845376,0.012562445,0.009461953,0.00034545522,0.00020885686,0.00007895428,0.00050755194,0.00021253996,0.0081685465],"genre_scores_gemma":[0.9786275,0.005574131,0.007696483,0.00011463429,0.00005150172,0.000080035796,0.00032775392,0.000058378908,0.007469704],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997092,0.000020324038,0.000012292731,0.00007319595,0.00013893291,0.00004597935],"domain_scores_gemma":[0.9998504,0.000068432324,0.000022924445,0.000008351858,0.00004024425,0.00000957684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021979907,0.000271189,0.00038948518,0.00035188248,0.0004010438,0.0006525602,0.00034640369,0.0006439166,0.0031273852],"category_scores_gemma":[0.00031840333,0.00019263518,0.00036544018,0.00041455886,0.00034279816,0.00066851627,0.00029981465,0.0006263255,0.00040958915],"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":[0.00014955153,0.000036444828,0.00075500214,0.0005994322,0.000013719857,0.00032709696,0.00036379945,0.00097655534,0.9824203,0.0006407714,0.00044938576,0.013268012],"study_design_scores_gemma":[0.000008586082,0.0004287859,0.0053351335,0.000055500157,0.000029746443,0.00028141603,0.00078842073,0.0042080116,0.971913,0.00025531338,0.016662829,0.000033213524],"about_ca_topic_score_codex":0.0013805066,"about_ca_topic_score_gemma":0.0026208176,"teacher_disagreement_score":0.0031273852,"about_ca_system_score_codex":0.00034943243,"about_ca_system_score_gemma":0.00020077873,"threshold_uncertainty_score":0.010462165},"labels":[],"label_agreement":null},{"id":"W3169146648","doi":"10.1088/1755-1315/774/1/012042","title":"Novel techniques for the analysis of dynamic pressure in penstocks","year":2021,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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":"Hydro-Québec","funders":"","keywords":"Penstock; Strain gauge; Pressure measurement; Dynamic pressure; Line (geometry); Pressure sensor; Hydropower; Electrical conduit; Vibrating wire; Acoustics; Power (physics); Engineering; Structural engineering; Electrical engineering; Mechanical engineering; Mechanics; Physics; Mathematics","score_opus":0.0076376499069516005,"score_gpt":0.18972474731317138,"score_spread":0.18208709740621978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169146648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018869348,0.0003302806,0.9788128,0.000024472485,0.000039419694,0.000027305718,0.00010794843,0.0006991934,0.0010891317],"genre_scores_gemma":[0.3250306,0.001025095,0.6701119,0.000029105815,0.00007330471,0.00012190178,0.00031421363,0.00020062778,0.003093332],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957556,0.00005323356,0.00002113083,0.00009701826,0.00022566674,0.000027365153],"domain_scores_gemma":[0.9994879,0.00020773837,0.000092514136,0.000085557185,0.00011044977,0.000015873333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029288765,0.0007519879,0.0005292005,0.0012562709,0.00023578666,0.0010049763,0.0008186822,0.0005905306,0.0024925652],"category_scores_gemma":[0.0011634029,0.00030675883,0.00040230894,0.0015927076,0.00045469048,0.0010455119,0.00059084926,0.0009306127,0.0007298973],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015789656,0.00014205264,0.0027173762,0.0007078735,0.00007628079,0.0005240785,0.00045188496,0.058884665,0.37258703,0.012023413,0.001201785,0.5505257],"study_design_scores_gemma":[0.000023146187,0.00021865325,0.00856443,0.00006171187,0.000038367827,0.0009783432,0.00026259894,0.8579301,0.113957986,0.0063144783,0.011567517,0.000082650746],"about_ca_topic_score_codex":0.0007779262,"about_ca_topic_score_gemma":0.0008594659,"teacher_disagreement_score":0.0024925652,"about_ca_system_score_codex":0.00021946945,"about_ca_system_score_gemma":0.00036338234,"threshold_uncertainty_score":0.008338511},"labels":[],"label_agreement":null},{"id":"W3169166971","doi":"10.1061/9780784483466.097","title":"Improving Electricity Conservation in Small-Medium Municipal Water Distribution Systems in Ontario, Canada","year":2021,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2021","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Electricity; Energy conservation; Energy consumption; Electricity system; Environmental economics; Computer science; Electric power distribution; Water conservation; Environmental science; Equating; Agency (philosophy); Electricity generation; Engineering; Power (physics); Water resources","score_opus":0.004912156787174377,"score_gpt":0.13991339034209785,"score_spread":0.13500123355492347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169166971","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":[0.97467786,0.00029738934,0.008227773,0.0004646567,0.000010353401,0.00024912826,0.0012236587,0.00030623664,0.0145430565],"genre_scores_gemma":[0.99037665,0.00012932351,0.004949191,0.000017732154,0.0000013502042,0.000022424287,0.00040939028,0.000024002153,0.004069985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994837,0.000061099185,0.000019298877,0.000064278756,0.00021849951,0.00015310592],"domain_scores_gemma":[0.9993781,0.0001352406,0.00005210102,0.000022062432,0.00031451244,0.000098045755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046387443,0.0003211495,0.0002221008,0.0008315147,0.0017069401,0.0015279717,0.0007154295,0.00028149923,0.002371685],"category_scores_gemma":[0.0018421122,0.00022071048,0.0002668806,0.0021098736,0.00066617737,0.00052570883,0.0005172377,0.00024919971,0.00012947999],"study_design_candidate":"observational","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":[0.0005878196,0.0003601348,0.19007337,0.00054639747,0.00013352887,0.0012412494,0.0012897969,0.6080581,0.014811663,0.009121118,0.011403238,0.16237357],"study_design_scores_gemma":[0.00016177105,0.00025911236,0.24809723,0.00008707764,0.000111772795,0.00013137766,0.0044474294,0.7078925,0.010458604,0.002744166,0.02551431,0.00009473296],"about_ca_topic_score_codex":0.9825289,"about_ca_topic_score_gemma":0.99282044,"teacher_disagreement_score":0.035568256,"about_ca_system_score_codex":0.035568256,"about_ca_system_score_gemma":0.023064679,"threshold_uncertainty_score":0.25806695},"labels":[],"label_agreement":null},{"id":"W3169678762","doi":"10.1088/1755-1315/774/1/012021","title":"MOC-CFD coupled model of load rejection in hydropower station","year":2021,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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":"Hydro-Québec; University of Toronto","funders":"","keywords":"Penstock; Draft tube; Load rejection; Turbine; Computational fluid dynamics; Torque; Vortex; Francis turbine; Mechanics; Flow (mathematics); Marine engineering; Casing; Hydropower; Engineering; Water hammer; Solver; Simulation; Structural engineering; Mechanical engineering; Computer science; Physics","score_opus":0.008672096967538255,"score_gpt":0.17706592161430534,"score_spread":0.16839382464676708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169678762","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":[0.89132535,0.0001787994,0.07096431,0.00044063662,0.000077447505,0.00016357153,0.0016375299,0.00088993175,0.03432234],"genre_scores_gemma":[0.992603,0.000043175518,0.0019414676,0.000039525432,0.000008250358,0.00006181341,0.0002290103,0.00004266079,0.0050310916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999828,0.000029135075,0.000007947028,0.000046065008,0.000042032603,0.000046714275],"domain_scores_gemma":[0.99966073,0.00012091579,0.000058970523,0.000033621385,0.00007894011,0.000046819554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021155753,0.0005041821,0.0007751845,0.0005703176,0.0006681016,0.001531135,0.0011441343,0.0018008326,0.004115296],"category_scores_gemma":[0.0009781963,0.0005298905,0.00055848155,0.0006214926,0.0009375142,0.0005670124,0.000606412,0.00064703956,0.0004394961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021879441,0.000018756808,0.000997999,0.00000851808,0.0000054011357,0.000097778575,0.000013956992,0.99687046,0.0006641091,0.000496923,0.00012743186,0.00067685946],"study_design_scores_gemma":[0.0000075211833,0.000009638961,0.0004283088,0.000001403296,0.0000018411232,0.0000074464015,0.0000069277553,0.99914753,0.00015773228,0.000102543265,0.00012553696,0.0000035643075],"about_ca_topic_score_codex":0.06592266,"about_ca_topic_score_gemma":0.01915319,"teacher_disagreement_score":0.06592266,"about_ca_system_score_codex":0.0017501286,"about_ca_system_score_gemma":0.0015115481,"threshold_uncertainty_score":0.13107795},"labels":[],"label_agreement":null},{"id":"W3169851190","doi":"10.1061/9780784483466.045","title":"CFD Simulation of Air–Water Interactions in Rapidly Filling Horizontal Pipe with Entrapped Air","year":2021,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2021","topic":"Water Systems and 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":"Mechanics; Water hammer; Computational fluid dynamics; Mixing (physics); Flow (mathematics); Volume of fluid method; Pipe flow; Materials science; Solver; Computer simulation; Breakup; Airflow; Transient (computer programming); Air water; Geotechnical engineering; Geology; Turbulence; Mechanical engineering; Engineering; Physics; Computer science","score_opus":0.004950907312180999,"score_gpt":0.17394602030606743,"score_spread":0.16899511299388642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169851190","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":[0.978314,0.0001048707,0.017513892,0.00008097383,0.000020448006,0.000028616052,0.00014434168,0.00012421038,0.0036687853],"genre_scores_gemma":[0.9954516,0.00005042725,0.0035100612,0.000009077835,0.0000030101742,0.000019280636,0.000052525887,0.00001052688,0.0008934831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991345,0.000019885449,0.000005286142,0.000012364006,0.000021175481,0.000027864684],"domain_scores_gemma":[0.9996586,0.00022540393,0.000038895065,0.000013913596,0.00003389173,0.000029373621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020425982,0.00034988695,0.0005168779,0.000323156,0.00044723187,0.0004952814,0.00032730616,0.0006523056,0.00089711626],"category_scores_gemma":[0.00050248805,0.00024339827,0.0003590894,0.00034044348,0.00049737486,0.0003708452,0.00035648246,0.00034536314,0.00006400627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077072706,0.00004634939,0.002883597,0.000039836505,0.000011813284,0.00025102307,0.000093189614,0.98259985,0.009524854,0.00094069,0.00013085015,0.003400917],"study_design_scores_gemma":[0.0000042932234,0.00002888546,0.0006485672,0.0000020830103,0.0000021896815,0.000010129389,0.000021063597,0.99789196,0.0012216426,0.00007635984,0.00008964694,0.0000031624925],"about_ca_topic_score_codex":0.010817572,"about_ca_topic_score_gemma":0.0052403677,"teacher_disagreement_score":0.010817572,"about_ca_system_score_codex":0.00047082483,"about_ca_system_score_gemma":0.00072963437,"threshold_uncertainty_score":0.02150917},"labels":[],"label_agreement":null},{"id":"W3170610946","doi":"10.3390/w13111597","title":"An Overview of the Numerical Approaches to Water Hammer Modelling: The Ongoing Quest for Practical and Accurate Numerical Approaches","year":2021,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":48,"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":"Finite volume method; Computational fluid dynamics; Computer science; Partial differential equation; Applied mathematics; Water hammer; Finite difference; Upwind scheme; Numerical analysis; Momentum (technical analysis); Transient (computer programming); Mechanics; Mathematics; Physics; Discretization; Mathematical analysis","score_opus":0.27468303178748954,"score_gpt":0.28212084135301885,"score_spread":0.0074378095655293075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170610946","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":[0.0017487039,0.35815853,0.6052089,0.008326976,0.0035002823,0.00008977366,0.00021460524,0.00054363057,0.022208542],"genre_scores_gemma":[0.028003452,0.6359491,0.31622025,0.0017338158,0.0042436565,0.00029816406,0.00047465885,0.00042862634,0.012648372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865925,0.00037216922,0.00014447511,0.00013761467,0.00063555257,0.00005076431],"domain_scores_gemma":[0.9980635,0.0009726883,0.00010636899,0.00015231222,0.0006614274,0.0000436909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023687098,0.0012042772,0.0012801028,0.0023416716,0.00068838365,0.0029253005,0.0021506625,0.0021336025,0.004539798],"category_scores_gemma":[0.004534745,0.0008042602,0.0011349369,0.0025360242,0.0018911095,0.003196244,0.0013685797,0.00394607,0.0030244184],"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":[0.000043674056,0.00006629399,0.00063358847,0.006759231,0.0000879125,0.00018273538,0.0003155569,0.10338232,0.0039473423,0.46214077,0.03724902,0.38519156],"study_design_scores_gemma":[0.000011309546,0.000090090325,0.0005176786,0.0025755435,0.000045911725,0.00044750897,0.00020119926,0.10010665,0.0021399555,0.19912457,0.6946184,0.000121198],"about_ca_topic_score_codex":0.0026642417,"about_ca_topic_score_gemma":0.0020454505,"teacher_disagreement_score":0.004539798,"about_ca_system_score_codex":0.0013658524,"about_ca_system_score_gemma":0.0016185874,"threshold_uncertainty_score":0.015187085},"labels":[],"label_agreement":null},{"id":"W3174656366","doi":"10.1038/s41545-021-00125-2","title":"Forecasting point-of-consumption chlorine residual in refugee settlements using ensembles of artificial neural networks","year":2021,"lang":"en","type":"article","venue":"npj Clean Water","topic":"Water Systems and 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":"Centre for Global Health Research; York University","funders":"Achmea; York University; Natural Sciences and Engineering Research Council of Canada; Enhancing Learning and Research for Humanitarian Assistance; United States Agency for International Development","keywords":"Residual; Artificial neural network; Consumption (sociology); Human settlement; Point (geometry); Refugee; Chlorine; Point estimation; Environmental science; Water supply; Computer science; Environmental engineering; Engineering; Statistics; Artificial intelligence; Geography; Mathematics; Waste management; Materials science; Algorithm","score_opus":0.04224455865913453,"score_gpt":0.23976185723197188,"score_spread":0.19751729857283734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174656366","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":[0.97541964,0.00014745259,0.022857543,0.0001469035,0.000035322566,0.000014742904,0.00028263216,0.00009843953,0.000997338],"genre_scores_gemma":[0.99754983,0.000028810697,0.0020518473,0.000009881798,0.00000405123,0.000006867716,0.00014973166,0.0000034454456,0.00019552877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998288,0.0000548404,0.000010536227,0.000041604795,0.000031225223,0.000033012344],"domain_scores_gemma":[0.99922097,0.00039310366,0.00009185416,0.00005459677,0.00019594714,0.00004350178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008436691,0.00061519974,0.00049343845,0.0004703016,0.00019556802,0.00048525826,0.000496169,0.00056328217,0.00045123685],"category_scores_gemma":[0.0019945283,0.00022634781,0.00054759905,0.0004695191,0.00019445682,0.00056666584,0.00042846304,0.0006796722,0.000090940084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030295232,0.000022579881,0.008080524,0.0000061482497,0.000034946366,0.000023936698,0.000009889834,0.98764384,0.00031783743,0.000052079322,0.00011120951,0.0036667848],"study_design_scores_gemma":[0.0000011056162,0.000009312908,0.0017651002,0.0000015499002,0.0000036449396,0.0000017033528,0.000008143387,0.9980063,0.000106108804,0.00006996215,0.00002419278,0.0000028511377],"about_ca_topic_score_codex":0.029773772,"about_ca_topic_score_gemma":0.022333894,"teacher_disagreement_score":0.029773772,"about_ca_system_score_codex":0.00072612654,"about_ca_system_score_gemma":0.0003689899,"threshold_uncertainty_score":0.059200943},"labels":[],"label_agreement":null},{"id":"W3185249967","doi":"10.23919/acc50511.2021.9482769","title":"Pipeline crawler development for mapping gas pipeline topology","year":2021,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Pipeline (software); Odometer; Inertial measurement unit; Web crawler; Global Positioning System; Computer science; Real-time computing; Geographic information system; Pipeline transport; Topology (electrical circuits); Engineering; Remote sensing; Geography; Artificial intelligence; Mechanical engineering; Electrical engineering","score_opus":0.018935680916868414,"score_gpt":0.2138506519352894,"score_spread":0.19491497101842098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185249967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045326322,0.0001672998,0.949706,0.000059263482,0.000027612195,0.00007325752,0.00017987753,0.0024009391,0.0020594494],"genre_scores_gemma":[0.35715216,0.00040456757,0.63781375,0.00002273843,0.000010056168,0.00012391442,0.0006568445,0.00014492992,0.0036710394],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980766,0.000025175927,0.000008017631,0.00005168426,0.00009230249,0.000015298408],"domain_scores_gemma":[0.9998111,0.000028389555,0.000025762576,0.00003747575,0.000085740074,0.000011595194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017109679,0.00059004955,0.00032239777,0.0007713436,0.00026080312,0.00036236804,0.00049954717,0.00032012793,0.0016417516],"category_scores_gemma":[0.0005043357,0.0002484671,0.00036290893,0.00079325424,0.00016377485,0.0008495018,0.0005674901,0.00030792828,0.000623799],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000992935,0.000051211107,0.0052959137,0.00028486643,0.00004262191,0.0003652564,0.00036794404,0.06913042,0.3294477,0.0048617246,0.0049119666,0.5851411],"study_design_scores_gemma":[0.000015563484,0.0002530576,0.007325712,0.000019900912,0.00002850609,0.00071226637,0.00016059552,0.7872482,0.18440697,0.0009916254,0.018777097,0.000060632945],"about_ca_topic_score_codex":0.002939623,"about_ca_topic_score_gemma":0.0039446256,"teacher_disagreement_score":0.002939623,"about_ca_system_score_codex":0.0002312307,"about_ca_system_score_gemma":0.0006254587,"threshold_uncertainty_score":0.0058450103},"labels":[],"label_agreement":null},{"id":"W3185978610","doi":"10.15530/urtec-2021-5443","title":"Rate-Transient Analysis of Communicating Wells Using the Dynamic Drainage Area (DDA) Concept","year":2021,"lang":"en","type":"article","venue":"Proceedings of the 9th Unconventional Resources Technology Conference","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Transient analysis; Transient (computer programming); Drainage; Computer science; Geology; Transient response; Engineering; Electrical engineering; Operating system","score_opus":0.014023199862985036,"score_gpt":0.22215587126210645,"score_spread":0.20813267139912142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185978610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20067917,0.00075265527,0.7699278,0.00090311427,0.000120997305,0.00006868638,0.00015233537,0.00040031277,0.026994914],"genre_scores_gemma":[0.98898715,0.00015671396,0.006625184,0.000036369336,0.000022471253,0.000020790281,0.00002730325,0.000061545834,0.0040623723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968684,0.00010871684,0.000008413144,0.000043068623,0.00007613873,0.000076798846],"domain_scores_gemma":[0.9984201,0.0011094697,0.00012706146,0.000061198065,0.00022081648,0.00006133502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009957639,0.0004424036,0.0006557673,0.0006618876,0.00034865743,0.0011806784,0.0010161875,0.000762643,0.002564686],"category_scores_gemma":[0.003358407,0.0003731786,0.00056054967,0.00047482032,0.0012170752,0.0013917026,0.0008167867,0.0009210631,0.00016618197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011454548,0.000051315823,0.0009025256,0.000056440138,0.000022973987,0.00010783676,0.00008422959,0.9068539,0.007334861,0.077636994,0.0007307269,0.006103705],"study_design_scores_gemma":[0.0000018062312,0.0000039321344,0.00004832677,0.0000014357007,0.000001911636,0.0000050061017,0.0000063806397,0.9979346,0.00025970076,0.0016720713,0.000062606145,0.0000022053425],"about_ca_topic_score_codex":0.005415564,"about_ca_topic_score_gemma":0.0030832114,"teacher_disagreement_score":0.005415564,"about_ca_system_score_codex":0.0014286158,"about_ca_system_score_gemma":0.00080786645,"threshold_uncertainty_score":0.010768056},"labels":[],"label_agreement":null},{"id":"W3189408373","doi":"10.1139/cjce-2020-0604","title":"Regulation response of pilot-operated diaphragm type pressure-reducing valves: laboratory testing and impact on the performance of pressure control modes in water distribution systems","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Institut National de la Recherche Scientifique","funders":"","keywords":"Pressure regulator; Control valves; Pressure control; Relief valve; Leakage (economics); Diaphragm (acoustics); Pressure sensor; Limit (mathematics); Control theory (sociology); Engineering; Environmental science; Control (management); Computer science; Control engineering; Mechanical engineering; Mathematics","score_opus":0.006988044469928743,"score_gpt":0.17142110379496428,"score_spread":0.16443305932503555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189408373","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":[0.9972121,0.00007407393,0.002251147,0.000019399207,0.00001150883,0.000025923433,0.000088244335,0.00008352456,0.00023410971],"genre_scores_gemma":[0.99925154,0.00002616945,0.0005986769,0.0000036471042,0.0000012329236,0.000010346562,0.00002256946,0.0000027982792,0.00008306853],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999323,0.00017613804,0.000047789712,0.0001288892,0.00019442673,0.00012980435],"domain_scores_gemma":[0.998206,0.0008899007,0.00024894185,0.00016401662,0.0004108303,0.00008032659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068189565,0.00044327974,0.00043343127,0.0003077827,0.00024867186,0.0004024724,0.00084686826,0.000496798,0.00087663514],"category_scores_gemma":[0.0022675756,0.00017291745,0.000285485,0.0002179235,0.0005272469,0.00033429294,0.00028823328,0.00041040286,0.0000994731],"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":[0.0024638984,0.0017465175,0.017785829,0.0009874983,0.00009969927,0.0006901862,0.00056476216,0.14765628,0.7944144,0.00033366858,0.00054452335,0.032712616],"study_design_scores_gemma":[0.00017661735,0.00893024,0.03389642,0.000041810228,0.0000997304,0.00023961891,0.00046511402,0.20625946,0.7486636,0.00014341742,0.0009945596,0.000089405505],"about_ca_topic_score_codex":0.007253439,"about_ca_topic_score_gemma":0.005503485,"teacher_disagreement_score":0.007253439,"about_ca_system_score_codex":0.00048938853,"about_ca_system_score_gemma":0.00041231085,"threshold_uncertainty_score":0.014422417},"labels":[],"label_agreement":null},{"id":"W3190313535","doi":"10.1061/9780784483619.047","title":"The Challenge of Air Valve Selection in Pumping Systems","year":2021,"lang":"en","type":"article","venue":"Pipelines 2021","topic":"Water Systems and 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":"WSP (Canada); Thornhill Medical (Canada)","funders":"","keywords":"Water hammer; Transient (computer programming); Pipeline transport; Outflow; Inflow; Pipeline (software); Environmental science; Atmospheric pressure; Marine engineering; Engineering; Hydraulic machinery; Petroleum engineering; Mechanical engineering; Computer science; Mechanics; Meteorology; Physics","score_opus":0.010627168547899109,"score_gpt":0.20654409898639578,"score_spread":0.19591693043849667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190313535","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":[0.5548254,0.0022752588,0.42248994,0.0031627002,0.00015009056,0.00013616336,0.00015650799,0.00051567855,0.01628826],"genre_scores_gemma":[0.9882301,0.00025246287,0.0105073005,0.000046558675,0.000019631034,0.00002360028,0.000014717274,0.000027662185,0.0008779952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982298,0.0010188586,0.00007872539,0.00022574408,0.000262603,0.00018425434],"domain_scores_gemma":[0.99320436,0.0055933814,0.00044225564,0.0002654641,0.0003701247,0.00012439786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024804368,0.00041343336,0.00093870837,0.0002770379,0.0007978753,0.0017708307,0.0009916617,0.001932374,0.0019702814],"category_scores_gemma":[0.010084697,0.0005624769,0.00043304593,0.00030993755,0.0012017671,0.0022156336,0.00080069597,0.00085257925,0.00032925248],"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":[0.000113090864,0.000065700064,0.0022294573,0.00012876347,0.000019103676,0.00016919975,0.00009707034,0.95987964,0.0045539797,0.006517144,0.0005734868,0.025653422],"study_design_scores_gemma":[0.000032558186,0.00022210517,0.00080769445,0.000024307059,0.000016475706,0.00010822209,0.00016232555,0.98575324,0.0034360143,0.007855411,0.0015589548,0.000022788508],"about_ca_topic_score_codex":0.0024754803,"about_ca_topic_score_gemma":0.0015520258,"teacher_disagreement_score":0.0024804368,"about_ca_system_score_codex":0.0007763951,"about_ca_system_score_gemma":0.00094766874,"threshold_uncertainty_score":0.013117909},"labels":[],"label_agreement":null},{"id":"W3191181181","doi":"10.1061/9780784483602.010","title":"Increasing Pipelines’ Resilience for a Changing Climate","year":2021,"lang":"en","type":"article","venue":"Pipelines 2021","topic":"Water Systems and 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":"Canadian Pharmacists Association","funders":"","keywords":"Pipeline transport; Sustainability; Resilience (materials science); Extreme weather; Environmental science; Natural disaster; Environmental resource management; Risk analysis (engineering); Population; Climate change; Environmental planning; Natural resource economics; Environmental economics; Business; Engineering; Civil engineering; Environmental engineering","score_opus":0.009400674867236882,"score_gpt":0.2211477991503256,"score_spread":0.2117471242830887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191181181","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":[0.6016091,0.0017257802,0.2753634,0.010209621,0.00031168124,0.00011094383,0.00080436724,0.0015331194,0.10833201],"genre_scores_gemma":[0.988801,0.00038435444,0.006716158,0.000069156,0.000016596441,0.00001654647,0.00007070328,0.000050382834,0.003875094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99969995,0.000046368546,0.000012447178,0.000041887015,0.0000731336,0.00012625246],"domain_scores_gemma":[0.9995864,0.00008785703,0.00008918987,0.00007103617,0.000086220105,0.00007926555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060801744,0.00034869087,0.00023950731,0.0005574511,0.0008510108,0.0011334907,0.00043840904,0.0007800084,0.0066776834],"category_scores_gemma":[0.0019579206,0.00021051281,0.00042925202,0.00047955752,0.00094664405,0.0017735823,0.0018635979,0.000584746,0.0008360796],"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":[0.00018116068,0.00008713647,0.013887955,0.0002583617,0.000079067184,0.0010271122,0.0005487016,0.68444586,0.06875688,0.10223631,0.008836888,0.119654596],"study_design_scores_gemma":[0.000036667843,0.0007596547,0.029883496,0.00022495583,0.000091475435,0.0019851308,0.0031807544,0.6261788,0.027638553,0.16654773,0.14331307,0.00015978386],"about_ca_topic_score_codex":0.003047086,"about_ca_topic_score_gemma":0.0030136325,"teacher_disagreement_score":0.0066776834,"about_ca_system_score_codex":0.0010267034,"about_ca_system_score_gemma":0.00089515664,"threshold_uncertainty_score":0.022339106},"labels":[],"label_agreement":null},{"id":"W3191194657","doi":"10.15530/urtec-2021-5028","title":"Coupling Physics-Based Full Field Hydraulic Model With Advanced Data Analytics: Evolution of Surface Pipeline Operations","year":2021,"lang":"en","type":"article","venue":"Proceedings of the 9th Unconventional Resources Technology Conference","topic":"Water Systems and 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":"ConocoPhillips (Canada)","funders":"","keywords":"Pipeline (software); Coupling (piping); Field (mathematics); Surface (topology); Analytics; Computer science; Data science; Mechanical engineering; Engineering","score_opus":0.019877418767305123,"score_gpt":0.22056064443039566,"score_spread":0.20068322566309055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191194657","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":[0.9520354,0.0000958169,0.041123535,0.0005997546,0.00008999817,0.00004255297,0.00089870114,0.0012822128,0.0038319065],"genre_scores_gemma":[0.9947968,0.000030207266,0.0043551517,0.000025504183,0.000007390145,0.000012140329,0.00027583662,0.000058598373,0.00043844478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998374,0.00004381884,0.000011217969,0.000043721288,0.00003486227,0.000029013903],"domain_scores_gemma":[0.9991047,0.0004316794,0.000055587592,0.00013720758,0.00017754389,0.00009323385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005378769,0.00050446746,0.0006108548,0.00046520884,0.00043425758,0.0011249181,0.00079204835,0.0011592811,0.001561124],"category_scores_gemma":[0.0021405395,0.00049801957,0.0005793844,0.00076057855,0.0005904906,0.0016252843,0.0005932766,0.0010091845,0.00022797569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042111555,0.00008331117,0.0021199519,0.000009305407,0.000012327568,0.00002456439,0.000018761137,0.99337524,0.0010652171,0.0004734185,0.00029562894,0.002480156],"study_design_scores_gemma":[0.0000037441766,0.0000051697234,0.0003884564,3.7953362e-7,0.0000011786968,0.0000012244953,0.0000031579955,0.9993042,0.000120061115,0.00014034638,0.000029321345,0.0000027096366],"about_ca_topic_score_codex":0.04618839,"about_ca_topic_score_gemma":0.019880239,"teacher_disagreement_score":0.04618839,"about_ca_system_score_codex":0.0008756116,"about_ca_system_score_gemma":0.0017752951,"threshold_uncertainty_score":0.091839075},"labels":[],"label_agreement":null},{"id":"W3191712575","doi":"10.1061/9780784483602.002","title":"ATCO’s Urban Pipeline Replacement (UPR) Program","year":2021,"lang":"en","type":"article","venue":"Pipelines 2021","topic":"Water Systems and 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":"ATCO (Canada); Alberta Energy","funders":"","keywords":"Pipeline (software); Computer science; Programming language","score_opus":0.009361435412575185,"score_gpt":0.2203585966839798,"score_spread":0.2109971612714046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191712575","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":[0.45842293,0.0011859797,0.04493846,0.005673849,0.00039609682,0.0025870074,0.006598215,0.0034977363,0.47669968],"genre_scores_gemma":[0.84700054,0.0007577698,0.026865404,0.0005571122,0.000048568683,0.0002861935,0.0036591305,0.00024288744,0.120582364],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99846977,0.00016063747,0.00002061213,0.00008974146,0.0009004117,0.0003587499],"domain_scores_gemma":[0.99808073,0.00011585127,0.00008046355,0.00012184502,0.0012043358,0.00039677112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012898546,0.00031191265,0.00020335676,0.0007296424,0.0017501808,0.0011581706,0.0010890581,0.00036284648,0.010740664],"category_scores_gemma":[0.0020502948,0.00017139365,0.0003128233,0.0010588842,0.0007401857,0.00039567682,0.0009902574,0.00061687425,0.0010358114],"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":[0.0016721921,0.0014326464,0.058629364,0.00054950826,0.00009253411,0.0012581776,0.0017677533,0.06155281,0.020640992,0.051394083,0.22230278,0.57870716],"study_design_scores_gemma":[0.00038565032,0.0020334995,0.11550182,0.00017209676,0.000116197116,0.00076914666,0.003450003,0.041866843,0.02048374,0.0041788775,0.81089437,0.00014782045],"about_ca_topic_score_codex":0.596656,"about_ca_topic_score_gemma":0.74560654,"teacher_disagreement_score":0.596656,"about_ca_system_score_codex":0.010300136,"about_ca_system_score_gemma":0.038770977,"threshold_uncertainty_score":0.8114387},"labels":[],"label_agreement":null},{"id":"W3192541426","doi":"10.1029/2021wr029926","title":"Multi‐Sensor Fusion for Transient‐Based Pipeline Leak Localization in the Dempster‐Shafer Evidence Framework","year":2021,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water Systems and 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","funders":"Research Grants Council, University Grants Committee; National Natural Science Foundation of China","keywords":"Leak; Leak detection; Dempster–Shafer theory; Leakage (economics); Sensor fusion; Computer science; Transient (computer programming); Signature (topology); Artificial intelligence; Noise (video); Pipeline (software); Data mining; Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.10003026243386655,"score_gpt":0.33978053106112405,"score_spread":0.2397502686272575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192541426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015314039,0.0002728609,0.9836925,0.00009678553,0.000010665996,0.00002227286,0.000021002303,0.0000922519,0.00047761283],"genre_scores_gemma":[0.8136169,0.00043719963,0.18481004,0.000052036914,0.000032718828,0.00009886216,0.00008285529,0.000025322097,0.00084414124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990089,0.00037989454,0.00006906191,0.00015241142,0.00032547012,0.000064168795],"domain_scores_gemma":[0.99782014,0.0012671994,0.0003767098,0.00014364689,0.00032659335,0.00006565704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028335657,0.0010354809,0.0015841873,0.0015138244,0.00045208805,0.0013449955,0.0012966865,0.0014230656,0.0008561519],"category_scores_gemma":[0.0062274123,0.00065003696,0.0009356911,0.0013428171,0.0011846324,0.002415096,0.0016905046,0.0013324039,0.00016195235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015460595,0.000038678663,0.0008139069,0.00014556525,0.00008742019,0.00012653432,0.00006257807,0.92876756,0.003793965,0.011408248,0.0002948907,0.05430603],"study_design_scores_gemma":[0.0000027712215,0.000013081331,0.00011955475,0.0000044297763,0.000005383133,0.000011200661,0.0000044677604,0.99742115,0.00065324095,0.0016829633,0.00007475469,0.00000712066],"about_ca_topic_score_codex":0.003344356,"about_ca_topic_score_gemma":0.0020899056,"teacher_disagreement_score":0.003344356,"about_ca_system_score_codex":0.0011118408,"about_ca_system_score_gemma":0.0009491171,"threshold_uncertainty_score":0.014985502},"labels":[],"label_agreement":null},{"id":"W3192608070","doi":"10.1061/9780784483602.029","title":"A New Method to Detect Pipe Leaks during Pneumatic Testing","year":2021,"lang":"en","type":"article","venue":"Pipelines 2021","topic":"Water Systems and 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":"TransCanada (Canada)","funders":"","keywords":"Leak; Gas leak; Volume (thermodynamics); Chemistry; Materials science; Analytical Chemistry (journal); Mechanics; Thermodynamics; Chromatography; Physics","score_opus":0.017572423103590206,"score_gpt":0.24613261674110565,"score_spread":0.22856019363751545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192608070","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":[0.056882627,0.002524169,0.92960864,0.00031272226,0.0005977432,0.0003430188,0.0002039219,0.004300239,0.0052270214],"genre_scores_gemma":[0.568645,0.0018827035,0.41794878,0.00040302525,0.00024037641,0.00043507328,0.00028426904,0.00033813715,0.00982268],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99800557,0.00018791825,0.000100416895,0.00039698195,0.0012246169,0.00008460233],"domain_scores_gemma":[0.99766064,0.00056400645,0.00043068218,0.00029310316,0.0009592628,0.00009226745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008754185,0.0009841851,0.00073528686,0.0023541711,0.0004128298,0.00079079985,0.0013222389,0.0011596759,0.00188315],"category_scores_gemma":[0.0029869457,0.00050816545,0.0004501982,0.00094105146,0.00068473426,0.0022285082,0.0010740223,0.0008294853,0.00075930695],"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":[0.0003535296,0.00011130635,0.0053789546,0.000843614,0.000050315026,0.00072207,0.0004305737,0.0034483697,0.60889673,0.002404077,0.0029522683,0.3744083],"study_design_scores_gemma":[0.00010648174,0.0015985512,0.0123515045,0.0001794909,0.00019723467,0.004896649,0.00034505743,0.17016786,0.76463765,0.00275842,0.04244133,0.00031981728],"about_ca_topic_score_codex":0.0004849119,"about_ca_topic_score_gemma":0.0005227916,"teacher_disagreement_score":0.0023541711,"about_ca_system_score_codex":0.00047891203,"about_ca_system_score_gemma":0.0004254143,"threshold_uncertainty_score":0.006299734},"labels":[],"label_agreement":null},{"id":"W3193803806","doi":"10.22034/jcee.2021.44669.2007","title":"Probabilistic zoning of hydraulic performance of water distribution network by applying key parameter uncertainty","year":2021,"lang":"en","type":"article","venue":"Jurnal Teknik Lingkungan : Electronic Journal of Civil and Environmental Engineering","topic":"Water Systems and 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":"Jacobs (Canada)","funders":"","keywords":"Key (lock); Probabilistic logic; Zoning; MATLAB; Computer science; Civil engineering; Engineering; Artificial intelligence","score_opus":0.002444973065792359,"score_gpt":0.14507892448435084,"score_spread":0.14263395141855847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193803806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27063045,0.0006104368,0.71948826,0.0004583097,0.000055882017,0.00011834917,0.0011783962,0.0013342146,0.006125709],"genre_scores_gemma":[0.95961905,0.00021105424,0.03869892,0.000017075816,0.000011310928,0.00006378555,0.00040617707,0.000046726003,0.0009259464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966323,0.00009458078,0.000016597329,0.00008721378,0.000091669346,0.000046826906],"domain_scores_gemma":[0.99949753,0.00023856993,0.000073640986,0.00004687146,0.00012043254,0.000022932883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007134455,0.00053645734,0.0003785841,0.00095981546,0.0003407846,0.0007997436,0.00043725563,0.00043732024,0.001518947],"category_scores_gemma":[0.0023881611,0.00029440984,0.0005319138,0.0010040527,0.00039908753,0.0009417807,0.00081311475,0.0005190943,0.00014862037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010016181,0.000009931701,0.0024583025,0.000050928225,0.000019812842,0.000028186189,0.00004943265,0.9741154,0.0012412928,0.0023037887,0.00032127154,0.019301489],"study_design_scores_gemma":[0.0000047114486,0.00001335214,0.0016384398,0.000006275533,0.0000070310593,0.000008113155,0.00002511643,0.99530303,0.0007247321,0.0018881198,0.00037020672,0.000010908169],"about_ca_topic_score_codex":0.01701465,"about_ca_topic_score_gemma":0.013840921,"teacher_disagreement_score":0.01701465,"about_ca_system_score_codex":0.00073686166,"about_ca_system_score_gemma":0.0010791487,"threshold_uncertainty_score":0.03383124},"labels":[],"label_agreement":null},{"id":"W3194710586","doi":"10.1016/j.heliyon.2021.e07820","title":"Energy saving and management of water pumping networks","year":2021,"lang":"en","type":"article","venue":"Heliyon","topic":"Water Systems and 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":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Energy (signal processing); Energy management; Environmental science; Engineering; Mathematics; Statistics","score_opus":0.006177473191045657,"score_gpt":0.16924967019762072,"score_spread":0.16307219700657505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194710586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20654517,0.00075110653,0.77266353,0.0005154394,0.000054655447,0.00013099262,0.00015849095,0.0004455807,0.018735021],"genre_scores_gemma":[0.9773744,0.00022247042,0.019881811,0.000022459224,0.000008722322,0.00005725115,0.000048351332,0.000014947948,0.0023694958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997956,0.0000764043,0.000008134448,0.00003782287,0.000050492647,0.000031521824],"domain_scores_gemma":[0.9998448,0.000073077186,0.00002894243,0.000013255133,0.00003097725,0.000008934672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034466488,0.0003872274,0.00032522154,0.00036276746,0.0003638238,0.00071095076,0.000493621,0.000375524,0.0014990724],"category_scores_gemma":[0.0007367153,0.00016338493,0.00021860818,0.00040465366,0.00028116256,0.00090622937,0.00040169523,0.00025275868,0.00012128013],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025635112,0.00001800864,0.00040781667,0.000018388926,0.000008264244,0.000020312857,0.000012871054,0.96829736,0.0015831103,0.0025931704,0.00028419343,0.02673096],"study_design_scores_gemma":[0.0000032082792,0.000020128036,0.0002609477,0.0000022368558,0.0000035907624,0.0000055810715,0.000009283073,0.9967957,0.0006483183,0.0016872016,0.000561377,0.0000024349524],"about_ca_topic_score_codex":0.00378449,"about_ca_topic_score_gemma":0.003738154,"teacher_disagreement_score":0.00378449,"about_ca_system_score_codex":0.00071528903,"about_ca_system_score_gemma":0.0005369052,"threshold_uncertainty_score":0.0075249076},"labels":[],"label_agreement":null},{"id":"W3196274375","doi":"10.1007/978-3-030-77650-3_10","title":"Misbehaving Drinking Water Systems: Risk and the Complex Nature of Failure","year":2021,"lang":"en","type":"book-chapter","venue":"Palgrave studies in sustainable business in association with Future Earth","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Variety (cybernetics); Risk analysis (engineering); Business; Quality (philosophy); Environmental economics; Computer science; Computer security; Economics","score_opus":0.006787762014455672,"score_gpt":0.20177303632361795,"score_spread":0.1949852743091623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196274375","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":[0.07540405,0.063982695,0.1606371,0.08902234,0.0014837957,0.00008608046,0.00038997093,0.00026389924,0.60873],"genre_scores_gemma":[0.8877095,0.029286725,0.013031246,0.00302003,0.0010978213,0.000063543994,0.0001018121,0.00009396327,0.065595314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993331,0.0003074172,0.000021066917,0.00006156042,0.00021202193,0.000064773136],"domain_scores_gemma":[0.997971,0.0015409754,0.00014741726,0.00010601968,0.00017376758,0.000060830676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010262844,0.00051598594,0.00040835072,0.0005853603,0.000888233,0.0047017783,0.0008351535,0.0021935094,0.003858496],"category_scores_gemma":[0.0039538033,0.00033618129,0.00025628263,0.0010736801,0.00526636,0.0053875847,0.0011818903,0.0022594107,0.00039628684],"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":[0.000012307759,0.000029878991,0.002010864,0.00009077427,0.000023936498,0.00017383932,0.0021544832,0.011151881,0.0002856289,0.9206829,0.020448104,0.04293543],"study_design_scores_gemma":[0.0000022781392,0.000016196733,0.0016830475,0.00013764913,0.000008506703,0.000118302545,0.0018575385,0.006952719,0.00011137868,0.91973484,0.069359004,0.000018492841],"about_ca_topic_score_codex":0.004694262,"about_ca_topic_score_gemma":0.007844432,"teacher_disagreement_score":0.0047017783,"about_ca_system_score_codex":0.001633879,"about_ca_system_score_gemma":0.0015869818,"threshold_uncertainty_score":0.012907982},"labels":[],"label_agreement":null},{"id":"W3196864648","doi":"10.1061/(asce)wr.1943-5452.0001466","title":"Discussion of “Hydraulic Analysis of Intermittent Water-Distribution Networks Considering Partial-Flow Regimes” by S. Mohan and G. R. Abhijith","year":2021,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Hudbay Minerals (Canada); University of Toronto","funders":"","keywords":"Flow (mathematics); Hydrology (agriculture); Distribution (mathematics); Environmental science; Water flow; Mechanics; Mathematics; Geology; Environmental engineering; Geotechnical engineering; Physics; Mathematical analysis","score_opus":0.006936338250255515,"score_gpt":0.19564641487092777,"score_spread":0.18871007662067224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196864648","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057078138,0.016835447,0.68190366,0.15887684,0.012501292,0.00016507095,0.0007591562,0.00041808718,0.071462266],"genre_scores_gemma":[0.86442745,0.0128527265,0.048375316,0.014523948,0.013782362,0.0002817813,0.00027978717,0.00017843528,0.045298163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996264,0.00012984835,0.000015717922,0.00006129543,0.00013463518,0.00003199693],"domain_scores_gemma":[0.99937516,0.00038000412,0.000052840445,0.000032911288,0.00012873253,0.000030271454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009884773,0.000373989,0.0005035244,0.00064428285,0.00093150017,0.0012391779,0.0019448948,0.0025818115,0.0036697008],"category_scores_gemma":[0.003010883,0.00026164093,0.0011184601,0.0008740382,0.001764836,0.0023595295,0.0007453692,0.0018243768,0.00033627165],"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":[0.00005333151,0.000045651323,0.0007315596,0.0002939094,0.00006481707,0.0007395469,0.00021484186,0.30169508,0.0025632798,0.6035063,0.06981221,0.02027944],"study_design_scores_gemma":[0.000025971616,0.00006667827,0.001557451,0.00013073326,0.00003955718,0.00025963085,0.00012454385,0.5066559,0.0012864132,0.4175419,0.07226714,0.000044137727],"about_ca_topic_score_codex":0.008482387,"about_ca_topic_score_gemma":0.0066057043,"teacher_disagreement_score":0.008482387,"about_ca_system_score_codex":0.001458784,"about_ca_system_score_gemma":0.0011551231,"threshold_uncertainty_score":0.016866028},"labels":[],"label_agreement":null},{"id":"W3197274838","doi":"10.36909/jer.v9i3a.8857","title":"Comprehensive optimization of project cost for long supply pipelines","year":2021,"lang":"en","type":"article","venue":"Journal of Engineering Research","topic":"Water Systems and 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":"HydraTek (Canada)","funders":"King Saud University","keywords":"Pipeline (software); Pipeline transport; Nominal Pipe Size; Range (aeronautics); Genetic algorithm; Engineering; Computer science; Marine engineering; Reliability engineering; Mathematical optimization; Operations research; Mechanical engineering; Mathematics","score_opus":0.06856206615185727,"score_gpt":0.33385279014999353,"score_spread":0.26529072399813625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197274838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44305575,0.0009802905,0.5360808,0.00043567363,0.000030443432,0.00022319553,0.00047299705,0.0003664837,0.018354446],"genre_scores_gemma":[0.946807,0.00031707573,0.04755902,0.000024296214,0.000010160462,0.00018855683,0.00025155602,0.0001285396,0.004713739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993243,0.0002973631,0.000019048548,0.00007712816,0.00017425463,0.000107808344],"domain_scores_gemma":[0.999278,0.00038939944,0.00009153201,0.00004784867,0.000118294076,0.000074937445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001678181,0.00095730583,0.0010207784,0.001108969,0.00049788365,0.0012869916,0.00069019606,0.0008063475,0.0033339807],"category_scores_gemma":[0.0025359602,0.00075625087,0.00067154557,0.0012043645,0.0005446728,0.0013229443,0.00077027373,0.0006867157,0.00024402773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003663989,0.000020575082,0.0003565025,0.000028561204,0.000012459891,0.000032618445,0.000013080038,0.9887189,0.0004508228,0.0017061866,0.00020742457,0.008416205],"study_design_scores_gemma":[0.0000142788995,0.00016054514,0.00083773024,0.000009314426,0.000020543563,0.000037044243,0.00004666314,0.9940347,0.00084670144,0.0031787877,0.0008027413,0.0000109466655],"about_ca_topic_score_codex":0.00369075,"about_ca_topic_score_gemma":0.0051744916,"teacher_disagreement_score":0.00369075,"about_ca_system_score_codex":0.0017533922,"about_ca_system_score_gemma":0.00188699,"threshold_uncertainty_score":0.012721777},"labels":[],"label_agreement":null},{"id":"W3198663138","doi":"10.1080/1573062x.2021.1971264","title":"Stochastic long-term reliability of water distribution networks using Monte Carlo simulation","year":2021,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and 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":"Jacobs (Canada)","funders":"","keywords":"Monte Carlo method; Reliability (semiconductor); Benchmark (surveying); Probabilistic logic; Term (time); Computer science; Stochastic simulation; Stochastic modelling; Probability distribution; Mathematical optimization; Component (thermodynamics); Time horizon; Reliability engineering; Engineering; Mathematics; Statistics; Artificial intelligence","score_opus":0.01108458976399787,"score_gpt":0.20812950193072102,"score_spread":0.19704491216672315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198663138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044795003,0.000325692,0.9521822,0.00013436859,0.000023488723,0.00003887793,0.00007675172,0.00023905843,0.002184547],"genre_scores_gemma":[0.9312342,0.0003439927,0.066468954,0.000034653083,0.000041531745,0.00016902461,0.00014764568,0.000073849835,0.0014860719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992995,0.000373316,0.00002517859,0.00007236474,0.00016593847,0.00006365771],"domain_scores_gemma":[0.9973718,0.0019666618,0.00022657152,0.00014627202,0.0002310365,0.000057612786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017856123,0.0005307012,0.00077505125,0.00079639326,0.00042970176,0.00080402807,0.00093825994,0.0008651859,0.00096238486],"category_scores_gemma":[0.0046614795,0.00053748634,0.00084665883,0.0007046353,0.0007400538,0.0009638053,0.0005725676,0.00088782364,0.00012849235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004685431,0.000002958369,0.00019298156,0.000004000185,0.000005559621,0.000009182627,0.000005457363,0.99706775,0.00010103014,0.0017414318,0.000024235927,0.00084076845],"study_design_scores_gemma":[6.623668e-7,0.0000017080733,0.00003819002,0.0000010528896,0.0000011051851,0.0000021803319,9.697741e-7,0.9993262,0.000037758557,0.00054976484,0.000039183313,0.0000011611271],"about_ca_topic_score_codex":0.01136667,"about_ca_topic_score_gemma":0.00793347,"teacher_disagreement_score":0.01136667,"about_ca_system_score_codex":0.0011013446,"about_ca_system_score_gemma":0.0010470997,"threshold_uncertainty_score":0.022601008},"labels":[],"label_agreement":null},{"id":"W3199975030","doi":"10.3390/w13182579","title":"Stringency of Water Conservation Determines Drinking Water Quality Trade-Offs: Lessons Learned from a Full-Scale Water Distribution System","year":2021,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"Polytechnique Montréal; Canadian Hydrographic Service; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Environmental science; Water quality; Water conservation; Robustness (evolution); Residual; Water supply; Reliability (semiconductor); Environmental engineering; Water resources; Computer science; Ecology; Chemistry","score_opus":0.03543393757748852,"score_gpt":0.24169644201820767,"score_spread":0.20626250444071914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199975030","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":[0.9900358,0.0000569552,0.0076763947,0.00018419798,0.00000425661,0.000017984808,0.00007943408,0.000045804,0.0018992238],"genre_scores_gemma":[0.9988564,0.000028397957,0.0008131831,0.0000070573524,0.0000017634617,0.000003477836,0.000031186584,0.0000060189864,0.00025245952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998659,0.00004539877,0.0000055430637,0.000038521943,0.00001993885,0.000024689665],"domain_scores_gemma":[0.9988758,0.0007473656,0.000080819475,0.00009245246,0.00014274826,0.00006073122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000493173,0.00026391054,0.00039830134,0.00018736834,0.00025734212,0.00053218286,0.0005494456,0.0004391213,0.0018530708],"category_scores_gemma":[0.0024876217,0.00020351369,0.00034724022,0.00026789063,0.0005432436,0.0011970733,0.00045768294,0.00037166188,0.00011006323],"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":[0.00011681571,0.00012248768,0.017137777,0.000052480213,0.00004702412,0.00023561127,0.00009055902,0.9642544,0.0049529257,0.001406906,0.00032931025,0.011253718],"study_design_scores_gemma":[0.00001956997,0.00013841059,0.009640269,0.000003478709,0.000020889698,0.00002750753,0.00013835938,0.98591554,0.0010990266,0.0027299612,0.00025438136,0.00001262335],"about_ca_topic_score_codex":0.014204606,"about_ca_topic_score_gemma":0.00866035,"teacher_disagreement_score":0.014204606,"about_ca_system_score_codex":0.0007469759,"about_ca_system_score_gemma":0.00042972944,"threshold_uncertainty_score":0.0282439},"labels":[],"label_agreement":null},{"id":"W3200667102","doi":"10.1016/j.watres.2021.117680","title":"Failure modeling of water distribution pipelines using meta-learning algorithms","year":2021,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":56,"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":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Pipeline transport; Urbanization; Environmental science; Residual; Index (typography); Environmental engineering; Pipe network analysis; Engineering; Civil engineering; Computer science; Petroleum engineering; Algorithm","score_opus":0.1052345638775606,"score_gpt":0.31197100132546246,"score_spread":0.20673643744790188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200667102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10549488,0.0006837453,0.890067,0.00036338143,0.00006221856,0.00006443568,0.0002720977,0.0005568082,0.0024354828],"genre_scores_gemma":[0.96399254,0.00022490876,0.03209036,0.00005230385,0.00003753237,0.00010516901,0.0002587186,0.00007710358,0.003161332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995579,0.00016464855,0.000029770714,0.00010461657,0.00006498734,0.000078132056],"domain_scores_gemma":[0.99729925,0.0018632098,0.0002977765,0.00010358072,0.00033842374,0.000097658725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017517245,0.0012063144,0.0019201665,0.0014322774,0.0005492977,0.0015018206,0.002006715,0.0022304177,0.0018483133],"category_scores_gemma":[0.004368357,0.0011343485,0.0016774837,0.0009846034,0.0009972227,0.0014264964,0.0009452799,0.0015100511,0.00031408982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000067562282,0.0000038555236,0.00010905726,0.0000046504233,0.0000063947473,0.000003696819,0.000002704145,0.9986084,0.000022211883,0.00020503227,0.000031510106,0.0009957206],"study_design_scores_gemma":[7.232196e-7,0.0000018932811,0.000016590786,8.2061325e-7,0.0000011664278,6.129368e-7,6.774732e-7,0.9997104,0.000016402799,0.00024011116,0.000010019928,6.0852955e-7],"about_ca_topic_score_codex":0.019377306,"about_ca_topic_score_gemma":0.009301651,"teacher_disagreement_score":0.019377306,"about_ca_system_score_codex":0.001363676,"about_ca_system_score_gemma":0.0013848171,"threshold_uncertainty_score":0.03852898},"labels":[],"label_agreement":null},{"id":"W3201866395","doi":"10.1016/j.knosys.2021.107522","title":"A clustering solution for analyzing residential water consumption patterns","year":2021,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Water Systems and 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":"Artificial Intelligence in Medicine (Canada)","funders":"Australian Research Council; Yarra Valley Water; City West Water","keywords":"Cluster analysis; Computer science; Data mining; Data set; Hierarchical clustering; Set (abstract data type); Profiling (computer programming); Machine learning; Artificial intelligence","score_opus":0.023243057879182856,"score_gpt":0.23906253461553842,"score_spread":0.21581947673635557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201866395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039026193,0.00029714676,0.95566493,0.00025206184,0.000057695357,0.00016988865,0.0009821731,0.001092812,0.0024570425],"genre_scores_gemma":[0.31682166,0.00022307147,0.67610544,0.00010870412,0.000057997095,0.00027745016,0.002371671,0.00015893682,0.0038750113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948186,0.00008584067,0.00003258682,0.00020097029,0.00012981943,0.00006892948],"domain_scores_gemma":[0.9992223,0.00035962288,0.000058530753,0.00006702269,0.0002543122,0.000038232836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065975165,0.001049409,0.0013375571,0.0024975627,0.0010140612,0.0009693155,0.0015766616,0.0016527112,0.002677161],"category_scores_gemma":[0.0024830892,0.0004992485,0.0016329491,0.003244947,0.00037902233,0.00095612305,0.0007828369,0.00074673176,0.0005614678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012835933,0.00021454405,0.0022973707,0.00013397248,0.00021325055,0.00009602354,0.00013546448,0.76859146,0.0019723657,0.0052407454,0.0053384276,0.215638],"study_design_scores_gemma":[0.000008321522,0.000014820068,0.00035176502,0.0000065665577,0.000019030287,0.000016608914,0.000031356856,0.99547416,0.00029025186,0.0033498034,0.00043137252,0.0000059203576],"about_ca_topic_score_codex":0.038217824,"about_ca_topic_score_gemma":0.034516938,"teacher_disagreement_score":0.038217824,"about_ca_system_score_codex":0.0012099071,"about_ca_system_score_gemma":0.0019573662,"threshold_uncertainty_score":0.07599074},"labels":[],"label_agreement":null},{"id":"W3203455084","doi":"10.2166/wpt.2021.094","title":"A review of water quality factors in water main failure prediction models","year":2021,"lang":"en","type":"review","venue":"Water Practice & Technology","topic":"Water Systems and 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":"Water quality; Quality (philosophy); Predictive modelling; Identification (biology); Process (computing); Set (abstract data type); Environmental science; Computer science; Machine learning","score_opus":0.03719746393817485,"score_gpt":0.29736924931296344,"score_spread":0.2601717853747886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203455084","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":[0.0003977721,0.9982735,0.0005936599,0.00016560881,0.00009419356,0.000017855058,0.00007157979,0.000006551186,0.00037929008],"genre_scores_gemma":[0.0035937692,0.9953063,0.00068495393,0.00008267627,0.000079517486,0.000021889637,0.00007852605,0.0000027462024,0.00014965472],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982198,0.0005402452,0.00041110386,0.0002525827,0.0005056544,0.00007066316],"domain_scores_gemma":[0.9899923,0.0072989543,0.0009905885,0.00012715012,0.001500207,0.000090730675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004278619,0.0018166188,0.002407031,0.007201782,0.0003145894,0.0016885399,0.0014331971,0.0010620169,0.0029343476],"category_scores_gemma":[0.010900384,0.00085476943,0.0030109445,0.009023489,0.0003966421,0.0021808208,0.0006219652,0.000857613,0.0006533615],"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":[0.0001346537,0.000121623605,0.0015314615,0.31352574,0.001664703,0.00023737845,0.0002573011,0.0049803043,0.00070524804,0.0050923564,0.01508873,0.65666056],"study_design_scores_gemma":[0.000069718946,0.00081403006,0.011697771,0.31714135,0.012610339,0.0010321122,0.0005318741,0.005432664,0.0019906487,0.0067820805,0.6417038,0.00019365063],"about_ca_topic_score_codex":0.007136516,"about_ca_topic_score_gemma":0.008540878,"teacher_disagreement_score":0.007201782,"about_ca_system_score_codex":0.0015801061,"about_ca_system_score_gemma":0.004592645,"threshold_uncertainty_score":0.02262777},"labels":[],"label_agreement":null},{"id":"W3208226784","doi":"10.1109/isie45552.2021.9576182","title":"Environment Prediction to Enhance the Navigation System of Water Pipeline Inspection Platforms","year":2021,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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","funders":"","keywords":"Pipeline transport; Pipeline (software); Process (computing); Computer science; Real-time computing; Engineering","score_opus":0.0039038087244712165,"score_gpt":0.17018541459563288,"score_spread":0.16628160587116167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208226784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18923403,0.00021389074,0.8044269,0.0002132508,0.00008854823,0.00004575886,0.0001455812,0.0018789377,0.0037531084],"genre_scores_gemma":[0.97525114,0.000059665606,0.02308801,0.000043363543,0.000010274306,0.000023959845,0.00009499548,0.000020617876,0.0014079573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998965,0.000009377864,0.0000041385815,0.000039677805,0.000025962372,0.00002423257],"domain_scores_gemma":[0.9998708,0.000026645153,0.000022729511,0.000011884785,0.000054964283,0.000012964021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001604364,0.0005446616,0.00032903848,0.000218569,0.00022389553,0.00031648457,0.0004271696,0.00035393174,0.000997064],"category_scores_gemma":[0.000531163,0.0002121789,0.00032178825,0.000144212,0.00017425849,0.000486515,0.00049465604,0.00046016154,0.0002581058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020986401,0.00013220667,0.008127976,0.000063536805,0.000031212247,0.00015182164,0.00008537451,0.81397223,0.028047243,0.0010349343,0.001267675,0.14687596],"study_design_scores_gemma":[0.000003007102,0.000038482794,0.0010938677,0.0000026842308,0.000006115463,0.000010091718,0.000005311595,0.996151,0.0022355595,0.00022976684,0.00021946429,0.0000046620858],"about_ca_topic_score_codex":0.009200515,"about_ca_topic_score_gemma":0.007932851,"teacher_disagreement_score":0.009200515,"about_ca_system_score_codex":0.0003561151,"about_ca_system_score_gemma":0.0005583993,"threshold_uncertainty_score":0.018293917},"labels":[],"label_agreement":null},{"id":"W3208248054","doi":"10.3389/frwa.2021.718215","title":"Performance Modeling and Simulation for Water Distribution Networks","year":2021,"lang":"en","type":"article","venue":"Frontiers in Water","topic":"Water Systems and 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":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Benchmarking; Vision; Asset (computer security); Sustainability; Asset management; Environmental economics; Performance indicator; Cash flow; Computer science; Operations research; Business; Finance; Engineering; Economics; Marketing","score_opus":0.006496306395667401,"score_gpt":0.1759886271940362,"score_spread":0.16949232079836882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208248054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047228534,0.0006090495,0.88254726,0.0013467039,0.00015969055,0.00029664068,0.00081436086,0.0008952316,0.066102624],"genre_scores_gemma":[0.85064703,0.0013198247,0.12542707,0.00020663299,0.00009153155,0.0007127686,0.0011274577,0.00025667303,0.020211088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992077,0.00030726197,0.000037338577,0.00008966427,0.00023778649,0.00012035353],"domain_scores_gemma":[0.998486,0.0009005517,0.00016812635,0.00008887715,0.00029611366,0.000060345832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014244093,0.0010529245,0.00084880233,0.0010843731,0.0008272912,0.001848412,0.0015829885,0.0018252052,0.0045048334],"category_scores_gemma":[0.004257857,0.00048632096,0.0010659427,0.0012455964,0.0008258086,0.001696408,0.0011853944,0.0013948109,0.0007066143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047892495,0.000009969913,0.0001958183,0.000007691657,0.0000036526046,0.000011747697,0.000015576948,0.9904322,0.0000718446,0.007849809,0.00016206203,0.001234828],"study_design_scores_gemma":[0.0000022255385,0.0000037666673,0.000036725927,0.0000042260854,0.0000011938675,0.000002857261,0.000008769482,0.9969386,0.000051262134,0.0023178684,0.0006302431,0.0000022908105],"about_ca_topic_score_codex":0.037278883,"about_ca_topic_score_gemma":0.015844803,"teacher_disagreement_score":0.037278883,"about_ca_system_score_codex":0.0026381263,"about_ca_system_score_gemma":0.0020852052,"threshold_uncertainty_score":0.0741238},"labels":[],"label_agreement":null},{"id":"W3212261904","doi":"10.1115/1.4052984","title":"Transferable Pipeline Rupture Detection Using Multiple Artificial Intelligence Classifiers During Transient Operations","year":2021,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Water Systems and 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":"Alberta Energy; University of Calgary","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Pipeline (software); Classifier (UML); Artificial neural network; Pattern recognition (psychology); Operator (biology); Machine learning; Data mining","score_opus":0.017249407072203137,"score_gpt":0.22272737438502127,"score_spread":0.20547796731281814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212261904","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":[0.5558774,0.00021052304,0.43863958,0.00019378419,0.0000888208,0.00010007232,0.00017014354,0.0021525428,0.002567179],"genre_scores_gemma":[0.97625136,0.00003406676,0.023011927,0.000022221915,0.000013810394,0.000025681833,0.00011724983,0.000015621283,0.0005079964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945897,0.00007892568,0.000039967923,0.00014999647,0.00017793523,0.00009433204],"domain_scores_gemma":[0.99867237,0.00043443975,0.00023975984,0.0001407635,0.00043572308,0.00007695211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097517716,0.00068276835,0.0007536846,0.0013609254,0.00028747038,0.00090056466,0.0007185271,0.000832987,0.0007325937],"category_scores_gemma":[0.0031857619,0.00027536755,0.0004902278,0.00064635143,0.00028924938,0.0010387736,0.0007397016,0.0007424502,0.0003165365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077440013,0.0006112257,0.038512304,0.00012232776,0.00015172092,0.0005137371,0.00023515304,0.53013545,0.05580216,0.0009067273,0.0020569798,0.37017784],"study_design_scores_gemma":[0.0000032459518,0.000062169594,0.003840432,0.0000039002575,0.000007919437,0.0000224032,0.000027748465,0.98958963,0.0060774838,0.00021788717,0.0001383015,0.000008808866],"about_ca_topic_score_codex":0.0034069049,"about_ca_topic_score_gemma":0.0021846427,"teacher_disagreement_score":0.0034069049,"about_ca_system_score_codex":0.00055263744,"about_ca_system_score_gemma":0.00051800936,"threshold_uncertainty_score":0.006774187},"labels":[],"label_agreement":null},{"id":"W3212439178","doi":"10.1109/jsen.2021.3128816","title":"A BiLSTM Based Pipeline Leak Detection and Disturbance Assisted Localization Method","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Water Systems and 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":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Leak; Multilateration; Pipeline (software); Computer science; Artificial intelligence; Real-time computing; Engineering; Azimuth; Mathematics","score_opus":0.011963521001868146,"score_gpt":0.22504543988374442,"score_spread":0.21308191888187628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212439178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024931684,0.00061588606,0.9669223,0.00023887769,0.00015263756,0.000060184426,0.00015678447,0.004496389,0.002425362],"genre_scores_gemma":[0.72664046,0.0005581702,0.25943077,0.000479802,0.00012981343,0.00019548467,0.000911738,0.00023259677,0.011421022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966395,0.00002341788,0.000022327265,0.000111387155,0.00012113917,0.000057802714],"domain_scores_gemma":[0.99969316,0.000041321426,0.00003388883,0.000026393454,0.000177007,0.000028376651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044894125,0.0010159541,0.00080582366,0.00064486393,0.00034992365,0.0005861349,0.0013658128,0.00091627485,0.0029743367],"category_scores_gemma":[0.0010154442,0.0004423266,0.0005779801,0.00047091956,0.00032407168,0.0010393803,0.0010060866,0.001237496,0.0014524208],"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":[0.00025463838,0.00021392005,0.0020828862,0.00023618595,0.00010545084,0.0002716997,0.00011329927,0.21240662,0.045708645,0.0030150665,0.007555468,0.7280361],"study_design_scores_gemma":[0.0000094296465,0.000055494675,0.00032797066,0.000009004527,0.000014083791,0.000044105906,0.000008795972,0.99351853,0.004319719,0.0006793487,0.0010047926,0.000008631008],"about_ca_topic_score_codex":0.0066864826,"about_ca_topic_score_gemma":0.006409178,"teacher_disagreement_score":0.0066864826,"about_ca_system_score_codex":0.0005362263,"about_ca_system_score_gemma":0.0012729343,"threshold_uncertainty_score":0.013295114},"labels":[],"label_agreement":null},{"id":"W3213430722","doi":"10.1080/00221686.2021.1944923","title":"Computational fluid dynamics for sub-atmospheric pressure analysis in pipe drainage By Mohsen Besharat, Oscar Enrique Coronado-Hernández, Vicente Samuel Fuertes-Miquel, Maria Teresa Viseu and Helena Margarida Ramos, <i>J. Hydraulic Res</i>. 58(4), 2020, 553–565, 10.1080/00221686.2019.1625819","year":2021,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and 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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drainage; Geology; Closure (psychology); Atmospheric pressure; Humanities; Hydrology (agriculture); Geotechnical engineering; Art; Oceanography","score_opus":0.009163047060475167,"score_gpt":0.2513676202222643,"score_spread":0.24220457316178912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213430722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09229978,0.08275302,0.70309687,0.04252313,0.0072455467,0.00019321106,0.0010107328,0.0012284587,0.06964928],"genre_scores_gemma":[0.7047212,0.07093147,0.14450544,0.0015534412,0.0036275277,0.00037087334,0.0011073721,0.0011345325,0.07204815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996277,0.00010346465,0.000013575065,0.00004646058,0.00018966522,0.000019040128],"domain_scores_gemma":[0.99951756,0.00028839556,0.000036311594,0.000015169497,0.00011439475,0.00002811412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007540531,0.0004854202,0.0004678935,0.00054277136,0.00041627834,0.00084000756,0.00043700312,0.0005496801,0.0024215484],"category_scores_gemma":[0.00231212,0.00033296182,0.0004272806,0.00057087344,0.0007193972,0.00094311277,0.0014274631,0.0013225387,0.0007706871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023858098,0.00011360081,0.0027749455,0.000384328,0.000085977874,0.00021594837,0.00023619823,0.5778969,0.006600242,0.10539143,0.12077817,0.18528362],"study_design_scores_gemma":[0.000017353674,0.000023711382,0.0009222622,0.00009834789,0.000013459167,0.000053869644,0.00004656599,0.9329206,0.0016765204,0.015739243,0.048458938,0.000029267365],"about_ca_topic_score_codex":0.008954074,"about_ca_topic_score_gemma":0.005345636,"teacher_disagreement_score":0.008954074,"about_ca_system_score_codex":0.0006080159,"about_ca_system_score_gemma":0.001569682,"threshold_uncertainty_score":0.017803907},"labels":[],"label_agreement":null},{"id":"W3217181181","doi":"10.3390/jmse9111311","title":"Simulations of the Concentration Fields of Rosette-Type Multiport Buoyant Discharges Using Combined CFD and Multigene Genetic Programming Techniques","year":2021,"lang":"en","type":"article","venue":"Journal of Marine Science and Engineering","topic":"Water Systems and 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":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Rosette (schizont appearance); Computational fluid dynamics; Effluent; Mechanics; Type (biology); Mixing (physics); Marine engineering; Mechanical engineering; Environmental science; Engineering; Physics; Environmental engineering; Biology; Ecology","score_opus":0.008594805624089185,"score_gpt":0.21057174883507732,"score_spread":0.20197694321098814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217181181","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":[0.6486096,0.00036043598,0.33999404,0.00039098144,0.000095825446,0.000117864736,0.00072908954,0.00074376346,0.008958428],"genre_scores_gemma":[0.91420937,0.00023316489,0.08304831,0.00007032954,0.000011155215,0.00015128609,0.00059245306,0.00005556414,0.0016284226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999162,0.000022381297,0.000004436937,0.000018295204,0.000022494796,0.000016194715],"domain_scores_gemma":[0.999721,0.00015846425,0.000036155485,0.000015840948,0.00004848009,0.000020085616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002159694,0.0005993288,0.00036690736,0.00040597122,0.00031030417,0.0005141635,0.00064730353,0.0010517937,0.00077374576],"category_scores_gemma":[0.0006283784,0.00028850685,0.0006897499,0.00044641577,0.0003782167,0.00034688236,0.00048599797,0.00060747087,0.00009990945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007562074,0.000011043931,0.00067283184,0.000009873194,0.000005074444,0.000027603208,0.000007963671,0.9963856,0.0007896992,0.00028447845,0.00006576975,0.0017325731],"study_design_scores_gemma":[0.0000016648465,0.000004027345,0.00011125047,9.4353584e-7,0.0000010116273,0.0000031313316,0.000003283592,0.99941564,0.00031075027,0.00007899469,0.00006791959,0.0000013987255],"about_ca_topic_score_codex":0.016815467,"about_ca_topic_score_gemma":0.010250542,"teacher_disagreement_score":0.016815467,"about_ca_system_score_codex":0.0005284975,"about_ca_system_score_gemma":0.0008977618,"threshold_uncertainty_score":0.033435166},"labels":[],"label_agreement":null},{"id":"W32367240","doi":"10.1109/icetce.2012.201","title":"Design Challenges in Hydropower Systems: Trade-offs and Difficulties in Operation","year":2012,"lang":"en","type":"article","venue":"International Conference on Electric Technology and Civil Engineering","topic":"Water Systems and 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":"Variety (cybernetics); Presumption; Risk analysis (engineering); Litany; Hydroelectricity; Hydropower; Pipeline transport; Quality (philosophy); Hydraulic machinery; Computer science; Microservices; Systems design; Engineering; Systems engineering; Business; Mechanical engineering; Law","score_opus":0.021941335093010023,"score_gpt":0.20878459054060294,"score_spread":0.18684325544759292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W32367240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21237673,0.030924106,0.47859007,0.12101141,0.001678947,0.0013606931,0.00016763582,0.00047304062,0.15341738],"genre_scores_gemma":[0.7586788,0.009169172,0.21601799,0.004999256,0.0005143809,0.0010357964,0.000054949094,0.00034036135,0.00918942],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.93421316,0.044339307,0.003147426,0.0021370414,0.014956084,0.0012069229],"domain_scores_gemma":[0.9164768,0.060144413,0.0050721997,0.0045832507,0.011883718,0.0018396586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051613014,0.0006730946,0.0009008229,0.0015314594,0.0035737897,0.011089963,0.00216278,0.0026229734,0.0031862264],"category_scores_gemma":[0.07464474,0.0008464604,0.00049271394,0.0012736177,0.008312178,0.009535744,0.0037639975,0.0034400602,0.00075094594],"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":[0.00021716535,0.00042760663,0.008662052,0.0075986194,0.00022400242,0.0016459556,0.04570328,0.030850599,0.011586064,0.44257358,0.019145403,0.43136558],"study_design_scores_gemma":[0.0001153866,0.00087799464,0.00779508,0.0033102355,0.00010956219,0.0015527854,0.060671985,0.015431405,0.0061970395,0.57862395,0.32505164,0.00026288445],"about_ca_topic_score_codex":0.0007268726,"about_ca_topic_score_gemma":0.0020501064,"teacher_disagreement_score":0.051613014,"about_ca_system_score_codex":0.004133446,"about_ca_system_score_gemma":0.0061241393,"threshold_uncertainty_score":0.272959},"labels":[],"label_agreement":null},{"id":"W4200111391","doi":"10.3390/w13243546","title":"Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction","year":2021,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Artificial neural network; Particle swarm optimization; Mean squared error; Genetic algorithm; Computer science; Feedforward neural network; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.004969881355836853,"score_gpt":0.1849094257009762,"score_spread":0.17993954434513937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200111391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13826294,0.0009971728,0.85580385,0.0002858,0.0000747424,0.00009698025,0.00006753327,0.00053933315,0.0038716095],"genre_scores_gemma":[0.9540845,0.00031887362,0.04359142,0.000059170205,0.000027677876,0.00014212764,0.00009607542,0.00002506925,0.0016550384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997156,0.000104293365,0.000018671293,0.00005942717,0.000059366223,0.000042580185],"domain_scores_gemma":[0.9988821,0.0008098861,0.00010456678,0.000021089527,0.00015868469,0.000023679728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010242693,0.0009035867,0.00089886005,0.00052668585,0.00034386304,0.00062727235,0.00078996306,0.0009841645,0.00090406893],"category_scores_gemma":[0.002806641,0.00047687645,0.0005382156,0.0005711053,0.00054331997,0.0007328668,0.0005346941,0.0009795453,0.00012830869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017395983,0.000020499723,0.00038960992,0.00001702793,0.000012483685,0.000010695228,0.000009512007,0.9895779,0.00013114276,0.00026220587,0.00007987452,0.009471731],"study_design_scores_gemma":[0.0000016614205,0.0000066139737,0.000043056072,0.0000012382841,0.0000013113596,6.966119e-7,0.0000014665382,0.99976915,0.00004928124,0.00010728563,0.000017526077,6.718605e-7],"about_ca_topic_score_codex":0.025719646,"about_ca_topic_score_gemma":0.014879982,"teacher_disagreement_score":0.025719646,"about_ca_system_score_codex":0.0010338736,"about_ca_system_score_gemma":0.0011873642,"threshold_uncertainty_score":0.05113989},"labels":[],"label_agreement":null},{"id":"W4200294106","doi":"10.11606/d.18.2019.tde-16112021-151456","title":"Métodos de partição de rede de abastecimento de água: um estudo de caso em Pederneiras/SP","year":2019,"lang":"pt","type":"dissertation","venue":"","topic":"Water Systems and 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":"","keywords":"Partition (number theory); Computer science; Modularity (biology); Index (typography); Water supply network; Operations research; Water supply; Data mining; Mathematics; Environmental science; Environmental engineering","score_opus":0.012823169925908432,"score_gpt":0.2486913505175939,"score_spread":0.23586818059168546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200294106","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":[0.9805008,0.00014417636,0.00886795,0.00014085774,0.000013415837,0.00018442477,0.00023276175,0.00018221879,0.009733339],"genre_scores_gemma":[0.9888039,0.00012335154,0.006963522,0.000025399326,0.0000032760875,0.000070063936,0.000086988584,0.000050715615,0.0038726341],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981219,0.00050425594,0.00009886726,0.00041093715,0.0006489398,0.00021504088],"domain_scores_gemma":[0.99641526,0.0014347883,0.00052945735,0.00034911223,0.0010290089,0.00024235861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016525587,0.00056560093,0.00058362744,0.0016329834,0.0016133614,0.0026388995,0.0011867943,0.0013098657,0.0054604085],"category_scores_gemma":[0.007437896,0.00061965315,0.0006611786,0.0016248178,0.0013693613,0.0013226722,0.0016577999,0.00067431293,0.0012032624],"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":[0.00089923484,0.0010317361,0.62229854,0.0011351695,0.00012970183,0.0047141346,0.073118664,0.00494334,0.041721907,0.0041174316,0.002582134,0.24330808],"study_design_scores_gemma":[0.000051590167,0.0016926941,0.7997968,0.00029283244,0.00022323593,0.0036586714,0.11450381,0.01803127,0.025488282,0.0025877873,0.033427324,0.00024582408],"about_ca_topic_score_codex":0.031474564,"about_ca_topic_score_gemma":0.05216114,"teacher_disagreement_score":0.031474564,"about_ca_system_score_codex":0.0014743275,"about_ca_system_score_gemma":0.0016206144,"threshold_uncertainty_score":0.06258273},"labels":[],"label_agreement":null},{"id":"W4200317104","doi":"10.1007/s41742-021-00379-1","title":"Simulation of Chlorine Injection in Water Distribution Networks in Response to Contaminations","year":2021,"lang":"en","type":"article","venue":"International Journal of Environmental Research","topic":"Water Systems and 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":"McMaster University","funders":"","keywords":"Contamination; Water supply; Booster (rocketry); Offset (computer science); Water quality; Environmental science; Chlorine; Computer science; Engineering; Environmental engineering; Chemistry","score_opus":0.0181672786555008,"score_gpt":0.2985001465905275,"score_spread":0.28033286793502665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200317104","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":[0.98071,0.00010100554,0.0103678135,0.0004803055,0.00006249307,0.000053939326,0.0005106301,0.0002515288,0.0074622384],"genre_scores_gemma":[0.9972119,0.00002504121,0.0012235986,0.000029150688,0.0000033959866,0.000017258171,0.00012877697,0.00001488325,0.0013460406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996846,0.0000807813,0.000011958193,0.000055157398,0.0000461034,0.000121483434],"domain_scores_gemma":[0.99736506,0.0018398686,0.00020658274,0.0000757857,0.00031868563,0.00019398566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056878186,0.00062156667,0.0008045442,0.0007817955,0.00055129745,0.0010434496,0.0010508812,0.0021243587,0.003241773],"category_scores_gemma":[0.0029668857,0.0005646836,0.0008600246,0.000691885,0.00087708497,0.00059668923,0.0005760116,0.001025353,0.00016201599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051575666,0.0000283417,0.0010203683,0.0000073828337,0.000006936906,0.000032094194,0.000007958027,0.997982,0.0002679353,0.00019287395,0.00007659129,0.0003260124],"study_design_scores_gemma":[0.00000986255,0.00001863947,0.00020571705,0.000001094881,0.0000031147626,0.0000027272579,0.000010058241,0.9994147,0.00023417012,0.000062837695,0.000034833243,0.0000022415975],"about_ca_topic_score_codex":0.07408272,"about_ca_topic_score_gemma":0.030863091,"teacher_disagreement_score":0.07408272,"about_ca_system_score_codex":0.0019380058,"about_ca_system_score_gemma":0.0016095793,"threshold_uncertainty_score":0.14730304},"labels":[],"label_agreement":null},{"id":"W4200584175","doi":"10.1061/(asce)wr.1943-5452.0001512","title":"Data Mining Algorithms for Water Main Condition Prediction—Comparative Analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Hyperparameter; Decision tree; Computer science; Data mining; Sensitivity (control systems); Machine learning; Support vector machine; AdaBoost; Set (abstract data type); Random forest; Process (computing); Engineering","score_opus":0.04079785253270803,"score_gpt":0.26968025788526273,"score_spread":0.2288824053525547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200584175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40635344,0.060633678,0.5087382,0.003470901,0.00045056955,0.00051747676,0.002104068,0.0019843061,0.015747368],"genre_scores_gemma":[0.7588422,0.0152726555,0.2214936,0.0002409094,0.00013744838,0.00022391626,0.0024009997,0.00008991864,0.0012983767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972947,0.0009845704,0.0003263918,0.00032365942,0.0009506541,0.00012006401],"domain_scores_gemma":[0.98616946,0.010971165,0.00038969764,0.00057527237,0.0017847787,0.00010959751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007309515,0.0010230244,0.0013640026,0.004274519,0.00042738672,0.0013417655,0.0010895287,0.0010109253,0.0008739471],"category_scores_gemma":[0.014511501,0.00027243447,0.0012733696,0.0045072692,0.0002577748,0.0022179182,0.0007041513,0.0009827,0.00029188415],"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":[0.0004940244,0.0005362462,0.024991475,0.00069923745,0.0005456995,0.00009180935,0.0001063438,0.37068617,0.00073999044,0.004893007,0.003854863,0.5923612],"study_design_scores_gemma":[0.000020549685,0.00020210275,0.005885327,0.00008388026,0.00010185728,0.00008042534,0.00010307043,0.987636,0.0011873362,0.0025033723,0.0021784434,0.000017656543],"about_ca_topic_score_codex":0.0066818977,"about_ca_topic_score_gemma":0.0041451394,"teacher_disagreement_score":0.007309515,"about_ca_system_score_codex":0.0012123978,"about_ca_system_score_gemma":0.0011189539,"threshold_uncertainty_score":0.03865683},"labels":[],"label_agreement":null},{"id":"W4205735987","doi":"10.1007/s11269-021-03034-8","title":"Optimal Operational Scheduling of Pumps to Improve the Performance of Water Distribution Networks","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and 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":"Jacobs (Canada)","funders":"","keywords":"Particle swarm optimization; MATLAB; Reliability engineering; Computer science; Reliability (semiconductor); Software; Leakage (economics); Scheduling (production processes); Simulation; Real-time computing; Power (physics); Engineering; Algorithm","score_opus":0.00412070599879554,"score_gpt":0.16565421262630708,"score_spread":0.16153350662751154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205735987","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":[0.5743074,0.0007559967,0.41179883,0.00089054595,0.00035048492,0.0001316507,0.00023365862,0.0005577475,0.010973696],"genre_scores_gemma":[0.99155575,0.00006913972,0.0077641313,0.000018383207,0.000017100148,0.0000119622155,0.00003400933,0.000014729024,0.00051474466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953854,0.00019939311,0.000015042493,0.000061795916,0.00006131209,0.00012388524],"domain_scores_gemma":[0.99917847,0.00045110623,0.00011776982,0.000043183853,0.00013284334,0.000076552686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008680426,0.000537686,0.0006505129,0.00043861364,0.00039650997,0.0009103609,0.0005159481,0.00042282278,0.0015665592],"category_scores_gemma":[0.0030561252,0.00033184508,0.00021793037,0.0004337144,0.0004244652,0.0007577755,0.00033234403,0.00060169026,0.00013852632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014617827,0.00007329557,0.00040930122,0.000021645868,0.000012448185,0.000013036942,0.000013857069,0.9817092,0.0017657807,0.0026997463,0.00063649,0.012499058],"study_design_scores_gemma":[0.000011029399,0.000043335516,0.00017610354,0.000001262651,0.000004929553,0.000001923863,0.0000079027095,0.99782556,0.0007245598,0.0010664121,0.00013476185,0.0000022333852],"about_ca_topic_score_codex":0.0062639336,"about_ca_topic_score_gemma":0.0057353834,"teacher_disagreement_score":0.0062639336,"about_ca_system_score_codex":0.0011102415,"about_ca_system_score_gemma":0.0017171623,"threshold_uncertainty_score":0.012454987},"labels":[],"label_agreement":null},{"id":"W4206011623","doi":"10.47749/t/unicamp.2018.993670","title":"Avaliação da resiliência de adutoras de abastecimento de água considerando efeitos transitórios","year":2018,"lang":"pt","type":"dissertation","venue":"","topic":"Water Systems and 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":"Resilience (materials science); Context (archaeology); Order (exchange); Water hammer; Scarcity; Work (physics); Environmental science; Environmental planning; Business; Transport engineering; Computer science; Engineering; Geography; Finance; Economics","score_opus":0.013572264896126982,"score_gpt":0.24842041626424202,"score_spread":0.23484815136811504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206011623","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":[0.9895111,0.00022752554,0.00747778,0.00004993818,0.000009516518,0.00002987185,0.00018648003,0.00010289177,0.0024048465],"genre_scores_gemma":[0.9974535,0.00009855459,0.0018720613,0.0000031389777,0.0000030446133,0.00001038525,0.000087971326,0.000010243165,0.00046094292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946004,0.000104692255,0.000027983415,0.0001361872,0.00016618494,0.00010490884],"domain_scores_gemma":[0.99705553,0.0015660076,0.0004751181,0.00022391211,0.00056255545,0.00011694292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014963761,0.0004208423,0.00045547736,0.001303718,0.00039178212,0.0009791272,0.00038601743,0.00044453458,0.0015186686],"category_scores_gemma":[0.0063296235,0.00017148176,0.0006275205,0.0008790419,0.00053244166,0.00074316014,0.00049699715,0.00041809463,0.00030942526],"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":[0.0017176047,0.0006419447,0.32199028,0.0006279317,0.00040614177,0.00064923253,0.0035959887,0.23135465,0.10233979,0.004776401,0.0014149444,0.33048508],"study_design_scores_gemma":[0.000038803002,0.0032740415,0.39170128,0.000117432566,0.00032255068,0.00060354377,0.0039863796,0.52540594,0.063943446,0.0051439274,0.0053302823,0.00013232794],"about_ca_topic_score_codex":0.004967451,"about_ca_topic_score_gemma":0.0042621833,"teacher_disagreement_score":0.004967451,"about_ca_system_score_codex":0.00068926165,"about_ca_system_score_gemma":0.00033241155,"threshold_uncertainty_score":0.009877086},"labels":[],"label_agreement":null},{"id":"W4210711026","doi":"10.1115/imece2021-69330","title":"Innovation in Hydrotesting Above Ground Pipes: Analytical Solution via Integral Transforms for Discerning Test Fluid Temperatures Subject to Ambient Temperature Variations","year":2021,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Nova Chemicals (Canada)","funders":"","keywords":"Biot number; Transient (computer programming); Work (physics); Mechanics; Pipeline transport; Hydrostatic equilibrium; Integral transform; Temperature measurement; Materials science; Thermodynamics; Mathematics; Computer science; Mathematical analysis; Physics; Engineering; Mechanical engineering","score_opus":0.011296012010546917,"score_gpt":0.2279119175598,"score_spread":0.21661590554925308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210711026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03734095,0.00022945517,0.9568756,0.00016871511,0.000029335963,0.000056532856,0.000044176217,0.00019326132,0.0050620637],"genre_scores_gemma":[0.82903194,0.00062105735,0.16278984,0.00009134539,0.000038380404,0.00015842664,0.00009138869,0.00021435553,0.006963229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997744,0.00006193228,0.0000105029685,0.000034430355,0.00008622234,0.000032589647],"domain_scores_gemma":[0.99918073,0.00053484144,0.00010013956,0.00004094021,0.000118664226,0.000024636396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010520488,0.00056722865,0.0005989984,0.00052011973,0.00026625922,0.0010183734,0.00072929903,0.0011390346,0.0017350982],"category_scores_gemma":[0.0030282494,0.0003817529,0.00079033204,0.0004367784,0.0011944511,0.0009863827,0.0008742056,0.0012731423,0.00031307602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003651217,0.00005050514,0.00066630647,0.00014830843,0.000011726429,0.00018863818,0.00017500053,0.945434,0.008582628,0.024908982,0.0003860768,0.019411359],"study_design_scores_gemma":[0.0000011834206,0.0000051562515,0.000030047926,0.0000045373895,0.0000010976158,0.000010576339,0.000010665128,0.99809736,0.00057743373,0.001102151,0.00015764737,0.0000021919943],"about_ca_topic_score_codex":0.0035436787,"about_ca_topic_score_gemma":0.0019623057,"teacher_disagreement_score":0.0035436787,"about_ca_system_score_codex":0.00069645036,"about_ca_system_score_gemma":0.001324755,"threshold_uncertainty_score":0.0070461035},"labels":[],"label_agreement":null},{"id":"W4211097443","doi":"10.1061/(asce)ir.1943-4774.0001662","title":"Discussion of “Development of a New Practical Formula for Pipe-Sizing Problems within the Framework of a Hybrid Computational Strategy” by Kaan Yetilmezsoy, Majid Bahramian, Emel Kıyan, and Mojtaba Bahramian","year":2022,"lang":"en","type":"article","venue":"Journal of Irrigation and Drainage Engineering","topic":"Water Systems and 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":"Toronto Metropolitan University","funders":"","keywords":"Sizing; Computer science; Management science; Epistemology; Philosophy; Engineering; Chemistry","score_opus":0.011494430643654097,"score_gpt":0.2278169461880926,"score_spread":0.2163225155444385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211097443","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003447317,0.00068466086,0.97299236,0.0078075314,0.001164227,0.00006288032,0.000069963804,0.00009690306,0.013674081],"genre_scores_gemma":[0.18449211,0.0016489363,0.7807793,0.0044210684,0.0023184852,0.00053723226,0.00020139814,0.00034485536,0.025256544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999281,0.00034575662,0.0000420807,0.000085185915,0.00020553617,0.000040565716],"domain_scores_gemma":[0.9984011,0.0010421708,0.000068710375,0.00011322711,0.000329601,0.000045281366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025091723,0.0009176841,0.00070621376,0.0006869523,0.00082754437,0.0013631546,0.0026284356,0.002426054,0.008663396],"category_scores_gemma":[0.008349175,0.0005074989,0.0013062516,0.00077920005,0.0018644655,0.003271183,0.0016585657,0.0037823352,0.0010866693],"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":[0.000033647677,0.000039026814,0.00019386718,0.0001678853,0.00003764909,0.0001389203,0.000116130606,0.11805867,0.0013026828,0.84473294,0.0146188475,0.020559637],"study_design_scores_gemma":[0.000022440552,0.000029822952,0.00013756473,0.000051283576,0.000014923789,0.00006363941,0.00004619712,0.68814754,0.00077488384,0.28853482,0.022150625,0.00002634832],"about_ca_topic_score_codex":0.0038320937,"about_ca_topic_score_gemma":0.004168322,"teacher_disagreement_score":0.008663396,"about_ca_system_score_codex":0.0010577675,"about_ca_system_score_gemma":0.0013510289,"threshold_uncertainty_score":0.028981924},"labels":[],"label_agreement":null},{"id":"W4211219420","doi":"10.1080/00221686.2021.2001589","title":"Numerical study on the mechanisms of storm geysers in a vertical riser-chamber system","year":2022,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and 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":"Suncor Energy (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inflow; Pressure drop; Mechanics; Airflow; Environmental science; Geology; Surge; Flow (mathematics); Volume (thermodynamics); Materials science; Geotechnical engineering; Hydrology (agriculture); Geomorphology; Engineering; Mechanical engineering","score_opus":0.0482966985225251,"score_gpt":0.29937121348904067,"score_spread":0.25107451496651556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211219420","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":[0.97300154,0.00020318676,0.020072632,0.0002191302,0.000045310426,0.00008452905,0.00025195672,0.000191071,0.0059305965],"genre_scores_gemma":[0.99307245,0.0000913528,0.005292006,0.000023662831,0.000006545519,0.000042420972,0.000089588095,0.000016210284,0.0013658267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978966,0.000045240675,0.00001196424,0.000044004602,0.00004571929,0.00006339316],"domain_scores_gemma":[0.99959606,0.00018826063,0.000065791464,0.000026835338,0.00006572518,0.000057252702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003442726,0.0005602463,0.0008344861,0.00047432518,0.00093397294,0.00089360785,0.00084250426,0.0015753178,0.0016440206],"category_scores_gemma":[0.00080247,0.0003896785,0.00068612857,0.00035285496,0.00084282504,0.0006541674,0.0007234711,0.00064973783,0.00014987584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011588812,0.00010331049,0.0042224247,0.00006357513,0.000018006582,0.0003251415,0.00008938323,0.98361903,0.008294561,0.0010478693,0.00019724724,0.0019035445],"study_design_scores_gemma":[0.000015722559,0.000047477704,0.0005660958,0.0000029739717,0.0000057009192,0.000012962573,0.00003452183,0.99818534,0.0009236728,0.00007560761,0.00012240942,0.0000074575432],"about_ca_topic_score_codex":0.020453932,"about_ca_topic_score_gemma":0.008592522,"teacher_disagreement_score":0.020453932,"about_ca_system_score_codex":0.0008859028,"about_ca_system_score_gemma":0.0010903192,"threshold_uncertainty_score":0.04066974},"labels":[],"label_agreement":null},{"id":"W4213374262","doi":"10.3390/en15041526","title":"The Impact of Water Hammer on Hydraulic Power Units","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Water Systems and 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":"Université du Québec à Trois-Rivières; Hydro-Québec","funders":"","keywords":"Water hammer; Overpressure; Hydropower; Closure (psychology); Hydraulic machinery; Hydraulic structure; Transient (computer programming); Hydraulics; Environmental science; Engineering; Mechanics; Geotechnical engineering; Computer science; Mechanical engineering","score_opus":0.007770794926285383,"score_gpt":0.1921939917607903,"score_spread":0.18442319683450492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213374262","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":[0.9596147,0.003990482,0.023422088,0.00018084308,0.00007690832,0.0000656165,0.0003033185,0.00029485064,0.012051263],"genre_scores_gemma":[0.9970126,0.0010874346,0.0009399083,0.000014080302,0.000012501193,0.0000083521345,0.00006925996,0.000014603129,0.0008412854],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988998,0.0002076001,0.00006999047,0.00012618222,0.00060084724,0.000095450654],"domain_scores_gemma":[0.9980259,0.0012827382,0.0002951502,0.0000845617,0.0002598962,0.000051874158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000798249,0.00038835124,0.0003993587,0.00089936284,0.00033613943,0.0010705558,0.00059066457,0.0006518976,0.0018118728],"category_scores_gemma":[0.0033437824,0.0002457197,0.0002850278,0.0010019682,0.0007140365,0.0013433415,0.0006870823,0.0003609608,0.00037615027],"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":[0.000837674,0.00016161446,0.07676115,0.0036264888,0.00017243471,0.006063503,0.0011819283,0.30344138,0.10031279,0.005079485,0.002737707,0.49962384],"study_design_scores_gemma":[0.00006786354,0.005054327,0.3601746,0.0010727905,0.0004903772,0.0029495717,0.005609346,0.3420084,0.24545503,0.010818545,0.025951711,0.0003474454],"about_ca_topic_score_codex":0.0018975305,"about_ca_topic_score_gemma":0.0028685562,"teacher_disagreement_score":0.0018975305,"about_ca_system_score_codex":0.00072498294,"about_ca_system_score_gemma":0.0004131487,"threshold_uncertainty_score":0.006061256},"labels":[],"label_agreement":null},{"id":"W4214806825","doi":"10.1007/s10661-022-09874-0","title":"Staged energy and water quality optimization for large water distribution systems","year":2022,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Water quality; Scheduling (production processes); Computer science; Schedule; Hydraulics; Environmental science; Process engineering; Engineering; Operations management","score_opus":0.008972331174197561,"score_gpt":0.22241927973484849,"score_spread":0.2134469485606509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214806825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33671016,0.00067759247,0.6430671,0.00043109438,0.00007673703,0.00029116886,0.0006361023,0.00047704714,0.017632933],"genre_scores_gemma":[0.96690583,0.00011659938,0.02679305,0.00003564946,0.000012264336,0.00011772465,0.00015059998,0.00007316664,0.0057951743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997025,0.00012716779,0.000007821714,0.000038001202,0.00007439394,0.0000500283],"domain_scores_gemma":[0.9994479,0.00038865104,0.000042065014,0.000027532767,0.000070441485,0.000023497641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007730935,0.0006603071,0.000868888,0.000446612,0.00038865476,0.00094373606,0.0006910484,0.00067443924,0.0042148973],"category_scores_gemma":[0.0016809361,0.0007073544,0.00072898035,0.0005931923,0.00052009174,0.00106099,0.00055909134,0.00071347744,0.00019234557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053290587,0.00001865283,0.00012727219,0.000021750086,0.000009861745,0.000013034734,0.0000070896126,0.9947994,0.00073742605,0.00073709246,0.00016422627,0.0033108545],"study_design_scores_gemma":[0.0000084602825,0.00004305329,0.00018924958,0.0000016955876,0.0000038781122,0.0000026115915,0.0000044956137,0.9980946,0.00043440814,0.0010677087,0.00014716623,0.0000027245596],"about_ca_topic_score_codex":0.009347448,"about_ca_topic_score_gemma":0.013660273,"teacher_disagreement_score":0.009347448,"about_ca_system_score_codex":0.0014138725,"about_ca_system_score_gemma":0.0010881609,"threshold_uncertainty_score":0.01858604},"labels":[],"label_agreement":null},{"id":"W4220912314","doi":"10.1080/0305215x.2022.2044030","title":"Simulation-based optimization of pump scheduling for drinking water distribution systems","year":2022,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Water Systems and 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 Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Scheduling (production processes); Computer science; Grid; Sizing; Meta heuristic; Benchmark (surveying); Schematic; Engineering; Mathematics","score_opus":0.007937142143298012,"score_gpt":0.1897989032275559,"score_spread":0.1818617610842579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220912314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3408916,0.0011452861,0.6294379,0.000972274,0.00018150512,0.00022898318,0.00063768297,0.00062031194,0.025884474],"genre_scores_gemma":[0.9672267,0.00026251955,0.029622784,0.00005187381,0.000015834896,0.0001117709,0.00019347492,0.00004395463,0.0024711397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966526,0.00018869698,0.000009958186,0.000029699411,0.000048668047,0.000057724126],"domain_scores_gemma":[0.9988741,0.0008749473,0.000079560654,0.000028768965,0.000086984524,0.000055542238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007343045,0.00072595634,0.0009171412,0.0005732671,0.00037662353,0.0007571948,0.00054004375,0.0008779617,0.0024157993],"category_scores_gemma":[0.0018387553,0.00054804736,0.00067945005,0.0006758947,0.0005510293,0.00044305963,0.00057152484,0.00073791464,0.0001413794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000102210615,0.0000048954043,0.00005821239,0.000005529204,0.0000033277397,0.0000032899522,0.0000018493051,0.99906796,0.0000584903,0.00026010876,0.000034263856,0.0004918827],"study_design_scores_gemma":[0.000003699675,0.000005078672,0.000028993381,9.2746706e-7,0.0000011884518,6.1202286e-7,0.0000017807832,0.99964297,0.000057075595,0.00018574025,0.00007129396,7.6096615e-7],"about_ca_topic_score_codex":0.018275678,"about_ca_topic_score_gemma":0.014603403,"teacher_disagreement_score":0.018275678,"about_ca_system_score_codex":0.0012530826,"about_ca_system_score_gemma":0.0018322783,"threshold_uncertainty_score":0.036338627},"labels":[],"label_agreement":null},{"id":"W4221010027","doi":"10.1016/j.measurement.2022.111065","title":"A novel leak localization method using forward and backward transient characteristics","year":2022,"lang":"en","type":"article","venue":"Measurement","topic":"Water Systems and 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 Alberta","funders":"","keywords":"Transient (computer programming); Leak; Leak detection; Computer science; Control theory (sociology); Environmental science; Artificial intelligence; Environmental engineering; Programming language","score_opus":0.046013335370355374,"score_gpt":0.22444978178631153,"score_spread":0.17843644641595616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221010027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001677814,0.00004826216,0.99754936,0.000028224025,0.000023867182,0.000011174977,0.000009322424,0.00018371636,0.00046835857],"genre_scores_gemma":[0.12294007,0.0002536342,0.8699758,0.00010713207,0.00008872207,0.00011924385,0.00011045168,0.00016419268,0.0062406957],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997036,0.00005487554,0.000014651819,0.00007258466,0.00013667048,0.000017587548],"domain_scores_gemma":[0.99967396,0.00009370508,0.00003835554,0.000040740735,0.0001312427,0.000021986447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036741543,0.00085624284,0.00079488003,0.0006044155,0.00042417904,0.00064895407,0.0007616505,0.0009858772,0.0023842654],"category_scores_gemma":[0.00078604446,0.00043458512,0.0005511138,0.00065966113,0.0004383796,0.001538062,0.0010274486,0.0008054747,0.0010787479],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035017822,0.00016674299,0.0010690014,0.0005614772,0.00011997523,0.00018207534,0.00023704133,0.07483508,0.28525713,0.022243597,0.004411351,0.6105663],"study_design_scores_gemma":[0.000039557744,0.00009407735,0.00033472272,0.000016510012,0.000037449518,0.00020057474,0.000026740083,0.9603614,0.030731685,0.0029313215,0.005179106,0.000046898185],"about_ca_topic_score_codex":0.0013314113,"about_ca_topic_score_gemma":0.0014930668,"teacher_disagreement_score":0.0023842654,"about_ca_system_score_codex":0.00028182575,"about_ca_system_score_gemma":0.0008267542,"threshold_uncertainty_score":0.007976174},"labels":[],"label_agreement":null},{"id":"W4221052882","doi":"10.1139/er-2021-0046","title":"Criteria-based critical review of artificial intelligence applications in water-leak management","year":2022,"lang":"en","type":"article","venue":"Environmental Reviews","topic":"Water Systems and Optimization","field":"Engineering","cited_by":28,"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; Leak; Leak detection; Domain (mathematical analysis); Bridge (graph theory); Artificial intelligence; Data mining; Data science; Engineering","score_opus":0.025475102676556585,"score_gpt":0.2578245393694601,"score_spread":0.2323494366929035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221052882","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":[0.00034860554,0.9930286,0.0014928128,0.0017982381,0.00050022185,0.000112709255,0.00010351299,0.000014197606,0.002601092],"genre_scores_gemma":[0.004217503,0.9915064,0.0024754878,0.0008728113,0.00028523276,0.00015784168,0.00012563851,0.000008221486,0.00035088914],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9917572,0.0029305073,0.0023181534,0.0005604873,0.0022181342,0.00021542436],"domain_scores_gemma":[0.9582383,0.02963347,0.0024557381,0.0005721596,0.008752756,0.00034765323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010168089,0.0015902574,0.00275733,0.019581309,0.0010463648,0.004126329,0.0021019862,0.0019873315,0.0031508475],"category_scores_gemma":[0.03054074,0.00074355083,0.0024080067,0.019170484,0.0018804149,0.0042685405,0.0016839379,0.0019384993,0.00085847965],"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":[0.0001078801,0.000082129176,0.00083123724,0.29169473,0.0006440682,0.00039037227,0.0015072576,0.0016522964,0.0011011916,0.03403028,0.03416502,0.63379365],"study_design_scores_gemma":[0.000026284046,0.00023709303,0.0027786184,0.26106614,0.0016378919,0.0006058593,0.0012477334,0.0008080182,0.0009270157,0.014925439,0.71564066,0.00009929423],"about_ca_topic_score_codex":0.00663533,"about_ca_topic_score_gemma":0.010188044,"teacher_disagreement_score":0.019581309,"about_ca_system_score_codex":0.0054883403,"about_ca_system_score_gemma":0.013330114,"threshold_uncertainty_score":0.053774655},"labels":[],"label_agreement":null},{"id":"W4224218418","doi":"10.1002/opfl.1663","title":"Mobile DMA Unit Provides a Proactive Leakage Management Strategy","year":2022,"lang":"en","type":"article","venue":"Opflow","topic":"Water Systems and 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":"","keywords":"Benchmarking; Unit (ring theory); Leakage (economics); Unit testing; Computer science; Operations management; Business; Reliability engineering; Engineering; Operating system; Psychology; Marketing","score_opus":0.011776575976040466,"score_gpt":0.1978047771621041,"score_spread":0.18602820118606364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224218418","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":[0.41163135,0.00087426393,0.23104645,0.0046493537,0.00046598096,0.002769786,0.003042894,0.021372529,0.32414743],"genre_scores_gemma":[0.8807059,0.0001975397,0.03696551,0.00052401726,0.000067119,0.00038778965,0.00070155173,0.00029460632,0.080156066],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952054,0.000066079,0.00001746017,0.000055609726,0.00023469998,0.000105543506],"domain_scores_gemma":[0.9988921,0.000111750094,0.000101895275,0.00016168435,0.0005575536,0.00017502249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006775454,0.00044984344,0.00041296284,0.0012599372,0.0008971932,0.0014155734,0.0010884062,0.0005784505,0.0199664],"category_scores_gemma":[0.0013999075,0.00017182874,0.00023483962,0.0009400433,0.00028852996,0.00093636574,0.0013280716,0.00044019066,0.0048405305],"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":[0.00048023832,0.0005919518,0.028976608,0.00023556681,0.000030358016,0.0002953047,0.0005186705,0.012276034,0.04739746,0.0065598623,0.06441174,0.83822626],"study_design_scores_gemma":[0.00041617407,0.0035292679,0.049880143,0.00025065057,0.000093975235,0.0010622594,0.0025100259,0.089172915,0.05805017,0.0038030343,0.79103404,0.00019735559],"about_ca_topic_score_codex":0.011912238,"about_ca_topic_score_gemma":0.025234582,"teacher_disagreement_score":0.0199664,"about_ca_system_score_codex":0.0010030904,"about_ca_system_score_gemma":0.0026806784,"threshold_uncertainty_score":0.06679422},"labels":[],"label_agreement":null},{"id":"W4224879627","doi":"10.1016/j.watres.2022.118480","title":"Comparing quantitative probability of occurrence to a risk matrix approach: A study of chlorine residual data","year":2022,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and 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":"Dalhousie University","funders":"","keywords":"Risk assessment; Risk management; Matrix (chemical analysis); Residual; Statistics; Risk analysis (engineering); Computer science; Data mining; Data Matrix; Reliability engineering; Engineering; Mathematics; Algorithm; Chemistry","score_opus":0.30302839415711735,"score_gpt":0.390153396217937,"score_spread":0.08712500206081963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224879627","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":[0.91205186,0.00025837138,0.08564931,0.00014264922,0.000011968398,0.00004501058,0.0003422783,0.0001610316,0.0013375725],"genre_scores_gemma":[0.98822314,0.000047577476,0.011112495,0.000009561242,0.00000661413,0.000012779721,0.00028422126,0.000031019455,0.00027261392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9970499,0.0018212376,0.000112086986,0.00028784384,0.00060754805,0.00012131103],"domain_scores_gemma":[0.91137546,0.082357556,0.0016996257,0.0020986577,0.0022108424,0.00025785543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00855055,0.00032364717,0.0005271892,0.0021452622,0.00030314387,0.0012463862,0.0010846835,0.0007883121,0.0013294511],"category_scores_gemma":[0.030562168,0.00018498147,0.0007556169,0.0018889498,0.00063327537,0.0016921923,0.0004742463,0.0007352606,0.000087448025],"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":[0.0014532394,0.00043029466,0.03846011,0.00032246768,0.00035735877,0.00020820314,0.00028071678,0.90015775,0.004618726,0.014343424,0.00083040795,0.0385373],"study_design_scores_gemma":[0.000018176775,0.00029952623,0.008741493,0.000007992205,0.000039440358,0.000073636664,0.00012791282,0.9852962,0.0014275173,0.0037457854,0.00019925044,0.000023011675],"about_ca_topic_score_codex":0.007357982,"about_ca_topic_score_gemma":0.003906289,"teacher_disagreement_score":0.00855055,"about_ca_system_score_codex":0.0009620956,"about_ca_system_score_gemma":0.0006033737,"threshold_uncertainty_score":0.045220196},"labels":[],"label_agreement":null},{"id":"W4225794124","doi":"10.1109/tase.2022.3163407","title":"Combined Dual-Prediction Based Data Fusion and Enhanced Leak Detection and Isolation Method for WSN Pipeline Monitoring System","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":23,"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":"Leak; Wireless sensor network; Real-time computing; Computer science; Transmission (telecommunications); Sensor fusion; Pipeline (software); Data transmission; Isolation (microbiology); Electric power transmission; Fusion center; Pipeline transport; Engineering; Wireless; Artificial intelligence; Computer network; Telecommunications","score_opus":0.01769131416949927,"score_gpt":0.23826394013739574,"score_spread":0.22057262596789648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225794124","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":[0.04435597,0.00041374643,0.95294553,0.00019082068,0.000070755996,0.00004075605,0.000051192965,0.00097328617,0.0009579318],"genre_scores_gemma":[0.87988204,0.0003003194,0.11800247,0.00012219608,0.000039140188,0.000073648385,0.00013151125,0.000033127806,0.0014155568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936336,0.00006557222,0.000054668275,0.00018275021,0.0002739437,0.00005970931],"domain_scores_gemma":[0.9996164,0.00007962433,0.00006427699,0.00005709498,0.0001608982,0.00002175702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000578515,0.00070711755,0.000794897,0.0004983192,0.00040858221,0.00048722138,0.00092310755,0.00059266674,0.00057980424],"category_scores_gemma":[0.0012025887,0.0002775688,0.0005096355,0.00047344144,0.00027704795,0.0015269897,0.0010608966,0.0007875399,0.00019230926],"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":[0.0007804528,0.0003111623,0.007577353,0.0003874395,0.00012498215,0.00038496204,0.00047759374,0.22520746,0.14591621,0.0028580735,0.0027459392,0.6132283],"study_design_scores_gemma":[0.000013984353,0.00014075194,0.0010798508,0.000010659666,0.000020060528,0.000108508255,0.000033211458,0.96820486,0.028585209,0.00081410544,0.00096723385,0.000021646812],"about_ca_topic_score_codex":0.0018414853,"about_ca_topic_score_gemma":0.0015006069,"teacher_disagreement_score":0.0018414853,"about_ca_system_score_codex":0.00040446376,"about_ca_system_score_gemma":0.00056207017,"threshold_uncertainty_score":0.0036615133},"labels":[],"label_agreement":null},{"id":"W4226025457","doi":"10.2139/ssrn.4092477","title":"Development of a Dream-Based Inverse Model for Multi-Point Source Identification in River Pollution Incidents: Model Testing and Uncertainty Analysis","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Identification (biology); Point source pollution; Source model; Nonpoint source pollution; Environmental science; River pollution; Point (geometry); Econometrics; Computer science; Pollution; Inverse; Statistics; Mathematics; Ecology","score_opus":0.02262891544755217,"score_gpt":0.22799316820700236,"score_spread":0.2053642527594502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226025457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011397878,0.00007678696,0.9855158,0.00010659257,0.000021702754,0.000039835595,0.00014761095,0.0005615682,0.0021322255],"genre_scores_gemma":[0.7387102,0.00021492288,0.2533162,0.000116946416,0.000023994162,0.0003530752,0.0008831684,0.00016081237,0.0062207375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974936,0.000060867053,0.000021654707,0.000060216877,0.000079607584,0.000028219423],"domain_scores_gemma":[0.99938154,0.00023437034,0.00004712718,0.00006355108,0.00025175317,0.000021573065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008041296,0.0005199232,0.0007990973,0.00045982786,0.0003603306,0.00088215235,0.0013732767,0.0009981913,0.0020695678],"category_scores_gemma":[0.001612163,0.00054777367,0.00086681027,0.0003229207,0.00044288335,0.0009157717,0.0012624508,0.0010866822,0.00070288783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051652027,0.000024653567,0.00063157233,0.000080938225,0.000025241916,0.000047256253,0.000044288397,0.9756984,0.0019125055,0.0039125085,0.00039708897,0.017173927],"study_design_scores_gemma":[0.0000031995678,0.000009639355,0.00005798,0.000002407939,0.0000030059166,0.000008194894,0.0000051509323,0.99858356,0.0004451055,0.0005866407,0.00029176552,0.000003321454],"about_ca_topic_score_codex":0.01032555,"about_ca_topic_score_gemma":0.0072426377,"teacher_disagreement_score":0.01032555,"about_ca_system_score_codex":0.00052095036,"about_ca_system_score_gemma":0.0016021178,"threshold_uncertainty_score":0.02053088},"labels":[],"label_agreement":null},{"id":"W4226107956","doi":"10.1039/d2ew00010e","title":"Initial stages of particulate iron oxide attachment on drinking water PVC pipes characterized by turbidity data and brightfield microscopy from a full-scale laboratory","year":2022,"lang":"en","type":"article","venue":"Environmental Science Water Research & Technology","topic":"Water Systems and 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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Foundation for Innovation","keywords":"Turbidity; Materials science; Microscopy; Particulates; Optical microscope; Particle (ecology); Nanotechnology; Composite material; Optics; Geology; Chemistry; Scanning electron microscope; Physics; Oceanography","score_opus":0.018063016617878368,"score_gpt":0.27755710088407626,"score_spread":0.2594940842661979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226107956","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":[0.99172217,0.0001636321,0.005507334,0.000025901423,0.0000059518466,0.000043548986,0.00017220573,0.00007183479,0.0022874346],"genre_scores_gemma":[0.9913674,0.0001981484,0.004028971,0.000022284212,0.00000387942,0.00004240573,0.00020959081,0.000025833406,0.0041014976],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998449,0.000013520927,0.000004699091,0.000032082644,0.00006335538,0.000041455787],"domain_scores_gemma":[0.999666,0.00012449325,0.000048813763,0.000017438546,0.00009975459,0.000043498625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029833053,0.00022890791,0.0002470842,0.0003380197,0.00041934958,0.0003714791,0.00022071639,0.0002761029,0.001640547],"category_scores_gemma":[0.0004826673,0.00031127097,0.0001893186,0.00018866807,0.0003826547,0.0003697403,0.00028405094,0.00065885537,0.00047415387],"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":[0.000110857094,0.000020040428,0.002575023,0.0000249376,0.0000029531484,0.000048062528,0.00019198938,0.00013944367,0.99500835,0.00008505028,0.000055570526,0.0017376833],"study_design_scores_gemma":[0.000007112261,0.00040470433,0.06313456,0.000008388295,0.000008452135,0.00006933404,0.00032714376,0.0025598817,0.9323193,0.00007858839,0.0010666725,0.000015816242],"about_ca_topic_score_codex":0.005916022,"about_ca_topic_score_gemma":0.0057957773,"teacher_disagreement_score":0.005916022,"about_ca_system_score_codex":0.00041613195,"about_ca_system_score_gemma":0.0002967714,"threshold_uncertainty_score":0.011763215},"labels":[],"label_agreement":null},{"id":"W4229016231","doi":"10.1088/1742-6596/2217/1/012027","title":"Experimental transient cavitation bubbles in a conical-frustum shaped tube","year":2022,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Water Systems and 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":"Frustum; Cavitation; Conical surface; Mechanics; Bubble; Materials science; Tube (container); Oscillation (cell signaling); Nucleation; Transient (computer programming); Composite material; Physics; Chemistry; Mechanical engineering; Engineering; Thermodynamics","score_opus":0.018242998105471437,"score_gpt":0.2209852980319978,"score_spread":0.20274229992652634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229016231","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":[0.99606067,0.000072234965,0.0032478455,0.000023402288,0.000009399494,0.000020412539,0.000090541274,0.000066642315,0.0004088134],"genre_scores_gemma":[0.9983114,0.000025805879,0.0013026568,0.0000047570793,0.0000029977837,0.000020789286,0.000061039624,0.0000089194045,0.00026164623],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997781,0.000024827175,0.00001043643,0.0000615479,0.00006621344,0.000058746056],"domain_scores_gemma":[0.9995683,0.00017219428,0.00009083248,0.000045853114,0.000077565506,0.000045284607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002448808,0.00022580051,0.00021749568,0.00016164996,0.00033118643,0.00017501414,0.00041430845,0.00042080967,0.0011932782],"category_scores_gemma":[0.0005307867,0.00014479292,0.00017491836,0.0002156963,0.0004580325,0.0002808221,0.0002938521,0.0004112155,0.00011107603],"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":[0.00012378588,0.00008355849,0.0007435628,0.00003781479,0.0000064342753,0.00017960994,0.000120015946,0.0014219419,0.99589765,0.00042143324,0.0000981818,0.0008659846],"study_design_scores_gemma":[0.000035189085,0.0008458579,0.0040232525,0.000005462584,0.000011629609,0.000088617926,0.000061170605,0.029982556,0.9643333,0.00010231417,0.00049250945,0.00001819943],"about_ca_topic_score_codex":0.0011857355,"about_ca_topic_score_gemma":0.0005782317,"teacher_disagreement_score":0.0011932782,"about_ca_system_score_codex":0.00028244682,"about_ca_system_score_gemma":0.00013392731,"threshold_uncertainty_score":0.0039919615},"labels":[],"label_agreement":null},{"id":"W4234371023","doi":"10.31224/osf.io/yp2j7","title":"Critical Flow Centrality Measures on Interdependent Networks with Time-Varying Demands","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Interdependence; Reliability (semiconductor); Geospatial analysis; Critical infrastructure; Computer science; Centrality; Component (thermodynamics); Flow (mathematics); Measure (data warehouse); Flow network; Enhanced Data Rates for GSM Evolution; Interdependent networks; Electricity; Scale (ratio); Cascading failure; Operations research; Risk analysis (engineering); Distributed computing; Complex network; Data mining; Electric power system; Engineering; Business; Mathematical optimization; Geography; Computer security","score_opus":0.01215788492864119,"score_gpt":0.21396824666523973,"score_spread":0.20181036173659853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234371023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.118741415,0.0005603962,0.8743504,0.0003111491,0.000045841673,0.00006583289,0.00023747592,0.0001651282,0.0055223256],"genre_scores_gemma":[0.9004502,0.0005471933,0.09603908,0.00006459224,0.00017797976,0.00012097193,0.000285921,0.00010755394,0.002206547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883336,0.0004490086,0.000044567176,0.00024182635,0.000326448,0.00010474192],"domain_scores_gemma":[0.9862862,0.010181552,0.0015371633,0.000509713,0.00097584707,0.0005094637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021560169,0.0008652828,0.0005201151,0.0055271834,0.0006263076,0.0012678921,0.0008682535,0.0006885726,0.0022062631],"category_scores_gemma":[0.01306562,0.0002815561,0.00043972238,0.002467206,0.0019718816,0.0032368246,0.0011255328,0.00106567,0.00014204245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017689369,0.00006246401,0.009488564,0.00015775379,0.00011971623,0.00024100346,0.00050595397,0.60639054,0.004777203,0.31810337,0.0022137782,0.057762776],"study_design_scores_gemma":[0.000013674983,0.00005250437,0.0036361106,0.000032944434,0.000030695235,0.000118646756,0.00015411788,0.85738724,0.0012732927,0.13427766,0.0029932088,0.000030050713],"about_ca_topic_score_codex":0.0028821153,"about_ca_topic_score_gemma":0.0025523275,"teacher_disagreement_score":0.0055271834,"about_ca_system_score_codex":0.0017023847,"about_ca_system_score_gemma":0.00045802313,"threshold_uncertainty_score":0.012351692},"labels":[],"label_agreement":null},{"id":"W4234555700","doi":"10.1061/(asce)0733-9496(2002)128:1(86)","title":"Closure to “Velocity Profiles and Unsteady Pipe Friction in Transient Flow,” by Bruno Brunone, Bryan W. Karney, Michele Mecarelli, and Marco Ferrante July/August 2000, Vol. 126, No. 4, pp. 236–244","year":2002,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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 Toronto","funders":"","keywords":"Closure (psychology); Transient flow; Transient (computer programming); Mechanics; Humanities; Engineering; Geology; Art; Physics; Meteorology; Computer science; Political science; Law","score_opus":0.009744805500523594,"score_gpt":0.1834710360648961,"score_spread":0.1737262305643725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234555700","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046832085,0.14945878,0.68421274,0.026636256,0.044378683,0.00020980058,0.00041408942,0.0009994537,0.046858083],"genre_scores_gemma":[0.7578553,0.060474098,0.046391916,0.0043913065,0.028655993,0.00036836712,0.0007208565,0.0018258702,0.09931623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928766,0.00023814094,0.000044100783,0.00016503205,0.00017161238,0.000093461844],"domain_scores_gemma":[0.9985397,0.0006818269,0.00017437589,0.00013530094,0.00034532585,0.00012344483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016573771,0.00088411383,0.0013371552,0.0011803022,0.0009241826,0.0025673648,0.002325177,0.0015649362,0.0047427546],"category_scores_gemma":[0.009555851,0.0007949292,0.0011448086,0.0012363644,0.0044841953,0.0042170733,0.0023369843,0.002503525,0.0013170425],"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":[0.0007090721,0.00015643923,0.0023845981,0.00094955397,0.00014601654,0.00095732004,0.0012347146,0.105380505,0.005886384,0.4943864,0.27680424,0.11100475],"study_design_scores_gemma":[0.00009394603,0.00015359477,0.003982361,0.0005962408,0.00009930972,0.00048502712,0.0005522416,0.49374214,0.0043631154,0.28362256,0.21215431,0.00015512276],"about_ca_topic_score_codex":0.0070402822,"about_ca_topic_score_gemma":0.003444601,"teacher_disagreement_score":0.0070402822,"about_ca_system_score_codex":0.0015545246,"about_ca_system_score_gemma":0.001098639,"threshold_uncertainty_score":0.015866041},"labels":[],"label_agreement":null},{"id":"W4236811513","doi":"10.29007/gz11","title":"Technico-economic modelling of maintenance cost for hydroelectric turbine runners","year":2018,"lang":"en","type":"paratext","venue":"EasyChair preprint","topic":"Water Systems and 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":"École de Technologie Supérieure","funders":"Mitacs","keywords":"Hydroelectricity; Reliability (semiconductor); Monte Carlo method; Reliability engineering; Probabilistic logic; Turbine; Asset management; Asset (computer security); Net present value; Sensitivity (control systems); Computer science; Component (thermodynamics); Operations research; Engineering; Production (economics); Business; Power (physics); Mechanical engineering","score_opus":0.018120855355368403,"score_gpt":0.21987743820827585,"score_spread":0.20175658285290746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236811513","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":[0.522325,0.0017690148,0.39508748,0.0015175507,0.00009617163,0.00020560254,0.0030171783,0.00037485253,0.075607024],"genre_scores_gemma":[0.97698325,0.0004810962,0.01056551,0.000042163345,0.00001586427,0.000117688505,0.00031712203,0.00006067429,0.011416672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997547,0.000075932076,0.000010731094,0.000037092905,0.00006511243,0.00005649184],"domain_scores_gemma":[0.9989691,0.00067589653,0.000118883494,0.00003747477,0.00015596746,0.0000427128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007319671,0.00061152416,0.00051926007,0.0008089014,0.0005081306,0.0014301002,0.0015684352,0.0014764639,0.0038702881],"category_scores_gemma":[0.0025378775,0.0003895368,0.00065257517,0.0009749978,0.0006840765,0.0007127146,0.00038564886,0.00079095695,0.00027813966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008088899,0.0000058828973,0.0003454102,0.000009725915,0.0000033221288,0.000029479752,0.0000071886684,0.9943528,0.0001619583,0.0040751565,0.000115730916,0.00088527356],"study_design_scores_gemma":[0.0000020093817,0.000003960454,0.0003027327,0.000002843764,0.0000032756004,0.000008936654,0.0000048085476,0.9987571,0.000069048074,0.00063726463,0.00020509193,0.0000028813492],"about_ca_topic_score_codex":0.19157493,"about_ca_topic_score_gemma":0.11347335,"teacher_disagreement_score":0.19157493,"about_ca_system_score_codex":0.0048967637,"about_ca_system_score_gemma":0.0020148684,"threshold_uncertainty_score":0.3809197},"labels":[],"label_agreement":null},{"id":"W4237646553","doi":"10.36334/modsim.2011.e7.erechtchoukova","title":"Application of optimization techniques to water quality monitoring designs","year":2011,"lang":"en","type":"article","venue":"Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.","topic":"Water Systems and 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":"York University","funders":"","keywords":"Computer science; Quality (philosophy); Water quality; Reliability engineering; Engineering","score_opus":0.07773734934940135,"score_gpt":0.29218149707601043,"score_spread":0.21444414772660908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237646553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030039933,0.00019357596,0.9946931,0.00010114387,0.000016824868,0.0000690377,0.0000150360165,0.00010494195,0.0018023066],"genre_scores_gemma":[0.15182617,0.00067073305,0.84425324,0.0001117168,0.000055549564,0.00065998046,0.000088012166,0.00020749311,0.0021270406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949556,0.0032467893,0.00023431153,0.00040241165,0.0009453189,0.00021557052],"domain_scores_gemma":[0.9918628,0.0061487,0.00084566564,0.00036866296,0.0007115849,0.00006266669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006127908,0.0016344439,0.0013369475,0.0016852524,0.0004931126,0.0013865511,0.0010088269,0.0009686932,0.0024270688],"category_scores_gemma":[0.015732,0.0012462094,0.0014631742,0.0013560524,0.0011967677,0.0009876235,0.001289625,0.0015537625,0.00039394756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038692535,0.000045853743,0.00040443894,0.00017101635,0.00005530395,0.000033433167,0.00005803631,0.9179803,0.0011997046,0.021351986,0.00037802296,0.058283146],"study_design_scores_gemma":[0.00002335865,0.00007542868,0.0001788044,0.00003991218,0.000024775822,0.000023163326,0.00001877314,0.97188795,0.0011892031,0.024444554,0.0020826748,0.000011466581],"about_ca_topic_score_codex":0.002585558,"about_ca_topic_score_gemma":0.0020963417,"teacher_disagreement_score":0.006127908,"about_ca_system_score_codex":0.0016154844,"about_ca_system_score_gemma":0.002024569,"threshold_uncertainty_score":0.03240788},"labels":[],"label_agreement":null},{"id":"W4243230300","doi":"10.5194/dwesd-3-25-2010","title":"I-WARP: individual water main renewal planner","year":2010,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":13,"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":"Breakage; Mains electricity; Foundation (evidence); Margin (machine learning); Environmental science; Electricity; Civil engineering; Engineering; Computer science; Geography; Archaeology","score_opus":0.011449358508894633,"score_gpt":0.18797275086957846,"score_spread":0.17652339236068382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243230300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08814305,0.00039217784,0.8573239,0.0014045854,0.00008690658,0.00070907775,0.0015922931,0.0018192804,0.04852874],"genre_scores_gemma":[0.85715157,0.00027053192,0.12016982,0.00017975603,0.000034371948,0.0005991273,0.0005577165,0.00023383176,0.02080334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996904,0.00009253553,0.000010352331,0.00006074581,0.00005570214,0.000090262474],"domain_scores_gemma":[0.9994036,0.0002732403,0.000070598755,0.000047666934,0.000070717084,0.00013405342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010011457,0.00067876786,0.00082471786,0.0005109008,0.00037140137,0.0009338768,0.001562025,0.00096581894,0.019724485],"category_scores_gemma":[0.0019933574,0.0005025398,0.00054530863,0.00068367634,0.00059523317,0.0013046501,0.0017149086,0.0012327349,0.0009992317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009958419,0.000057555044,0.0004041746,0.000053858348,0.00001732297,0.000064393345,0.00003817064,0.95316166,0.0003035575,0.020304324,0.0039211432,0.021574222],"study_design_scores_gemma":[0.000022408476,0.000044366032,0.00006633362,0.0000068148493,0.0000058649152,0.000016965356,0.0000295896,0.9889509,0.00017886778,0.008596645,0.0020767604,0.0000044626436],"about_ca_topic_score_codex":0.005402266,"about_ca_topic_score_gemma":0.004904845,"teacher_disagreement_score":0.019724485,"about_ca_system_score_codex":0.0013908698,"about_ca_system_score_gemma":0.0017149355,"threshold_uncertainty_score":0.065984964},"labels":[],"label_agreement":null},{"id":"W4243551271","doi":"10.5194/dwes-2018-23","title":"Analysis of water distribution network under pressure deficient conditions throughEmitter Setting","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Software; Replicate; Computer science; Pressure head; Relation (database); Flow (mathematics); Simulation; Engineering; Mechanical engineering; Mathematics; Data mining","score_opus":0.009181428959412253,"score_gpt":0.21822611292251232,"score_spread":0.20904468396310005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243551271","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":[0.89808244,0.00015901674,0.09290769,0.00030255952,0.000022111411,0.00006093825,0.0005283306,0.00032749635,0.0076094046],"genre_scores_gemma":[0.9954365,0.00004074162,0.003559901,0.000009524185,0.000002506159,0.0000134682705,0.00011709234,0.000013654714,0.00080664223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997987,0.00007206974,0.00000630786,0.000038190232,0.00004172975,0.000043036078],"domain_scores_gemma":[0.99948955,0.00029915202,0.000056987432,0.000025657164,0.00009049781,0.0000381624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004456755,0.00046544612,0.0004254674,0.00060022966,0.00040681462,0.0007975723,0.0006182896,0.00085564016,0.0018313829],"category_scores_gemma":[0.0013812851,0.0002580667,0.00046561233,0.00040704277,0.00055368955,0.0008538292,0.00041892545,0.0004338347,0.00009090706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046249213,0.000024545021,0.0020132214,0.000028177583,0.000011583571,0.00014993007,0.000026610916,0.9923488,0.0016950762,0.0016179567,0.00018177563,0.0018560335],"study_design_scores_gemma":[0.000003256978,0.000015434613,0.00045487797,0.0000015203083,0.0000030638375,0.000010586134,0.000020667449,0.99849105,0.00048610565,0.0004327574,0.00007814436,0.0000025727568],"about_ca_topic_score_codex":0.013126008,"about_ca_topic_score_gemma":0.0063317684,"teacher_disagreement_score":0.013126008,"about_ca_system_score_codex":0.0009297809,"about_ca_system_score_gemma":0.00044087516,"threshold_uncertainty_score":0.026099205},"labels":[],"label_agreement":null},{"id":"W4246467828","doi":"10.1520/stp45402s","title":"Water Vapor Transmission Measurement and Significance of Corrections","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Water Systems and 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":"Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Water vapor; Environmental science; Transmission (telecommunications); Physics; Meteorology; Computer science; Telecommunications","score_opus":0.03135129010872305,"score_gpt":0.19204023551994104,"score_spread":0.16068894541121798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246467828","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":[0.51276684,0.014571351,0.3666599,0.00075168285,0.0011166435,0.00039702578,0.002441159,0.009557993,0.09173741],"genre_scores_gemma":[0.8808565,0.0032293557,0.089034006,0.00014925169,0.000049973605,0.00013681053,0.0012151675,0.000784934,0.024543978],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.998271,0.00033434073,0.0000618968,0.0002994291,0.00096906757,0.000064243264],"domain_scores_gemma":[0.9985281,0.00069096155,0.00008573407,0.00023252056,0.00045056286,0.000012137224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082920236,0.0004912557,0.00037143237,0.0007739666,0.00029646434,0.00073158287,0.0011836789,0.00044067408,0.002431736],"category_scores_gemma":[0.0032663187,0.00029526016,0.00020271096,0.0013262755,0.00038988533,0.0009963238,0.00037944384,0.00061481143,0.0013292815],"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":[0.0003185894,0.0001231254,0.009655877,0.0007770577,0.00004542352,0.00041173716,0.00062275375,0.009417544,0.55633056,0.0053108716,0.0055993046,0.41138715],"study_design_scores_gemma":[0.0000047054405,0.00013399831,0.009018573,0.000040304636,0.000027602871,0.00032212868,0.00015174474,0.008850639,0.96304464,0.00066390494,0.017709302,0.000032537377],"about_ca_topic_score_codex":0.001157418,"about_ca_topic_score_gemma":0.0016233712,"teacher_disagreement_score":0.002431736,"about_ca_system_score_codex":0.00040316858,"about_ca_system_score_gemma":0.00035622955,"threshold_uncertainty_score":0.008135021},"labels":[],"label_agreement":null},{"id":"W4247144882","doi":"10.5383/swes.7.02.009","title":"Exploring Weak Signals and Wild Cards in the Water Services","year":2015,"lang":"en","type":"article","venue":"International Journal of Sustainable Water and Environmental Systems","topic":"Water Systems and 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":"Alfred Kordelinin Säätiö","keywords":"Sustainability; Term (time); Computer science; Business; Ecology; Biology; Physics","score_opus":0.016166664739601707,"score_gpt":0.18378212376010586,"score_spread":0.16761545902050415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247144882","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":[0.94206643,0.00021146296,0.015730886,0.0016078559,0.00002522739,0.00010028339,0.000050804672,0.000012164989,0.04019484],"genre_scores_gemma":[0.9979328,0.00005434645,0.001340096,0.000080836704,0.0000028295997,0.000016168186,0.0000073997103,0.0000037768368,0.0005617827],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9923039,0.0044447333,0.00025643164,0.00046841003,0.0015027279,0.0010236881],"domain_scores_gemma":[0.98560053,0.009788144,0.002424734,0.0003847036,0.0010818845,0.0007200389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004976262,0.0003437785,0.0003641696,0.0021832003,0.0028739993,0.006412061,0.001072273,0.0015704399,0.004421003],"category_scores_gemma":[0.016389152,0.00034048778,0.00023702206,0.0027116667,0.01177595,0.008363941,0.006549372,0.0020907281,0.00021377746],"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":[0.0010108725,0.00046968757,0.13360465,0.00078368996,0.00009785849,0.0042301645,0.18665983,0.009428746,0.007875721,0.55903345,0.0015231526,0.0952822],"study_design_scores_gemma":[0.000069635054,0.0008801362,0.10663893,0.0005464645,0.00008794775,0.0008131662,0.5595154,0.019001829,0.005884246,0.26596498,0.04040623,0.00019092776],"about_ca_topic_score_codex":0.012205235,"about_ca_topic_score_gemma":0.015161435,"teacher_disagreement_score":0.012205235,"about_ca_system_score_codex":0.0046313093,"about_ca_system_score_gemma":0.0028829281,"threshold_uncertainty_score":0.033602655},"labels":[],"label_agreement":null},{"id":"W4247677141","doi":"10.1139/l10-030","title":"Physical and computational modelling of residence and flow development time in a large municipal disinfection clearwell","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residence time distribution; TRACER; Flow (mathematics); Residence time (fluid dynamics); Scale (ratio); Volumetric flow rate; Environmental science; Mechanics; Flow conditions; Transient flow; Steady state (chemistry); Simulation; Hydrology (agriculture); Computer science; Geotechnical engineering; Geology; Chemistry; Geography; Physics","score_opus":0.006020888880789953,"score_gpt":0.1647070431941944,"score_spread":0.15868615431340446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247677141","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":[0.99215394,0.00003880568,0.0061932574,0.000062949875,0.000005054165,0.000025710628,0.00019514683,0.00008202542,0.0012430258],"genre_scores_gemma":[0.99587715,0.000037343,0.0032770035,0.000007149508,0.0000010737069,0.000024255216,0.000110265486,0.0000132830955,0.0006524211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987316,0.000021235723,0.000006068635,0.000030799474,0.000027308794,0.00004135942],"domain_scores_gemma":[0.9996778,0.00016977315,0.000049681174,0.000023625253,0.000048637205,0.00003052743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025932112,0.00042921604,0.00034414802,0.0003057687,0.0006045909,0.00084505416,0.00079683017,0.0009204742,0.0012025109],"category_scores_gemma":[0.00080066174,0.00048763398,0.00040928117,0.00033144542,0.0005421891,0.00070104375,0.0003866402,0.00058745395,0.00011557608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090733054,0.000100615165,0.009082418,0.000020237405,0.00000871267,0.00009906046,0.000054773413,0.9795944,0.007456345,0.00056089356,0.0001349963,0.0027967468],"study_design_scores_gemma":[0.0000153522,0.000050068662,0.0027169238,0.0000017540247,0.0000038897374,0.000009563986,0.000034980836,0.9953471,0.0016309527,0.000060159684,0.0001200385,0.000009219066],"about_ca_topic_score_codex":0.10894292,"about_ca_topic_score_gemma":0.09037463,"teacher_disagreement_score":0.10894292,"about_ca_system_score_codex":0.002030468,"about_ca_system_score_gemma":0.002228431,"threshold_uncertainty_score":0.21661764},"labels":[],"label_agreement":null},{"id":"W4247687474","doi":"10.14796/jwmm.c461","title":"Inverse Optimization based Detection of Leaks from Simulated Pressure in Water Networks, Part 2: Analysis for Two Leaks","year":2018,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"University of Ghana; Ghana Education Trust Fund","keywords":"Inverse; Leak detection; Computer science; Algorithm; Leak; Environmental science; Mathematics","score_opus":0.01341726758286961,"score_gpt":0.20593512117373228,"score_spread":0.19251785359086265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247687474","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":[0.51436377,0.00022696433,0.48084098,0.0004164874,0.000029420582,0.00010272994,0.00015486039,0.0006048787,0.0032599098],"genre_scores_gemma":[0.9804246,0.00007173574,0.018270891,0.00002545768,0.000008231067,0.000049898368,0.000099864665,0.0000388244,0.0010105596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973136,0.000087729844,0.000012845454,0.00004297601,0.00008397756,0.000041019764],"domain_scores_gemma":[0.9985196,0.0010929186,0.00010390031,0.000059751594,0.00018281041,0.000040967545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080346013,0.00066764303,0.00084323226,0.0004904851,0.0002694229,0.00063999486,0.0005925995,0.0010055582,0.0011483687],"category_scores_gemma":[0.0042548454,0.00043341887,0.0007501606,0.0002945752,0.0008743515,0.0008366494,0.0006931115,0.0010049596,0.00010740261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001249833,0.00006079518,0.001277277,0.000060437404,0.00001894232,0.000058644684,0.000038318,0.98424226,0.0043823197,0.0010580237,0.00018830945,0.00848978],"study_design_scores_gemma":[0.0000027021354,0.0000117455465,0.0002482418,0.0000010834048,0.0000016657885,0.0000045769434,0.000003662368,0.9990108,0.0005773444,0.00011793593,0.000017495975,0.0000027569179],"about_ca_topic_score_codex":0.010822205,"about_ca_topic_score_gemma":0.003999393,"teacher_disagreement_score":0.010822205,"about_ca_system_score_codex":0.00067289063,"about_ca_system_score_gemma":0.00097230717,"threshold_uncertainty_score":0.02151841},"labels":[],"label_agreement":null},{"id":"W4255111573","doi":"10.1002/9781444338386.wbeah06351","title":"Water meter","year":2012,"lang":"en","type":"other","venue":"The Encyclopedia of Ancient History","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"","keywords":"Sizing; Metre; Environmental science; Interference (communication); Hydrology (agriculture); Marine engineering; Computer science; Engineering; Telecommunications; Chemistry; Geotechnical engineering; Physics","score_opus":0.008382555894172274,"score_gpt":0.16443856590748146,"score_spread":0.15605601001330918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255111573","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":[0.015891768,0.0010861027,0.13651234,0.002334764,0.0024286886,0.0015876855,0.11924874,0.040708777,0.6802011],"genre_scores_gemma":[0.16562948,0.0013828626,0.07517858,0.0024083063,0.00045821726,0.0013954155,0.07808804,0.0036497877,0.67180926],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979109,0.00015236011,0.00010286378,0.00039357232,0.0013375973,0.00010260756],"domain_scores_gemma":[0.9983279,0.0002197269,0.000098543576,0.0001790003,0.0011059557,0.00006886087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008223448,0.0010850453,0.0007793003,0.0026564961,0.00075058284,0.0015960633,0.001972379,0.001014787,0.20833638],"category_scores_gemma":[0.0029494881,0.0004787279,0.00037171255,0.003964126,0.00032582047,0.002322737,0.0011311048,0.0010691473,0.09922719],"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":[0.0004171689,0.0001646604,0.0041279662,0.00056332146,0.000025626869,0.000110588684,0.00011956679,0.0038223749,0.014608406,0.011381566,0.5772573,0.38740143],"study_design_scores_gemma":[0.00015308088,0.00016475766,0.0042688795,0.0001300027,0.000046486082,0.00014188878,0.000076990866,0.016607808,0.021984927,0.0047482504,0.95160484,0.00007203779],"about_ca_topic_score_codex":0.0026924426,"about_ca_topic_score_gemma":0.00253279,"teacher_disagreement_score":0.20833638,"about_ca_system_score_codex":0.00065691856,"about_ca_system_score_gemma":0.0012102841,"threshold_uncertainty_score":0.6969546},"labels":[],"label_agreement":null},{"id":"W4281677644","doi":"10.1007/978-981-19-0503-2_11","title":"Evidence-Based Water Loss Management: Development and Deployment of a Mobile DMA Testing Unit","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Water Systems and 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; HydraTek (Canada)","funders":"","keywords":"Benchmarking; Software deployment; Unit (ring theory); Computer science; Reliability engineering; Environmental economics; Engineering; Operations management; Business","score_opus":0.02328245847853683,"score_gpt":0.19021334547925267,"score_spread":0.16693088700071584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281677644","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":[0.25629374,0.004298818,0.6881357,0.010970985,0.00039080303,0.007999308,0.002772188,0.0048302356,0.024308182],"genre_scores_gemma":[0.3649738,0.0017488499,0.623443,0.0009358107,0.0001198578,0.003543972,0.00093874073,0.00015549365,0.0041403864],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99749905,0.0014078242,0.00021211854,0.00021893602,0.0005683406,0.00009360987],"domain_scores_gemma":[0.9845308,0.009989529,0.0010761459,0.00077994535,0.0027927186,0.00083095965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077114194,0.0004959369,0.0005818673,0.0015371427,0.00034019575,0.0015436743,0.0020998288,0.0009780215,0.0050406125],"category_scores_gemma":[0.020382198,0.00030472194,0.00040163877,0.0010892983,0.00044290398,0.0013399988,0.0018694636,0.00085912494,0.00084624067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001163723,0.000765146,0.02179825,0.0003576394,0.000106665,0.0002764087,0.00037356923,0.008991411,0.005590073,0.0038304215,0.0065153623,0.9502313],"study_design_scores_gemma":[0.0028052705,0.027710339,0.13314779,0.0038643093,0.0011252282,0.004271791,0.0033666454,0.58912504,0.05028825,0.037220977,0.14664134,0.00043298374],"about_ca_topic_score_codex":0.0030116288,"about_ca_topic_score_gemma":0.0041080485,"teacher_disagreement_score":0.0077114194,"about_ca_system_score_codex":0.0012007949,"about_ca_system_score_gemma":0.003911008,"threshold_uncertainty_score":0.040782392},"labels":[],"label_agreement":null},{"id":"W4281717138","doi":"10.1061/9780784484258.102","title":"Top Factors Leading to Water Main Failures—An Analysis of 12 Canadian Cities","year":2022,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2022","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Categorical variable; Data collection; Computer science; Random forest; Dimension (graph theory); Water pipe; Engineering; Statistics; Mathematics; Machine learning","score_opus":0.005425468541374943,"score_gpt":0.16069827979979245,"score_spread":0.15527281125841752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281717138","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":[0.99197817,0.00023385911,0.00052424415,0.0001922638,0.000006601393,0.000083360464,0.0057359785,0.000032867785,0.0012127373],"genre_scores_gemma":[0.98862386,0.00032828024,0.0011956035,0.000057360943,0.000004138232,0.00005077761,0.008222965,0.000019975347,0.0014969822],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988857,0.0000805518,0.000059000544,0.00019084774,0.00042111136,0.0003629286],"domain_scores_gemma":[0.9968754,0.00032085233,0.00033666717,0.00013064322,0.0018713733,0.00046509656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008897981,0.0008845751,0.00075106195,0.004063907,0.0035779183,0.0017907686,0.001307535,0.0005331714,0.0012232898],"category_scores_gemma":[0.002931446,0.00050575775,0.0011227989,0.0088514,0.0008820897,0.0004223456,0.0013214998,0.00073224894,0.0002308111],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012812675,0.00006582568,0.97274244,0.00009614663,0.00013412695,0.00022193108,0.0014785294,0.0035095685,0.0005829906,0.0002620857,0.0031868888,0.017591331],"study_design_scores_gemma":[0.0000046740433,0.000021955984,0.99074495,0.00002347507,0.000047351714,0.00003704127,0.0027765532,0.004294571,0.00016992899,0.000047282487,0.0018033376,0.000028851953],"about_ca_topic_score_codex":0.9961046,"about_ca_topic_score_gemma":0.9977804,"teacher_disagreement_score":0.034863483,"about_ca_system_score_codex":0.034863483,"about_ca_system_score_gemma":0.037278187,"threshold_uncertainty_score":0.25295347},"labels":[],"label_agreement":null},{"id":"W4281907607","doi":"10.1061/9780784484258.098","title":"Insights and Challenges Associated with Air in Pressurized Water Conveyance Systems","year":2022,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2022","topic":"Water Systems and 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":"Hudbay Minerals (Canada); University of Toronto","funders":"","keywords":"Risk analysis (engineering); Variety (cybernetics); Safeguard; Limiting; Computer science; Engineering; Business; Mechanical engineering","score_opus":0.006200106844404392,"score_gpt":0.14044129482163012,"score_spread":0.13424118797722573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281907607","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":[0.6232396,0.018283706,0.3076244,0.010079215,0.0003436419,0.0001596295,0.00020726462,0.00022477677,0.03983786],"genre_scores_gemma":[0.9809099,0.0043017785,0.011990418,0.00014120869,0.000093286886,0.000034136938,0.000033221982,0.000026486621,0.0024695087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854434,0.0006995319,0.000080150305,0.00016317077,0.00037319577,0.00013964472],"domain_scores_gemma":[0.998125,0.0013052543,0.00021714895,0.00007703898,0.00021684413,0.00005871205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019321019,0.0006023403,0.00084700744,0.00038448354,0.00083869917,0.0026231247,0.00073296804,0.0012786107,0.0018895619],"category_scores_gemma":[0.003121293,0.00048648138,0.00043250632,0.00037567472,0.0019458067,0.0033726434,0.0012628308,0.0016157996,0.00020492871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048853626,0.00022363853,0.008649335,0.002018251,0.00013075396,0.0016544441,0.0021585727,0.7340618,0.032030687,0.080835275,0.0021589936,0.13558969],"study_design_scores_gemma":[0.00008284683,0.0023471345,0.017603207,0.0006407279,0.00019958921,0.0009844607,0.009358243,0.7842765,0.025944013,0.10415748,0.0540997,0.0003061284],"about_ca_topic_score_codex":0.003703908,"about_ca_topic_score_gemma":0.004295044,"teacher_disagreement_score":0.003703908,"about_ca_system_score_codex":0.0011953117,"about_ca_system_score_gemma":0.000864474,"threshold_uncertainty_score":0.010218024},"labels":[],"label_agreement":null},{"id":"W4283024615","doi":"10.1002/aws2.1288","title":"Evaluating satellite and in situ monitoring technologies for leak detection and response","year":2022,"lang":"en","type":"article","venue":"AWWA Water Science","topic":"Water Systems and 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":"American Water (Canada)","funders":"California Energy Commission","keywords":"Leak detection; Casual; Leak; Environmental science; Emerging technologies; Continuous monitoring; Satellite; Computer science; Remote sensing; Environmental resource management; Operations management; Engineering; Geology; Environmental engineering; Artificial intelligence","score_opus":0.026709395169376517,"score_gpt":0.2669360153507071,"score_spread":0.2402266201813306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283024615","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":[0.98704183,0.00024534157,0.008844498,0.000119243574,0.000044265318,0.00033388767,0.00039702925,0.00020671541,0.0027672013],"genre_scores_gemma":[0.9859083,0.00019213682,0.011888898,0.000054684828,0.000016402335,0.000114290255,0.00030421792,0.000016508262,0.0015045152],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977762,0.00074646814,0.00013866468,0.0003391822,0.0008768676,0.00012259402],"domain_scores_gemma":[0.9952898,0.0012116844,0.00080439367,0.00026767544,0.0022706368,0.00015571895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024328528,0.0007019389,0.0004514479,0.0012685922,0.00048633467,0.0010937362,0.0007902831,0.0005300425,0.0010973262],"category_scores_gemma":[0.004043013,0.00036036133,0.0004276102,0.0008722287,0.00029894724,0.0010944564,0.00048310342,0.00040500777,0.00020551536],"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":[0.0046029557,0.006650043,0.45240116,0.0010041337,0.0005854692,0.00023184138,0.0009269588,0.05332013,0.20679413,0.0004984465,0.0041715717,0.2688132],"study_design_scores_gemma":[0.0008371639,0.037209876,0.4557122,0.0001507462,0.001098162,0.0003467277,0.0035962577,0.2317391,0.25775158,0.00030302693,0.011036556,0.00021855831],"about_ca_topic_score_codex":0.039896008,"about_ca_topic_score_gemma":0.05833117,"teacher_disagreement_score":0.039896008,"about_ca_system_score_codex":0.00196497,"about_ca_system_score_gemma":0.00096214423,"threshold_uncertainty_score":0.07932758},"labels":[],"label_agreement":null},{"id":"W4285153627","doi":"10.1109/tcst.2022.3172524","title":"Adaptive Naive Bayes Classifier Based Filter Using Kernel Density Estimation for Pipeline Leakage Detection","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Control Systems Technology","topic":"Water Systems and 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":"Naive Bayes classifier; Kernel density estimation; Computer science; Pipeline transport; Leakage (economics); Kernel (algebra); Bayes classifier; Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Engineering; Support vector machine","score_opus":0.014745981910348155,"score_gpt":0.2065482557093109,"score_spread":0.19180227379896275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285153627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02352611,0.00077118765,0.97322714,0.00019863658,0.00013305886,0.00007140061,0.00008169804,0.0012177984,0.0007729637],"genre_scores_gemma":[0.6008763,0.0008046882,0.3912592,0.0005243992,0.00030740735,0.00023675893,0.00074061466,0.00012825403,0.0051224055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981481,0.00030323182,0.00017497937,0.00046325606,0.00067921745,0.00023125467],"domain_scores_gemma":[0.9970831,0.0012987637,0.000190969,0.000119756805,0.0012506918,0.000056613957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020331861,0.00087453733,0.0018307074,0.0011364748,0.0007163259,0.0010191265,0.0015340457,0.001701554,0.0018692582],"category_scores_gemma":[0.006582182,0.0005162191,0.0009933402,0.00095043384,0.00051701587,0.0017042187,0.00050989253,0.0014422319,0.00096709107],"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":[0.00089905027,0.00041978527,0.007274929,0.00027036096,0.00020872478,0.00021729709,0.00016492479,0.1898437,0.021268757,0.0038175453,0.006202282,0.7694127],"study_design_scores_gemma":[0.000021731468,0.0000646887,0.001008301,0.000015033164,0.000030654057,0.00007033727,0.000014479537,0.9930894,0.0037250665,0.001186864,0.00075254444,0.000020905227],"about_ca_topic_score_codex":0.01867902,"about_ca_topic_score_gemma":0.010981675,"teacher_disagreement_score":0.01867902,"about_ca_system_score_codex":0.0010671848,"about_ca_system_score_gemma":0.001959025,"threshold_uncertainty_score":0.037140608},"labels":[],"label_agreement":null},{"id":"W4285677270","doi":"10.1002/cjce.24556","title":"Performance characteristics of an axial‐flow gas induction impeller","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and 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":"Impeller; Mechanics; Rotational speed; Volumetric flow rate; Axial compressor; Flow (mathematics); Turbulence; Flow coefficient; Slip factor; Pressure coefficient; Materials science; Control theory (sociology); Mechanical engineering; Physics; Engineering; Gas compressor; Computer science","score_opus":0.005653417867739249,"score_gpt":0.14901749726350447,"score_spread":0.14336407939576523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285677270","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":[0.9897731,0.00015676029,0.0078068366,0.000046387333,0.000017106824,0.0000134831935,0.00016227902,0.0002309496,0.0017930558],"genre_scores_gemma":[0.9983559,0.000041521205,0.00078188133,0.0000061968594,0.000005620431,0.0000065971144,0.0001481646,0.00001843693,0.00063559803],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996424,0.000033944747,0.000023451235,0.000054446875,0.0001859656,0.00005971317],"domain_scores_gemma":[0.9986571,0.0005174332,0.00018755699,0.000090673726,0.00045374496,0.00009356156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047171925,0.000444606,0.00035366003,0.00045400616,0.00034705966,0.0004364854,0.00058771967,0.00038898448,0.0014328525],"category_scores_gemma":[0.0010352673,0.0001805661,0.0002721214,0.00039726365,0.00022410032,0.0004324373,0.00018108386,0.0003407482,0.00052625255],"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":[0.0041018305,0.00028467242,0.009640071,0.00026750306,0.0000426385,0.00038959083,0.0002494425,0.017659433,0.9290601,0.0002593505,0.0007436758,0.037301783],"study_design_scores_gemma":[0.00016933032,0.008682503,0.07977902,0.000030400102,0.00009775267,0.0007824888,0.0001525899,0.08717088,0.8198314,0.000121022196,0.0030824346,0.000100206606],"about_ca_topic_score_codex":0.0011561573,"about_ca_topic_score_gemma":0.0005450711,"teacher_disagreement_score":0.0014328525,"about_ca_system_score_codex":0.00035093707,"about_ca_system_score_gemma":0.0003129334,"threshold_uncertainty_score":0.0047934055},"labels":[],"label_agreement":null},{"id":"W4285814011","doi":"10.1109/iwcmc55113.2022.9824540","title":"Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case","year":2022,"lang":"en","type":"article","venue":"2022 International Wireless Communications and Mobile Computing (IWCMC)","topic":"Water Systems and 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":"Western University","funders":"","keywords":"Support vector machine; Computer science; Pipeline (software); Pipeline transport; Generalizability theory; Noise (video); Data mining; Artificial intelligence; Curse of dimensionality; Feature selection; Machine learning; Pattern recognition (psychology); Engineering; Mathematics; Statistics","score_opus":0.04427978310220612,"score_gpt":0.25401843617070863,"score_spread":0.2097386530685025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285814011","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":[0.8643056,0.00035823236,0.12994769,0.00049612444,0.00007038893,0.00016130267,0.0009676942,0.0011195899,0.0025734033],"genre_scores_gemma":[0.9671008,0.00015317704,0.030259315,0.0000634824,0.000029580031,0.00006954606,0.0008656827,0.000025057292,0.0014333272],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991221,0.00013550361,0.000078505924,0.00024322196,0.00028782187,0.00013283243],"domain_scores_gemma":[0.99921596,0.00034578226,0.000087124725,0.00010627815,0.00018741621,0.00005753312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008543501,0.00085626566,0.0007830891,0.0010542525,0.00045709522,0.00083021674,0.00097904,0.0014845805,0.0010747885],"category_scores_gemma":[0.0018720761,0.0002596227,0.0007551822,0.0008972662,0.00052413373,0.00094589026,0.00080459984,0.0005557948,0.00046807565],"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":[0.0023305828,0.001790684,0.19010223,0.00094975706,0.0002646137,0.009383779,0.0007171939,0.35737792,0.06484653,0.0022284868,0.0075621083,0.36244604],"study_design_scores_gemma":[0.00007599873,0.00056295894,0.048000723,0.00003774184,0.000098938006,0.001063346,0.00051561475,0.9051088,0.040436156,0.0014055015,0.0026367258,0.00005748071],"about_ca_topic_score_codex":0.004731837,"about_ca_topic_score_gemma":0.005675812,"teacher_disagreement_score":0.004731837,"about_ca_system_score_codex":0.0005572676,"about_ca_system_score_gemma":0.00066806,"threshold_uncertainty_score":0.009408593},"labels":[],"label_agreement":null},{"id":"W4286620328","doi":"10.1061/(asce)cf.1943-5509.0001747","title":"Integrated Reliability Assessment Model for Drinking Water Networks: A Case Study of the City of London, Canada","year":2022,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University; Canadian Council of Professional Engineers","funders":"","keywords":"Reliability (semiconductor); Context (archaeology); Reliability engineering; Engineering; Hydraulics; Quality (philosophy); Water supply; Civil engineering; Computer science; Environmental engineering","score_opus":0.010422432975170006,"score_gpt":0.19436871558754804,"score_spread":0.18394628261237803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286620328","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":[0.89221746,0.00061887514,0.061818846,0.0010497015,0.00004740311,0.000419889,0.0033433046,0.00038924336,0.04009534],"genre_scores_gemma":[0.98000264,0.00021906645,0.007327385,0.000037464844,0.000006959601,0.00012852855,0.0007836233,0.000029237706,0.0114651825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999592,0.0000961839,0.000012763161,0.00007001567,0.00009662705,0.00013247416],"domain_scores_gemma":[0.9990006,0.00032825887,0.00006458205,0.000024517023,0.0005095599,0.000072542476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068557437,0.0011087016,0.00063895754,0.0014363369,0.0014577778,0.0017448524,0.0020378889,0.0016628632,0.003056118],"category_scores_gemma":[0.001853819,0.00049024133,0.0008156558,0.0014097696,0.0009871564,0.0005749308,0.0008641055,0.00082645804,0.0002316142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036092028,0.000029846587,0.0033851499,0.00002798578,0.000016347392,0.00031240453,0.000102515245,0.9904881,0.000290961,0.0022888756,0.0007357428,0.0022860528],"study_design_scores_gemma":[0.000011022015,0.000015395064,0.0013228223,0.000007633191,0.000014085842,0.000018644501,0.00016382843,0.99733067,0.00009904576,0.00045371393,0.0005535665,0.000009545474],"about_ca_topic_score_codex":0.877005,"about_ca_topic_score_gemma":0.82786417,"teacher_disagreement_score":0.12299502,"about_ca_system_score_codex":0.014496351,"about_ca_system_score_gemma":0.0076415627,"threshold_uncertainty_score":0.24743867},"labels":[],"label_agreement":null},{"id":"W4288039708","doi":"10.1080/1573062x.2022.2102509","title":"Sensitivity-based adaptive procedure (SAP) for optimal rehabilitation of sewer systems","year":2022,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and 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":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Sorting; Sensitivity (control systems); Particle swarm optimization; Computer science; Multi-objective optimization; Mathematical optimization; Genetic algorithm; Engineering; Mathematics; Algorithm","score_opus":0.007369749817494957,"score_gpt":0.1815846029330112,"score_spread":0.17421485311551624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288039708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041709512,0.00018755501,0.95534736,0.00011834645,0.000026979513,0.00009916196,0.000033728542,0.00037272047,0.0021047434],"genre_scores_gemma":[0.89722127,0.00013276757,0.10097518,0.000102123246,0.000017014308,0.00022881429,0.00008365844,0.00006246588,0.0011767476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951255,0.00019718322,0.00002350721,0.00008220722,0.00012266479,0.00006196839],"domain_scores_gemma":[0.99923456,0.00048710476,0.00008857015,0.000041668907,0.00011778238,0.00003026708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095687585,0.00096342753,0.0007164461,0.0007345708,0.00047647365,0.0004845903,0.0007231599,0.00089116924,0.0014536531],"category_scores_gemma":[0.002212641,0.000482195,0.0010330688,0.0004901642,0.0006269463,0.0006959945,0.0010872937,0.0009636314,0.00012996104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025183472,0.000028347906,0.00048302172,0.000039288134,0.000033847205,0.000039158553,0.000024867892,0.981075,0.0019457887,0.0015919321,0.00022104329,0.01449251],"study_design_scores_gemma":[0.0000043424807,0.000030394882,0.00011723475,0.0000033286954,0.0000063679913,0.000009107097,0.000003802969,0.9985727,0.00038797868,0.00070494437,0.000155599,0.0000042261318],"about_ca_topic_score_codex":0.005965772,"about_ca_topic_score_gemma":0.004487345,"teacher_disagreement_score":0.005965772,"about_ca_system_score_codex":0.00048075023,"about_ca_system_score_gemma":0.0014595899,"threshold_uncertainty_score":0.011862099},"labels":[],"label_agreement":null},{"id":"W4288431179","doi":"10.1061/9780784484289.037","title":"The Level of Utilizing Water Pipeline Condition Assessment Tools by Public Owners: A Structured Survey","year":2022,"lang":"en","type":"article","venue":"Pipelines 2022","topic":"Water Systems and 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":"Aecom (Canada)","funders":"","keywords":"Asset (computer security); Benchmarking; Maturity (psychological); Pipeline (software); Business; Service (business); Investment (military); Computer science; Risk analysis (engineering); Environmental economics; Computer security; Marketing; Economics","score_opus":0.07425665402345788,"score_gpt":0.27240520430635906,"score_spread":0.19814855028290118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288431179","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":[0.99794084,0.000025333158,0.00045964713,0.00014403975,0.0000017476879,0.000052350195,0.00024424135,0.000005954644,0.001125705],"genre_scores_gemma":[0.99850345,0.000079290985,0.000547587,0.00006395584,0.0000034356085,0.00009147745,0.00022675506,0.0000025346606,0.00048156825],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959031,0.0015429413,0.00042873647,0.00035126295,0.0011845677,0.00058944075],"domain_scores_gemma":[0.9782169,0.008413156,0.006738919,0.0013504991,0.004371857,0.00090869097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053161825,0.0001159775,0.00023134802,0.0011423924,0.00032215845,0.001055478,0.00024071262,0.0005345346,0.0018592345],"category_scores_gemma":[0.015558043,0.00021736984,0.00027421955,0.0008644291,0.0006006406,0.0016931326,0.0009448373,0.00049510394,0.00054271024],"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":[0.00007248327,0.00026264435,0.96581674,0.000094122195,0.000022600658,0.00011401435,0.009961761,0.00021199154,0.001481132,0.0002068605,0.0007283128,0.021027315],"study_design_scores_gemma":[0.0000048630277,0.00059698493,0.9600688,0.00007248181,0.000012444055,0.000308959,0.033489242,0.00092380994,0.00083477265,0.00014596138,0.0035097627,0.00003176192],"about_ca_topic_score_codex":0.0022006135,"about_ca_topic_score_gemma":0.0024210403,"teacher_disagreement_score":0.0053161825,"about_ca_system_score_codex":0.0005999958,"about_ca_system_score_gemma":0.0009656841,"threshold_uncertainty_score":0.028114974},"labels":[],"label_agreement":null},{"id":"W4288434585","doi":"10.1061/9780784484302.003","title":"Using Machine Learning Techniques to Optimize Infrastructure Investment for the Water Distribution Network","year":2022,"lang":"en","type":"article","venue":"Pipelines 2022","topic":"Water Systems and 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":"Regional Municipality of Ottawa","funders":"","keywords":"Computer science; Quality (philosophy); Investment (military); Service (business); Risk analysis (engineering); Artificial intelligence; Reliability engineering; Engineering; Business","score_opus":0.013612932307690333,"score_gpt":0.22217640880358608,"score_spread":0.20856347649589574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288434585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21375628,0.0021334188,0.76831406,0.0024756826,0.00017927508,0.00027024077,0.0005633069,0.0011654816,0.011142261],"genre_scores_gemma":[0.89199376,0.0005607781,0.10264246,0.00023491938,0.00006852941,0.00021903867,0.00059483224,0.00010040792,0.0035853803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953103,0.00018463233,0.00002339883,0.00009506261,0.00007397774,0.00009198998],"domain_scores_gemma":[0.99823725,0.0012426958,0.0001818373,0.00005217338,0.00020853993,0.00007750545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001417402,0.0012687305,0.0009876126,0.0011413189,0.00040672562,0.0014204767,0.0009405531,0.0015204622,0.0023230608],"category_scores_gemma":[0.00514908,0.00069490186,0.00058852014,0.0012176463,0.00052624627,0.0015742135,0.0006676564,0.0013564548,0.0003731786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002069132,0.000027188606,0.00069430587,0.000022483488,0.000017934513,0.00001982092,0.000008922757,0.98544323,0.00012732457,0.0006610282,0.0004451716,0.012511971],"study_design_scores_gemma":[0.0000038375656,0.000010882993,0.00008920152,0.000003848164,0.000003845295,0.0000022192467,0.0000055137652,0.99852127,0.000060577917,0.0011587937,0.00013841374,0.0000015795048],"about_ca_topic_score_codex":0.013624898,"about_ca_topic_score_gemma":0.0133011835,"teacher_disagreement_score":0.013624898,"about_ca_system_score_codex":0.0019204601,"about_ca_system_score_gemma":0.0020064006,"threshold_uncertainty_score":0.027091205},"labels":[],"label_agreement":null},{"id":"W4288436446","doi":"10.1061/9780784484289.033","title":"Use of Monte Carlo Simulation in Long-Term Capital Planning of Rehabilitation of Water Distribution Networks","year":2022,"lang":"en","type":"article","venue":"Pipelines 2022","topic":"Water Systems and 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":"Hamilton Health Sciences; Manitoba Beekeepers' Association; Aecom (Canada)","funders":"","keywords":"Term (time); Monte Carlo method; Intervention (counseling); Selection (genetic algorithm); Computer science; Failure rate; Reliability engineering; Operations research; Engineering; Statistics; Mathematics; Artificial intelligence","score_opus":0.013763612192740369,"score_gpt":0.22461005604233455,"score_spread":0.2108464438495942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288436446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28679326,0.000574036,0.6911151,0.00079767953,0.000077924546,0.0002768878,0.00039903336,0.0007532734,0.019212905],"genre_scores_gemma":[0.9305335,0.00022826971,0.06713505,0.000059007725,0.000016133123,0.00020886805,0.00015933349,0.00005827196,0.0016016325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992613,0.00049844413,0.000030010213,0.000059527763,0.000086901804,0.0000637464],"domain_scores_gemma":[0.9942941,0.0047403476,0.0002745611,0.00017803066,0.00036646798,0.0001465013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022532574,0.000676348,0.0009169246,0.00085548236,0.0009376639,0.0013602899,0.0011733248,0.0014052858,0.0017052192],"category_scores_gemma":[0.006891824,0.00075057213,0.000666138,0.001091684,0.0009072125,0.0008649321,0.00068098324,0.00083978474,0.00014563007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008003409,0.0000063092534,0.0002965345,0.000003578581,0.000005075345,0.000009307308,0.00000778505,0.9980568,0.000027624443,0.000698126,0.00002513414,0.00085567025],"study_design_scores_gemma":[0.0000029206703,0.0000043110654,0.00006124306,0.0000022337802,0.0000023951413,0.0000018717709,0.0000050114695,0.9992906,0.000034832043,0.0005186775,0.0000728665,0.0000030280428],"about_ca_topic_score_codex":0.13997935,"about_ca_topic_score_gemma":0.117929585,"teacher_disagreement_score":0.13997935,"about_ca_system_score_codex":0.003122017,"about_ca_system_score_gemma":0.0030720704,"threshold_uncertainty_score":0.2783292},"labels":[],"label_agreement":null},{"id":"W4289529094","doi":"10.3390/w14152376","title":"Concerning Dynamic Effects in Pipe Systems with Two-Phase Flows: Pressure Surges, Cavitation, and Ventilation","year":2022,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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 Toronto","funders":"Instituto Superior Técnico; Fundação para a Ciência e a Tecnologia; Universidade de Lisboa; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Civil Engineering Research and Innovation for Sustainability","keywords":"Cavitation; Water hammer; Mechanics; Pipeline transport; Environmental science; Phase (matter); Marine engineering; Engineering; Meteorology; Mechanical engineering; Physics","score_opus":0.0038359417109205102,"score_gpt":0.19329608177293547,"score_spread":0.18946014006201498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289529094","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":[0.5342446,0.0043869005,0.44319215,0.0016176903,0.00018858368,0.000079322715,0.0000876886,0.00032078722,0.015882263],"genre_scores_gemma":[0.9905592,0.0012486476,0.006641236,0.000060334398,0.00006943877,0.000023648548,0.000024169909,0.000044175915,0.0013291544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978334,0.00006819275,0.0000110102865,0.0000319587,0.000074519536,0.000031057378],"domain_scores_gemma":[0.9990138,0.0007069516,0.00016961055,0.000039938765,0.000054735792,0.000014923686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005119572,0.00049198203,0.00058162224,0.00029934593,0.0006246947,0.0010616448,0.0005176276,0.00096116256,0.0015825499],"category_scores_gemma":[0.002179429,0.00020616341,0.00036462775,0.00026290122,0.0017396061,0.0012590218,0.0007952099,0.000605361,0.00009569931],"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":[0.00018735918,0.00008958132,0.0062584565,0.00092103425,0.00008107962,0.0020517516,0.000779795,0.77174175,0.113276,0.057955217,0.0006122091,0.046045788],"study_design_scores_gemma":[0.000021847505,0.00043263324,0.008123673,0.000100408084,0.000046489746,0.0008633336,0.0005069921,0.89288557,0.04850767,0.044164628,0.004222739,0.00012400222],"about_ca_topic_score_codex":0.0012179625,"about_ca_topic_score_gemma":0.0007409846,"teacher_disagreement_score":0.0015825499,"about_ca_system_score_codex":0.00031057082,"about_ca_system_score_gemma":0.00029648127,"threshold_uncertainty_score":0.0052942038},"labels":[],"label_agreement":null},{"id":"W4290830415","doi":"10.1038/s41598-022-16830-y","title":"Semi-supervised learning framework for oil and gas pipeline failure detection","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Computer science; Pipeline (software); Classifier (UML); Machine learning; Pipeline transport; Scalability; Artificial intelligence; Data mining; Supervised learning; Missing data; Artificial neural network; Database; Engineering","score_opus":0.0074333784261703,"score_gpt":0.19449708635050664,"score_spread":0.18706370792433633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290830415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013482703,0.00025902977,0.9839279,0.0001561447,0.00003141147,0.0000640511,0.00016963058,0.0013330125,0.0005762162],"genre_scores_gemma":[0.6417184,0.00035451015,0.35052806,0.00027808204,0.00018428465,0.00061106274,0.0018388755,0.00015918163,0.00432755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988105,0.00043443724,0.000076314966,0.00028819733,0.00028086855,0.00010967129],"domain_scores_gemma":[0.99774253,0.0008799761,0.0002335577,0.0003322929,0.0007202105,0.00009142152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023535588,0.00088545604,0.0014566394,0.0009054633,0.00048642134,0.0008971303,0.0026444523,0.0012240876,0.0013471376],"category_scores_gemma":[0.0035015498,0.0005002348,0.0011692352,0.00090712216,0.00067734625,0.0011276659,0.0013049539,0.001797675,0.0006480202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018109112,0.00032622993,0.0023850575,0.00013446824,0.00018293019,0.000081448976,0.00010277839,0.7330182,0.0022769992,0.0058113807,0.0036924253,0.25180703],"study_design_scores_gemma":[0.0000025315903,0.00002085714,0.00011678341,0.0000023325485,0.0000036984795,0.0000071783584,0.000004664343,0.9975642,0.00031681362,0.0017481957,0.00020954004,0.0000032129792],"about_ca_topic_score_codex":0.0070371763,"about_ca_topic_score_gemma":0.0069929524,"teacher_disagreement_score":0.0070371763,"about_ca_system_score_codex":0.0008999912,"about_ca_system_score_gemma":0.0019881753,"threshold_uncertainty_score":0.013992429},"labels":[],"label_agreement":null},{"id":"W4292395454","doi":"10.1139/cjce-2021-0466","title":"Nouveau cadre d’élaboration du plan d’intervention pour le réseau d’eau potable: analyse des critères de décisions","year":2022,"lang":"fr","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and 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":"École de Technologie Supérieure","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.008603800819853697,"score_gpt":0.18926849835293402,"score_spread":0.18066469753308032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292395454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16323856,0.004862546,0.74263656,0.0070450343,0.00028466317,0.003729655,0.002705333,0.0011376437,0.07436003],"genre_scores_gemma":[0.5226559,0.0025126783,0.46293023,0.00038969357,0.000034566892,0.0016594063,0.001211301,0.00021521663,0.008390993],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.991574,0.0040114587,0.0005277984,0.0010332273,0.0023012704,0.00055236445],"domain_scores_gemma":[0.9819001,0.011553037,0.0018040478,0.000894799,0.0034144786,0.000433613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0118002305,0.0017686809,0.0015271846,0.003021623,0.0016450384,0.00684831,0.00195823,0.0019958834,0.013208029],"category_scores_gemma":[0.026903214,0.0010040025,0.0020208873,0.002366296,0.0020757192,0.0032092382,0.0018452131,0.0021612018,0.0015608089],"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":[0.0011054428,0.0005323968,0.03848352,0.0053741923,0.00065926247,0.00046269927,0.004967846,0.29004273,0.010176513,0.10984432,0.009937192,0.52841395],"study_design_scores_gemma":[0.00046626022,0.00260783,0.09053874,0.0066047953,0.001146158,0.00045385803,0.020744307,0.47511172,0.026373327,0.21078888,0.16449562,0.0006684903],"about_ca_topic_score_codex":0.037995726,"about_ca_topic_score_gemma":0.065614,"teacher_disagreement_score":0.037995726,"about_ca_system_score_codex":0.008056536,"about_ca_system_score_gemma":0.016875155,"threshold_uncertainty_score":0.075549126},"labels":[],"label_agreement":null},{"id":"W4293071717","doi":"10.3389/frwa.2022.723639","title":"Performance Modeling and Simulation for Wastewater Collection Networks","year":2022,"lang":"en","type":"article","venue":"Frontiers in Water","topic":"Water Systems and 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":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Benchmarking; Asset management; Environmental economics; Sustainability; Business; Finance; Operations management; Economics","score_opus":0.006957130110062466,"score_gpt":0.1717397467588516,"score_spread":0.16478261664878913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293071717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29807115,0.0006830388,0.60320646,0.0018945872,0.00014099544,0.00036655556,0.0018649636,0.0010270098,0.092745245],"genre_scores_gemma":[0.9525323,0.00049372576,0.031489536,0.00009427348,0.000025285217,0.00030728176,0.00069857785,0.00009965861,0.014259301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942005,0.00018104578,0.000029040486,0.00009705193,0.0001359658,0.00013688905],"domain_scores_gemma":[0.998792,0.0006409558,0.00016597111,0.000057119865,0.00027398978,0.00007006205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009185913,0.0009997493,0.0008733373,0.00092693925,0.0008764872,0.0019959954,0.001512026,0.0020505385,0.0039350167],"category_scores_gemma":[0.0030688916,0.0004580098,0.0009866026,0.0011621309,0.0008714217,0.001292615,0.0013040991,0.0010925875,0.00045608083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054441703,0.000008002802,0.00029546084,0.000005647324,0.0000023480382,0.000014758749,0.0000130117505,0.99688727,0.00008409775,0.001972489,0.00007672955,0.00063469936],"study_design_scores_gemma":[0.0000030890358,0.0000054139923,0.0000906396,0.000002572994,0.0000013437965,0.0000031758664,0.000017109453,0.99862504,0.00006491003,0.000875214,0.00030872427,0.000002761262],"about_ca_topic_score_codex":0.08939486,"about_ca_topic_score_gemma":0.03255725,"teacher_disagreement_score":0.08939486,"about_ca_system_score_codex":0.0038316662,"about_ca_system_score_gemma":0.0025839834,"threshold_uncertainty_score":0.17774904},"labels":[],"label_agreement":null},{"id":"W4293213652","doi":"10.3390/w14172590","title":"The Crucial Importance of Air Valve Characterization to the Transient Response of Pipeline Systems","year":2022,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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 Toronto","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Inflow; Outflow; Transient (computer programming); Reliability (semiconductor); Flow (mathematics); Consistency (knowledge bases); Environmental science; Pipeline (software); Upstream (networking); Isentropic process; Computer science; Mechanics; Simulation; Marine engineering; Meteorology; Engineering; Mechanical engineering; Physics; Thermodynamics","score_opus":0.006200243193968277,"score_gpt":0.18020558407066345,"score_spread":0.17400534087669517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293213652","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":[0.7945847,0.00014619037,0.199376,0.00018461117,0.00002582895,0.00012630191,0.00018099352,0.0006577362,0.0047175167],"genre_scores_gemma":[0.99294055,0.000031437517,0.00669383,0.0000072675793,0.000002647591,0.000017212806,0.00005396814,0.00003902468,0.00021409194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983546,0.0006890301,0.00012051897,0.0001504493,0.0005717721,0.00011352729],"domain_scores_gemma":[0.989852,0.007166778,0.00071373675,0.0011346949,0.0010642224,0.00006848067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023695086,0.00041365038,0.00057294423,0.00063978595,0.0005404431,0.0015192442,0.0007486955,0.0008076279,0.0012681307],"category_scores_gemma":[0.019339504,0.0002114962,0.0003717928,0.00044335175,0.0008331362,0.0017829753,0.00057368004,0.0007567454,0.00020730354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074813765,0.00023044305,0.030674506,0.00035267757,0.000033928332,0.00042087963,0.0016168875,0.81652164,0.066161506,0.0049142363,0.00039594297,0.0779292],"study_design_scores_gemma":[0.0000245162,0.0010820217,0.015884962,0.000077629,0.000033935565,0.0003728186,0.0010243882,0.86690754,0.10868149,0.0029785142,0.002844534,0.00008775814],"about_ca_topic_score_codex":0.0035193341,"about_ca_topic_score_gemma":0.0021644146,"teacher_disagreement_score":0.0035193341,"about_ca_system_score_codex":0.0005298339,"about_ca_system_score_gemma":0.0008216775,"threshold_uncertainty_score":0.0125312805},"labels":[],"label_agreement":null},{"id":"W4294797025","doi":"10.1371/journal.pwat.0000040","title":"Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?","year":2022,"lang":"en","type":"article","venue":"PLOS Water","topic":"Water Systems and 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":"Centre for Global Health Research; York University","funders":"United Nations High Commissioner for Refugees; Natural Sciences and Engineering Research Council of Canada; Global Affairs Canada; Achmea; Enhancing Learning and Research for Humanitarian Assistance; Grand Challenges Canada; York University; Government of the United Kingdom; United States Agency for International Development","keywords":"Weighting; Statistics; Mean squared error; Residual; Probabilistic logic; Point estimation; Reliability (semiconductor); Econometrics; Computer science; Mathematics; Algorithm","score_opus":0.023955492429919675,"score_gpt":0.19546673948081214,"score_spread":0.17151124705089246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294797025","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":[0.50318885,0.0004691756,0.4910562,0.0012853142,0.000060453116,0.00007711776,0.00029343477,0.0002650239,0.0033044457],"genre_scores_gemma":[0.98509187,0.00011288768,0.014128407,0.000043543416,0.000012949223,0.000023667691,0.00010842955,0.000011423727,0.00046679733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999647,0.00014872792,0.000024844856,0.000061342136,0.00007169516,0.00004633497],"domain_scores_gemma":[0.9972627,0.0019098455,0.00026679423,0.00009098434,0.00041588527,0.000053768017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015850918,0.0005365799,0.0005489659,0.00043637288,0.00020885961,0.0007946039,0.00066538196,0.0008199762,0.0006409165],"category_scores_gemma":[0.0063219867,0.00031504798,0.00035461158,0.0003867377,0.00026444427,0.0013522718,0.0004944856,0.0008367326,0.00009626833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023165496,0.000013457184,0.0013814518,0.000009353345,0.000008818016,0.000010794852,0.000010185934,0.9909435,0.0001706486,0.00029129637,0.00008180153,0.0070556784],"study_design_scores_gemma":[0.0000011308333,0.0000089404475,0.00032536386,0.000002393575,0.0000018138867,0.0000022736617,0.0000057137827,0.9990876,0.00014080014,0.00038436824,0.000037404014,0.0000022688055],"about_ca_topic_score_codex":0.01822571,"about_ca_topic_score_gemma":0.01316615,"teacher_disagreement_score":0.01822571,"about_ca_system_score_codex":0.0008714902,"about_ca_system_score_gemma":0.00083273504,"threshold_uncertainty_score":0.036239207},"labels":[],"label_agreement":null},{"id":"W4300967501","doi":"10.1002/aws2.1309","title":"Integrated asset management of urban water and wastewater systems","year":2022,"lang":"en","type":"article","venue":"AWWA Water Science","topic":"Water Systems and 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 Waterloo","funders":"","keywords":"Wastewater; Asset management; Asset (computer security); Integrated business planning; Mains electricity; Business; Service (business); Capital (architecture); Integrated water resources management; Environmental economics; Water supply; Water resources; Environmental science; Finance; Computer science; Environmental engineering; Engineering; Economics; Computer security","score_opus":0.007195797011934113,"score_gpt":0.17349725085323975,"score_spread":0.16630145384130562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300967501","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":[0.61566955,0.000214053,0.37394187,0.00028801203,0.000014548344,0.0001452175,0.00051133137,0.0005200012,0.008695456],"genre_scores_gemma":[0.98686856,0.00004300882,0.012190325,0.0000066437774,0.0000022370218,0.000022207916,0.0001140409,0.000008831475,0.00074410584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995369,0.00014283456,0.000024720597,0.000080547754,0.00013040059,0.00008474515],"domain_scores_gemma":[0.9995511,0.00017740516,0.00008059884,0.00003441049,0.000108584485,0.000047928297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000673694,0.00039102059,0.0004946035,0.0010243127,0.00044016336,0.0017358087,0.00063382305,0.00040071973,0.0017650477],"category_scores_gemma":[0.0015735691,0.00024438748,0.0002872876,0.0012064754,0.00042521747,0.0011880118,0.0011787949,0.0003661959,0.00011898114],"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":[0.00003347467,0.00003844022,0.0045153135,0.000014634855,0.00002825422,0.000071674214,0.000046073477,0.9646855,0.00087078026,0.0038534387,0.00020601139,0.02563631],"study_design_scores_gemma":[0.000006905674,0.000017595989,0.0011012071,0.000002878837,0.000007919479,0.000007870427,0.0000455281,0.99519193,0.0005562222,0.0025804562,0.00047714906,0.0000043165096],"about_ca_topic_score_codex":0.04228351,"about_ca_topic_score_gemma":0.03856805,"teacher_disagreement_score":0.04228351,"about_ca_system_score_codex":0.0021271603,"about_ca_system_score_gemma":0.0016931826,"threshold_uncertainty_score":0.084074795},"labels":[],"label_agreement":null},{"id":"W4301848574","doi":"","title":"Asset management for water and sewer networks: the contribution of ASTEE","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Water Systems and 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":"GDG Environnement","funders":"","keywords":"Asset management; Asset (computer security); Business; Environmental science; Computer science; Finance; Computer security","score_opus":0.007387570414173211,"score_gpt":0.19231854324276332,"score_spread":0.1849309728285901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301848574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0803849,0.10192561,0.6987168,0.041409966,0.004923016,0.00011467058,0.0008572796,0.00039753385,0.07127022],"genre_scores_gemma":[0.71462184,0.07095273,0.12999961,0.0016144838,0.0059319553,0.00007656922,0.0007086977,0.00042404368,0.07567004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99929607,0.0002173204,0.000033127937,0.00013614538,0.00022651731,0.00009074411],"domain_scores_gemma":[0.9985719,0.00058124424,0.000054064112,0.00012221694,0.000513288,0.00015726827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014514102,0.0012351555,0.0011082767,0.0013134492,0.0006623839,0.0029558097,0.0010895411,0.0016538193,0.003595119],"category_scores_gemma":[0.00306191,0.00026828275,0.00095737196,0.0022336557,0.0007411322,0.002733815,0.001750011,0.0020286138,0.00064071926],"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":[0.00013170682,0.00024404263,0.002829735,0.00057221804,0.00030410173,0.00037579818,0.00030410246,0.42723778,0.0028940567,0.18233903,0.06425614,0.31851125],"study_design_scores_gemma":[0.000025625952,0.000065061635,0.001795588,0.00014778902,0.00012686342,0.00019280726,0.00020647027,0.7022534,0.0023701536,0.18995723,0.10281088,0.000048112124],"about_ca_topic_score_codex":0.014398676,"about_ca_topic_score_gemma":0.006581,"teacher_disagreement_score":0.014398676,"about_ca_system_score_codex":0.0018665743,"about_ca_system_score_gemma":0.002381408,"threshold_uncertainty_score":0.02862972},"labels":[],"label_agreement":null},{"id":"W4303022718","doi":"10.1007/s11269-022-03347-2","title":"Comparison of the Performance of a Surrogate Based Gaussian Process, NSGA2 and PSO Multi-objective Optimization of the Operation and Fuzzy Structural Reliability of Water Distribution System: Case Study for the City of Asmara, Eritrea","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":13,"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 British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Multi-objective optimization; Mathematical optimization; Harmony search; Surrogate model; Optimization problem; Computer science; Schedule; Maximization; Mathematics","score_opus":0.010665671976446465,"score_gpt":0.2267563975768062,"score_spread":0.21609072560035975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303022718","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":[0.9137945,0.0008660625,0.072316594,0.0003105397,0.00008738733,0.000046341534,0.00018563379,0.0003056238,0.01208738],"genre_scores_gemma":[0.993349,0.00009620616,0.0055865888,0.000012415729,0.0000043613245,0.000013817711,0.000096965516,0.000021074879,0.0008195261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949694,0.00023855682,0.000024500496,0.00004472789,0.00012844223,0.000066782806],"domain_scores_gemma":[0.99827015,0.0011122782,0.00009784688,0.00008483421,0.00037258482,0.000062266954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019241011,0.0006873351,0.0007701751,0.0007884612,0.00038218495,0.0010124611,0.00046793872,0.0010593028,0.00090877706],"category_scores_gemma":[0.0031840056,0.00021442497,0.0006004328,0.00072650175,0.00040007086,0.00053693104,0.00041553084,0.0005022235,0.00012278974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023656577,0.00009102887,0.0012452928,0.000068226,0.000033761986,0.000034762885,0.000033855813,0.98529667,0.0009847259,0.00091593555,0.00030025796,0.01075894],"study_design_scores_gemma":[0.00001352377,0.00015466231,0.0015202551,0.000005371596,0.000010567486,0.0000106484695,0.000028360084,0.9971648,0.0007382218,0.00020786718,0.00014070055,0.0000050818176],"about_ca_topic_score_codex":0.013198151,"about_ca_topic_score_gemma":0.009380531,"teacher_disagreement_score":0.013198151,"about_ca_system_score_codex":0.0007585948,"about_ca_system_score_gemma":0.0007923658,"threshold_uncertainty_score":0.026242614},"labels":[],"label_agreement":null},{"id":"W4304086175","doi":"10.2166/ws.2022.350","title":"Optimal design of district metered areas based on improved particle swarm optimization method for water distribution systems","year":2022,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Particle swarm optimization; Mathematical optimization; Cluster analysis; Computer science; Multi-objective optimization; Cluster (spacecraft); Algorithm; Mathematics; Artificial intelligence","score_opus":0.010425419731347075,"score_gpt":0.2130162071727456,"score_spread":0.20259078744139852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304086175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060501236,0.00024242498,0.9332548,0.00016440709,0.000044856733,0.00010111966,0.000045414712,0.00017020768,0.005475587],"genre_scores_gemma":[0.89899164,0.00015597248,0.09788982,0.000042488035,0.000015444986,0.00023440381,0.000058377,0.000024063203,0.0025878155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997969,0.00008452093,0.000008120298,0.00003812473,0.000041937605,0.000030409254],"domain_scores_gemma":[0.99975747,0.00012358795,0.00003173665,0.000010833623,0.00005749978,0.000018990006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063368323,0.0006507505,0.0007845004,0.0006011526,0.0004022363,0.00086193526,0.00053103414,0.00072146865,0.0019228011],"category_scores_gemma":[0.00084519427,0.00038292856,0.00049995637,0.00043521862,0.00045646276,0.0005069475,0.00047316804,0.00047821857,0.00012782925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020607784,0.000015795775,0.00023106483,0.00002231263,0.000009664968,0.000014732946,0.000013605699,0.99199194,0.00081273995,0.0010065094,0.00017744578,0.005683623],"study_design_scores_gemma":[0.0000049979376,0.000015860556,0.000066719345,0.0000013938513,0.000002402427,0.0000012905631,0.0000047248727,0.99948764,0.00010243466,0.00021096927,0.000100380224,0.0000011673006],"about_ca_topic_score_codex":0.0053662607,"about_ca_topic_score_gemma":0.0046037366,"teacher_disagreement_score":0.0053662607,"about_ca_system_score_codex":0.00063120347,"about_ca_system_score_gemma":0.00093948684,"threshold_uncertainty_score":0.010670066},"labels":[],"label_agreement":null},{"id":"W4304974394","doi":"10.5772/intechopen.106261","title":"Detection and Localization of a Failure in a Pipeline Using a Kalman Filter: An Intelligent Integrated Approach Powered by Bayesian Classification","year":2022,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Water Systems and 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 New Brunswick","funders":"","keywords":"Kalman filter; Computer science; Bayesian probability; Nonlinear system; Classifier (UML); Real-time computing; Constant false alarm rate; Scheduling (production processes); Sensor fusion; ALARM; False alarm; Artificial intelligence; Data mining; Engineering; Control theory (sociology)","score_opus":0.02202326557935497,"score_gpt":0.2148376531582096,"score_spread":0.19281438757885463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304974394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032592502,0.0003368193,0.99460274,0.00004891099,0.000019416431,0.000015821559,0.000023357115,0.00051535957,0.001178293],"genre_scores_gemma":[0.4266937,0.0016323905,0.5621126,0.0001258762,0.0001044815,0.00014543052,0.00021252906,0.00014060394,0.008832361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996829,0.000050826082,0.000021976834,0.000085749925,0.00013429782,0.00002427867],"domain_scores_gemma":[0.99976844,0.000087590066,0.0000317134,0.000030094245,0.000074682066,0.0000074310033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052353035,0.00075482525,0.00076494046,0.0006098685,0.0002684562,0.0009889582,0.0007604199,0.00090115087,0.0013017165],"category_scores_gemma":[0.00093672186,0.00037936703,0.00062969467,0.0005183734,0.00037117855,0.0011142403,0.0005339447,0.0005855765,0.0007275153],"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":[0.00015913724,0.000110114524,0.002087078,0.00025759332,0.000106152525,0.00009956693,0.0001384882,0.26923996,0.04229385,0.011720311,0.0032619932,0.6705257],"study_design_scores_gemma":[0.0000063691436,0.00007664699,0.0011193899,0.000029722907,0.00003443754,0.000076387354,0.000018582388,0.9848299,0.006332173,0.0045966646,0.0028527419,0.000026926105],"about_ca_topic_score_codex":0.0035060798,"about_ca_topic_score_gemma":0.0037026745,"teacher_disagreement_score":0.0035060798,"about_ca_system_score_codex":0.00054700224,"about_ca_system_score_gemma":0.00067795266,"threshold_uncertainty_score":0.0069713593},"labels":[],"label_agreement":null},{"id":"W4306175592","doi":"10.1088/1755-1315/1079/1/012003","title":"Investigation of Water Hammer Overpressure in the Hydraulic Passages of Hydropower Plants Equipped with Francis Turbines","year":2022,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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":true,"ca_institutions":"Université du Québec à Trois-Rivières; Hydro-Québec","funders":"","keywords":"Overpressure; Water hammer; Hydropower; Hammer; Experimental data; Marine engineering; Geotechnical engineering; Transient (computer programming); Environmental science; Computer science; Engineering; Structural engineering; Mechanical engineering; Mathematics; Statistics","score_opus":0.0077374822890212405,"score_gpt":0.1617277255123724,"score_spread":0.15399024322335117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306175592","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":[0.998833,0.000017052038,0.0006686114,0.000008486767,0.0000018241822,0.0000080556265,0.00015285735,0.000026982416,0.0002831037],"genre_scores_gemma":[0.9995252,0.000009614074,0.00025753528,0.0000016166821,5.404908e-7,0.0000032419557,0.00008105825,0.0000027444412,0.000118496566],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981266,0.000026207728,0.000010498644,0.000038786035,0.0000703839,0.000041426312],"domain_scores_gemma":[0.99920976,0.00035972282,0.00010930802,0.00006246814,0.00019782578,0.000060808852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000368525,0.00021129768,0.00029767508,0.0007604028,0.0003501227,0.0004519997,0.00036547202,0.0004034875,0.00090816984],"category_scores_gemma":[0.0010782075,0.00016423431,0.0002087266,0.0004535282,0.0004209063,0.00036355475,0.0002197314,0.00032590528,0.00015514782],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013015625,0.00068987586,0.5095088,0.0005363534,0.00016936749,0.0037009194,0.0017188475,0.2064793,0.20331098,0.00086593104,0.0018037425,0.069914356],"study_design_scores_gemma":[0.000017589142,0.00073208596,0.7481966,0.000037322534,0.000042668285,0.00024191888,0.0014761785,0.21563523,0.03250069,0.0002998265,0.00075655954,0.00006330663],"about_ca_topic_score_codex":0.027576428,"about_ca_topic_score_gemma":0.046022594,"teacher_disagreement_score":0.027576428,"about_ca_system_score_codex":0.00062805926,"about_ca_system_score_gemma":0.00040542244,"threshold_uncertainty_score":0.054831803},"labels":[],"label_agreement":null},{"id":"W4306251657","doi":"10.1088/1755-1315/1079/1/012017","title":"Experimental and numerical validation of a Francis turbine draft tube designed for mitigation of pressure fluctuations","year":2022,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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":"Hydro-Québec","funders":"","keywords":"Draft tube; Computational fluid dynamics; Flow (mathematics); Internal flow; Hull; Turbine; Mechanics; Francis turbine; Tube (container); Range (aeronautics); Complex geometry; Mechanical engineering; Engineering; Marine engineering; Geometry; Aerospace engineering; Physics; Mathematics","score_opus":0.007955199703810019,"score_gpt":0.18822976636498093,"score_spread":0.18027456666117092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306251657","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":[0.9802357,0.00007588743,0.0182691,0.00003890119,0.000043114593,0.00008643984,0.00022680816,0.0003052561,0.0007189179],"genre_scores_gemma":[0.9926662,0.0000381269,0.006480735,0.0000070667775,0.0000042787797,0.000046598896,0.00013192883,0.00002269213,0.00060244853],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994979,0.000087802844,0.000032031927,0.00007981909,0.0002347858,0.00006752299],"domain_scores_gemma":[0.99906045,0.00024432119,0.000108918764,0.00018142597,0.00032855506,0.00007629528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007589152,0.0005479473,0.00065948075,0.00042105236,0.0006010489,0.00044251158,0.00066054723,0.00088258536,0.0013910196],"category_scores_gemma":[0.0016732041,0.00016310567,0.0003485094,0.0002640072,0.00091516075,0.00040596604,0.00036848598,0.0004058333,0.0002349888],"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":[0.000994912,0.00036705408,0.005602912,0.00027339946,0.000020846783,0.0005813543,0.0003577307,0.04490166,0.92439556,0.00095256633,0.00064419134,0.020907704],"study_design_scores_gemma":[0.000069278,0.0053700237,0.01753494,0.000033563843,0.000037441176,0.00022351487,0.00021218021,0.09794443,0.8758548,0.00014894157,0.0025002537,0.00007048268],"about_ca_topic_score_codex":0.0012120636,"about_ca_topic_score_gemma":0.0011710079,"teacher_disagreement_score":0.0013910196,"about_ca_system_score_codex":0.0003605818,"about_ca_system_score_gemma":0.00040004455,"threshold_uncertainty_score":0.004653454},"labels":[],"label_agreement":null},{"id":"W4306251675","doi":"10.1088/1755-1315/1079/1/012058","title":"Comparison of fatigue loading in Francis turbine runners using extreme values interpolation","year":2022,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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; École de Technologie Supérieure","funders":"","keywords":"Interpolation (computer graphics); Kriging; Extreme value theory; Turbine; Marine engineering; Power (physics); Francis turbine; Environmental science; Computer science; Mathematics; Statistics; Engineering; Mechanical engineering; Artificial intelligence; Motion (physics); Physics","score_opus":0.035852240294491716,"score_gpt":0.23126994620859911,"score_spread":0.1954177059141074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306251675","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":[0.9237265,0.00008164826,0.074879,0.000022532942,0.000009343132,0.000018409468,0.00021551369,0.00019466493,0.00085236505],"genre_scores_gemma":[0.99024653,0.000021594085,0.009301453,0.0000025241504,0.0000014485648,0.0000108243385,0.00026055204,0.000013697398,0.00014144921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961275,0.0001387282,0.000023542434,0.0000809531,0.00008853306,0.00005551968],"domain_scores_gemma":[0.9982862,0.0010937328,0.00015106812,0.00015794914,0.00025896332,0.000052105675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010638874,0.00045260933,0.00050606835,0.0011363005,0.00019223831,0.0004955286,0.0005891372,0.0006514505,0.00073555845],"category_scores_gemma":[0.0031271917,0.00020213079,0.0006085908,0.00096745364,0.0002698566,0.00049115455,0.0003158127,0.0004438708,0.00019269674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006022212,0.00009337472,0.046076834,0.00013020432,0.00011982133,0.00016596199,0.00020899005,0.8913739,0.008493543,0.00055357,0.00026670954,0.051914938],"study_design_scores_gemma":[0.000004741436,0.00016285513,0.032160316,0.000014920538,0.000016499567,0.000039602073,0.00008997017,0.96339446,0.0036512641,0.00025422563,0.00018270628,0.000028544633],"about_ca_topic_score_codex":0.008614578,"about_ca_topic_score_gemma":0.007863649,"teacher_disagreement_score":0.008614578,"about_ca_system_score_codex":0.0002810921,"about_ca_system_score_gemma":0.0002495502,"threshold_uncertainty_score":0.017128885},"labels":[],"label_agreement":null},{"id":"W4306888769","doi":"10.2166/aqua.2022.206","title":"Does intermittent supply result in hydraulic transients? Mixed evidence from two systems","year":2022,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Tata Center for Technology and Design, Massachusetts Institute of Technology; Inter-American Development Bank; Blum Center for Developing Economies, University of California Berkeley; United States Agency for International Development","keywords":"Environmental science; Water supply; High pressure; Surge; Engineering; Environmental engineering; Electrical engineering","score_opus":0.023842602900557074,"score_gpt":0.26368531925951744,"score_spread":0.23984271635896037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306888769","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":[0.9465078,0.035192568,0.004564382,0.0021953154,0.00021816947,0.00017113119,0.0023260626,0.00006039058,0.008764194],"genre_scores_gemma":[0.99143136,0.0053678798,0.00092845544,0.00057042285,0.00015431439,0.000047621586,0.0011557586,0.000025366538,0.00031876523],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9901929,0.0034136507,0.001937076,0.0022608,0.0014642021,0.00073149597],"domain_scores_gemma":[0.8701396,0.082252204,0.028424315,0.008612389,0.008309179,0.0022623823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011888907,0.00058572774,0.0009737785,0.002351514,0.0008944823,0.0032927305,0.0018999305,0.0017845196,0.007812],"category_scores_gemma":[0.04859366,0.000646171,0.0014181506,0.0033496835,0.0025765833,0.0024876783,0.0024421841,0.0013941819,0.0006812513],"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":[0.004635808,0.00056363514,0.9319399,0.0060799555,0.0036308307,0.0010329498,0.0020901929,0.0004414336,0.0012714652,0.0014398907,0.001250444,0.045623504],"study_design_scores_gemma":[0.00026793007,0.0029851175,0.9670648,0.0028765309,0.004482086,0.0020031987,0.0042615114,0.0013583871,0.0020055692,0.0029503482,0.009626336,0.00011816675],"about_ca_topic_score_codex":0.0045059263,"about_ca_topic_score_gemma":0.0037906903,"teacher_disagreement_score":0.011888907,"about_ca_system_score_codex":0.00095620553,"about_ca_system_score_gemma":0.001147595,"threshold_uncertainty_score":0.06287527},"labels":[],"label_agreement":null},{"id":"W4307171308","doi":"10.3390/su142113788","title":"Hyperparameter Sensitivity Analysis of Deep Learning-Based Pipe Burst Detection Model for Multiregional Water Supply Networks","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Water Systems and Optimization","field":"Engineering","cited_by":14,"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":"Ministry of Environment - Saskatchewan","keywords":"Hyperparameter; Sensitivity (control systems); Hyperparameter optimization; Computer science; Artificial neural network; Artificial intelligence; Deep learning; Data set; Machine learning; Set (abstract data type); Activation function; Rectifier (neural networks); Function (biology); Pattern recognition (psychology); Data mining; Recurrent neural network; Engineering; Support vector machine","score_opus":0.00589998513152155,"score_gpt":0.20000762393970825,"score_spread":0.1941076388081867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307171308","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":[0.6796691,0.0020190415,0.3124657,0.000840675,0.00010283896,0.00022118048,0.000324283,0.00067537697,0.0036818532],"genre_scores_gemma":[0.99310124,0.00015912567,0.0060149836,0.000058581063,0.000006530352,0.000060599166,0.0001250249,0.000025711774,0.00044822122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884355,0.0004783651,0.000079735575,0.00024074224,0.0001891849,0.00016836388],"domain_scores_gemma":[0.99213165,0.0059723896,0.00050924084,0.00026391118,0.0010170371,0.00010577161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049943114,0.0016196658,0.00095909054,0.0010292061,0.0004962833,0.0010311552,0.0009919098,0.0014341098,0.00091230206],"category_scores_gemma":[0.012610163,0.00074451725,0.0013097984,0.00041392888,0.00078734255,0.0012950443,0.001221834,0.0023184128,0.00009864347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006653635,0.00001989105,0.0016032828,0.000036342895,0.000041318246,0.000048205962,0.000021899965,0.9946978,0.00057698035,0.00031506686,0.000085941254,0.0024867808],"study_design_scores_gemma":[0.0000033396404,0.00003430846,0.0005303817,0.0000109876255,0.000015157182,0.0000119526085,0.000013213019,0.998211,0.00075307064,0.00036392387,0.00004493345,0.000007675136],"about_ca_topic_score_codex":0.018799879,"about_ca_topic_score_gemma":0.006718498,"teacher_disagreement_score":0.018799879,"about_ca_system_score_codex":0.0021036193,"about_ca_system_score_gemma":0.0009624872,"threshold_uncertainty_score":0.037380874},"labels":[],"label_agreement":null},{"id":"W4309306075","doi":"10.3390/su142215104","title":"Development of the Methodology for Pipe Burst Detection in Multi-Regional Water Supply Networks Using Sensor Network Maps and Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"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":"Ministry of Environment - Saskatchewan","keywords":"Artificial neural network; Pressure sensor; Water supply; Computer science; Real-time computing; Engineering; Environmental science; Artificial intelligence; Environmental engineering","score_opus":0.0318242181022133,"score_gpt":0.25203066764607535,"score_spread":0.22020644954386204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309306075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005191074,0.000076710676,0.9934802,0.000051832085,0.000017969784,0.00005849576,0.00008510149,0.00059340347,0.00044511395],"genre_scores_gemma":[0.37808883,0.000279787,0.617414,0.00009675987,0.000056593253,0.0004906192,0.0005753484,0.00010443107,0.0028936258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996735,0.000042528678,0.000024543788,0.0001169372,0.00009954446,0.00004300197],"domain_scores_gemma":[0.99958915,0.000093409835,0.00006515208,0.000036093017,0.00019282545,0.000023392335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006336684,0.00089355756,0.0005069516,0.0011794459,0.00033325114,0.0006441124,0.0015109809,0.000778,0.0012446204],"category_scores_gemma":[0.0014093864,0.0005504799,0.00081115257,0.0008634947,0.00037667272,0.0008661192,0.0010468648,0.0011441528,0.00039706827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038633454,0.000058151974,0.0025577391,0.00010763597,0.00007718092,0.00015926144,0.000062580795,0.7365535,0.0062190713,0.004398111,0.0012425095,0.24852563],"study_design_scores_gemma":[0.0000015398064,0.000011805639,0.0002910092,0.0000043996133,0.0000048307493,0.000015895164,0.000009418327,0.9965101,0.0015652032,0.0011594617,0.0004227794,0.0000035049925],"about_ca_topic_score_codex":0.010059166,"about_ca_topic_score_gemma":0.009061937,"teacher_disagreement_score":0.010059166,"about_ca_system_score_codex":0.00081295084,"about_ca_system_score_gemma":0.001253427,"threshold_uncertainty_score":0.020001233},"labels":[],"label_agreement":null},{"id":"W4309668626","doi":"10.1177/11786302221137437","title":"Proximity of Water Wells to Public Water Testing Facilities in Alberta Using Drive Times","year":2022,"lang":"en","type":"article","venue":"Environmental Health Insights","topic":"Water Systems and 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 Calgary; University of Alberta","funders":"","keywords":"Sample (material); Environmental science; Water utility; Stewardship (theology); Water well; Groundwater; Water supply; Engineering; Environmental engineering; Geotechnical engineering","score_opus":0.021247835364405252,"score_gpt":0.20014685148869132,"score_spread":0.17889901612428608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309668626","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":[0.99664056,0.000112970345,0.0003975809,0.00004538345,0.0000013402591,0.000010075237,0.00048261782,0.00000988206,0.002299563],"genre_scores_gemma":[0.99811697,0.000118100084,0.0004456655,0.000006348323,8.716711e-7,0.000007530829,0.0002267875,0.0000017956817,0.00107595],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961394,0.000043860044,0.000014345341,0.00006646275,0.00015694204,0.000104473875],"domain_scores_gemma":[0.9992699,0.00015112701,0.00028245177,0.000024307092,0.00017056918,0.00010163038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021935838,0.00013328764,0.00012674561,0.0011818706,0.00072844606,0.00071306684,0.00040175096,0.00017168134,0.0019833709],"category_scores_gemma":[0.0012877244,0.0001719361,0.000114178554,0.0022709344,0.00042127864,0.00027237524,0.0006936256,0.00013902334,0.00011925114],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013414987,0.00004832805,0.963327,0.00006158721,0.000024262776,0.00050458155,0.0022048068,0.0041816565,0.0022515475,0.00086312124,0.00067312364,0.02572577],"study_design_scores_gemma":[0.0000025104143,0.000025506102,0.9926777,0.00002091512,0.00000677386,0.00012534375,0.0036852364,0.0018536418,0.00028246213,0.00013024267,0.0011790586,0.000010651119],"about_ca_topic_score_codex":0.78184783,"about_ca_topic_score_gemma":0.9084709,"teacher_disagreement_score":0.21815217,"about_ca_system_score_codex":0.004113704,"about_ca_system_score_gemma":0.003060315,"threshold_uncertainty_score":0.43887377},"labels":[],"label_agreement":null},{"id":"W4309751383","doi":"10.1061/jhend8.hyeng-13197","title":"Calculating Column Separation in Conduit Systems Using an Innovative Open Channel Based Model","year":2022,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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":"Thornhill Medical (Canada)","funders":"","keywords":"Open-channel flow; Finite volume method; Mechanics; Flow (mathematics); Riemann solver; Cavitation; Shock (circulatory); Solver; Channel (broadcasting); Riemann problem; Euler equations; Mathematics; Applied mathematics; Computer science; Physics; Mathematical optimization; Mathematical analysis; Riemann hypothesis","score_opus":0.04145110227572087,"score_gpt":0.27235904976701014,"score_spread":0.23090794749128926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309751383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15755361,0.00039299333,0.8214643,0.00025263336,0.00008413513,0.00013164495,0.0003711292,0.0007708692,0.018978689],"genre_scores_gemma":[0.9113567,0.00032023565,0.07743812,0.00009532934,0.000025820436,0.00029561875,0.00022117628,0.00012114618,0.010125903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999149,0.000013411911,0.000003454932,0.000018761,0.000029563567,0.00001988673],"domain_scores_gemma":[0.99982256,0.00007280976,0.000020165622,0.000015092598,0.0000458111,0.000023639403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017560486,0.00050592294,0.0005378664,0.00037711932,0.0005570058,0.0008947405,0.0012021294,0.0013412833,0.0021537829],"category_scores_gemma":[0.00044248143,0.0003059113,0.00051037053,0.00041832167,0.00059366314,0.00061402994,0.0006515274,0.0006210746,0.0002996589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014450256,0.000024173467,0.00035039725,0.000023846766,0.0000056340095,0.00006126146,0.000018726987,0.98838913,0.003371443,0.0051070508,0.00018229913,0.0024516368],"study_design_scores_gemma":[0.0000019896465,0.000004364507,0.00003792331,6.807142e-7,0.0000011481154,0.0000055781816,0.0000020698049,0.99940753,0.00019207633,0.00020354241,0.0001414302,0.000001727422],"about_ca_topic_score_codex":0.02134078,"about_ca_topic_score_gemma":0.011967912,"teacher_disagreement_score":0.02134078,"about_ca_system_score_codex":0.00082017115,"about_ca_system_score_gemma":0.0017879034,"threshold_uncertainty_score":0.042433143},"labels":[],"label_agreement":null},{"id":"W4310238830","doi":"10.1038/s41598-022-23126-8","title":"Urban water supply systems’ resilience under earthquake scenario","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Water Systems and 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 Windsor","funders":"","keywords":"Resilience (materials science); Water supply; Computer science; Environmental science; Environmental engineering; Materials science","score_opus":0.007264527038633553,"score_gpt":0.17629370029621358,"score_spread":0.16902917325758002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310238830","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":[0.9968612,0.000013484023,0.0010205033,0.000103444036,0.0000039834904,0.000014353805,0.00030215975,0.000036120236,0.0016447266],"genre_scores_gemma":[0.9996039,0.000009490047,0.00014213767,0.0000041167705,6.561703e-7,0.0000054602665,0.0000841266,0.0000024494182,0.00014747678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997353,0.000105811116,0.00001249784,0.000041128707,0.000024971672,0.00008019005],"domain_scores_gemma":[0.9989988,0.0005467125,0.00014151359,0.000078354744,0.00014741899,0.00008716047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005731428,0.00037482154,0.00033195232,0.0006404552,0.00038253432,0.0006651389,0.00051698164,0.00071526313,0.0017548533],"category_scores_gemma":[0.0018361589,0.0002285132,0.0004303109,0.00069998164,0.000646349,0.00069378794,0.00061676314,0.00045575688,0.000113914946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058212467,0.000034866327,0.006995997,0.000008659404,0.000022474906,0.00012162246,0.00003182824,0.99102026,0.000338128,0.0006123478,0.00016717061,0.0005884661],"study_design_scores_gemma":[0.000014657789,0.000082813385,0.0053229555,0.000004192008,0.000014880567,0.000020595608,0.00025568524,0.99313086,0.00042853478,0.0005674146,0.00014661459,0.0000108963595],"about_ca_topic_score_codex":0.02812918,"about_ca_topic_score_gemma":0.019704359,"teacher_disagreement_score":0.02812918,"about_ca_system_score_codex":0.0015775026,"about_ca_system_score_gemma":0.00062000233,"threshold_uncertainty_score":0.055930912},"labels":[],"label_agreement":null},{"id":"W4311611318","doi":"10.1080/00221686.2022.2132309","title":"Energy dissipation in a rapid filling vertical pipe with trapped air","year":2022,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and 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":"Fundamental Research Funds for the Central Universities; China Scholarship Council; Fok Ying Tong Education Foundation; National Natural Science Foundation of China","keywords":"Dissipation; Mechanics; Energy (signal processing); Thermal management of electronic devices and systems; Environmental science; Geology; Materials science; Meteorology; Physics; Thermodynamics; Mechanical engineering; Engineering","score_opus":0.028130608474801273,"score_gpt":0.26943357332306644,"score_spread":0.24130296484826516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311611318","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":[0.90177375,0.0001645697,0.09286409,0.00014530068,0.000028016537,0.00003899762,0.00013882683,0.00017752394,0.0046688532],"genre_scores_gemma":[0.9961234,0.00004476932,0.0024464105,0.000007639621,0.000002170914,0.000024698656,0.000026641486,0.000009910693,0.0013143955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99993134,0.000015158015,0.0000036423044,0.000011629238,0.000016026905,0.000022195094],"domain_scores_gemma":[0.9998616,0.00007024338,0.00002651027,0.000009854264,0.000016935823,0.0000148888785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017486316,0.0003648698,0.0004540728,0.0002107291,0.00033631336,0.0005028026,0.0005017082,0.00081962545,0.0007721761],"category_scores_gemma":[0.00048674116,0.0002788207,0.00041423002,0.00020861458,0.00069932686,0.00037272065,0.00051404565,0.00029643238,0.000066126195],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004520666,0.000017558348,0.0006648185,0.000022179242,0.0000054294132,0.000118453034,0.000039735565,0.99115753,0.005658772,0.00093919545,0.000048421196,0.0012827673],"study_design_scores_gemma":[0.0000073096257,0.000030870116,0.0002132536,0.000002069757,0.0000023074183,0.000014203334,0.000009524316,0.9988242,0.0006468585,0.00015989758,0.0000862438,0.0000032424978],"about_ca_topic_score_codex":0.0073173693,"about_ca_topic_score_gemma":0.0025402035,"teacher_disagreement_score":0.0073173693,"about_ca_system_score_codex":0.00043189185,"about_ca_system_score_gemma":0.00076188217,"threshold_uncertainty_score":0.014549553},"labels":[],"label_agreement":null},{"id":"W4311839068","doi":"10.2166/hydro.2022.040","title":"Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period","year":2022,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","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","keywords":"Categorical variable; Covariate; Bayesian probability; Series (stratigraphy); Time series; Variable (mathematics); Computer science; Linear regression; Autoregressive model; Econometrics; Statistics; Data mining; Mathematics; Geology","score_opus":0.006690021862068412,"score_gpt":0.16854271683281966,"score_spread":0.16185269497075125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311839068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22725505,0.00034182385,0.76846635,0.0005103385,0.000029804867,0.000074318676,0.00031274444,0.00023537119,0.0027741587],"genre_scores_gemma":[0.96651745,0.00014180853,0.030783763,0.000036642934,0.000016348695,0.00009181816,0.00021093833,0.000022326034,0.002178875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994887,0.00020288817,0.000022247752,0.00013368613,0.00008381589,0.00006860434],"domain_scores_gemma":[0.99871933,0.0007510493,0.00027751486,0.00004670887,0.00016463707,0.000040731768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021829302,0.0006746652,0.00096940366,0.0006249466,0.00028389826,0.0008330763,0.0011825586,0.00097534526,0.0012301672],"category_scores_gemma":[0.004188532,0.00075233524,0.00055942876,0.0005838133,0.0005777585,0.0008592706,0.0004950834,0.0009878804,0.00015581111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015612279,0.0000080755635,0.00038993458,0.00000434237,0.0000070234087,0.000007216315,0.0000050934227,0.99646497,0.000102519785,0.000795457,0.00007040849,0.0021293056],"study_design_scores_gemma":[0.0000019119007,0.0000036686147,0.0000946381,0.0000010483907,0.0000024759963,9.537508e-7,9.324189e-7,0.9996178,0.000038285267,0.00021197273,0.000024769492,0.0000014129803],"about_ca_topic_score_codex":0.029479101,"about_ca_topic_score_gemma":0.017202616,"teacher_disagreement_score":0.029479101,"about_ca_system_score_codex":0.0013208217,"about_ca_system_score_gemma":0.0015382232,"threshold_uncertainty_score":0.05861497},"labels":[],"label_agreement":null},{"id":"W4312441187","doi":"10.1109/sdpc55702.2022.9915815","title":"Development of an Automatic Recognition Model for Phase Resolved Partial Discharge Patterns in Hydro-Generators","year":2022,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Artificial neural network; Unsupervised learning; Noise (video); Representation (politics); Feature extraction; Phase (matter); Feature (linguistics); Machine learning; Image (mathematics); Physics","score_opus":0.03476950568728875,"score_gpt":0.24457452546684397,"score_spread":0.2098050197795552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312441187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027186772,0.00019817654,0.96936786,0.00032439,0.000041840955,0.00006129377,0.00020448859,0.0009033182,0.0017117669],"genre_scores_gemma":[0.83456206,0.00038303924,0.15631619,0.00015106675,0.000051320876,0.00045492913,0.0005610991,0.00011900005,0.007401266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998549,0.000016699276,0.000008756151,0.000070902955,0.0000316786,0.000017070248],"domain_scores_gemma":[0.9997936,0.00008418349,0.00003472473,0.000015654186,0.00006196354,0.000009946156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034132448,0.00046625923,0.00062617357,0.00035995911,0.0003213333,0.0007500105,0.001251812,0.0011873393,0.0011651524],"category_scores_gemma":[0.0006851788,0.00052657194,0.0008274205,0.00025812353,0.0005454256,0.0008033028,0.00056338747,0.001288601,0.00058560044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059366997,0.000068023335,0.0019562324,0.00008336762,0.000060975948,0.0001817053,0.00013188251,0.9229197,0.016475713,0.010807422,0.0013289286,0.045926705],"study_design_scores_gemma":[0.0000019313438,0.000007480824,0.0001507599,0.0000020670636,0.0000045287115,0.000015504871,0.000002555825,0.99789256,0.0007028345,0.00097629137,0.0002396197,0.000003939494],"about_ca_topic_score_codex":0.006990507,"about_ca_topic_score_gemma":0.0063299323,"teacher_disagreement_score":0.006990507,"about_ca_system_score_codex":0.0007317422,"about_ca_system_score_gemma":0.0009561227,"threshold_uncertainty_score":0.013899624},"labels":[],"label_agreement":null},{"id":"W4312629370","doi":"10.1115/ipc2022-87001","title":"CNN-Based Flow Pattern Identification Based On Flow-Induced Vibration Characteristics For Multiphase Flow Pipelines","year":2022,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Computer science; Flow (mathematics); Convolutional neural network; Noise (video); Short-time Fourier transform; Pipeline (software); Hilbert transform; Pipeline transport; Multiphase flow; Artificial intelligence; Vibration; Pattern recognition (psychology); Acoustics; Fourier transform; Engineering; Computer vision; Mechanics; Physics; Fourier analysis; Mechanical engineering; Filter (signal processing)","score_opus":0.014661812252393014,"score_gpt":0.21956456608349162,"score_spread":0.2049027538310986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312629370","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":[0.6491369,0.0005678145,0.3430663,0.00020714452,0.00012575147,0.00011109949,0.00093856896,0.0024245668,0.003421792],"genre_scores_gemma":[0.95168656,0.00016080588,0.045000575,0.000048062077,0.000018475921,0.00004886916,0.0009897375,0.000027106327,0.0020197546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989486,0.000007462269,0.0000061184705,0.000038852533,0.000026928847,0.000025830794],"domain_scores_gemma":[0.99987125,0.000024083474,0.000024454384,0.000018966683,0.000051412484,0.000009847202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023406195,0.0004976967,0.00032495684,0.0007123835,0.00014640909,0.00025057324,0.0004176091,0.00036246807,0.0011423298],"category_scores_gemma":[0.00049041194,0.00018264016,0.0003505978,0.0004888172,0.00014973157,0.0003805794,0.0003003631,0.00027328046,0.00029045893],"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":[0.0003801058,0.0002717209,0.017111689,0.0001651342,0.00014708072,0.0002960431,0.000073689655,0.24692163,0.14874531,0.0006883077,0.0034411955,0.5817581],"study_design_scores_gemma":[0.000003399797,0.00003073475,0.0053594233,0.000004389045,0.0000102697495,0.000021610083,0.00000825858,0.98558444,0.008650244,0.000100427584,0.00022169472,0.0000051663233],"about_ca_topic_score_codex":0.010917836,"about_ca_topic_score_gemma":0.011160915,"teacher_disagreement_score":0.010917836,"about_ca_system_score_codex":0.000468333,"about_ca_system_score_gemma":0.00043273813,"threshold_uncertainty_score":0.021708608},"labels":[],"label_agreement":null},{"id":"W4312697330","doi":"10.1115/ipc2022-87166","title":"Comparison of Pressure Decay Models for Liquid Pipelines","year":2022,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Alberta Energy","funders":"","keywords":"Interpolation (computer graphics); Linear interpolation; Pipeline transport; Pipeline (software); Spectral line; Range (aeronautics); Pressure measurement; Mathematics; Mechanics; Physics; Engineering; Mechanical engineering; Meteorology; Mathematical analysis; Polynomial","score_opus":0.030276995744140636,"score_gpt":0.25814630956900103,"score_spread":0.2278693138248604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312697330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45748505,0.00029188534,0.5329033,0.00025126358,0.000033048975,0.0001307273,0.00040826097,0.0011237027,0.007372786],"genre_scores_gemma":[0.9838811,0.00008487669,0.014893922,0.000014791036,0.0000041403778,0.00005258867,0.00021736119,0.000043256525,0.00080801046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952185,0.00014081917,0.000025060948,0.00007859039,0.00018089887,0.000052870433],"domain_scores_gemma":[0.9981346,0.0010778109,0.00017705851,0.000100197794,0.00046529714,0.000045024808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014206817,0.0006243373,0.00063639827,0.0008692299,0.00030262407,0.00072576094,0.0010461292,0.0005933481,0.0008567766],"category_scores_gemma":[0.0036685888,0.00030250844,0.0009080626,0.0006315374,0.00035620932,0.0007649423,0.0004342493,0.0008207443,0.00013788887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042233532,0.00002300623,0.0008354983,0.000017238626,0.000008232921,0.0000104744795,0.000014913555,0.992664,0.0004909915,0.0005729463,0.00006827965,0.0052521755],"study_design_scores_gemma":[0.0000015090199,0.000012934655,0.00014594462,0.0000012484304,0.0000016376194,0.0000017293129,0.0000035541213,0.9995011,0.00021201106,0.00007943866,0.00003681948,0.0000019731283],"about_ca_topic_score_codex":0.026265355,"about_ca_topic_score_gemma":0.008498512,"teacher_disagreement_score":0.026265355,"about_ca_system_score_codex":0.0014676095,"about_ca_system_score_gemma":0.0014184923,"threshold_uncertainty_score":0.052224934},"labels":[],"label_agreement":null},{"id":"W4313050355","doi":"10.14796/jwmm.c492","title":"Monitoring of Pressure Transients in Great Lakes Water Authority Water Transmission System","year":2022,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Transient (computer programming); Environmental science; Impact pressure; Pressure sensor; Transmission (telecommunications); Pressure measurement; Water level; Hydrology (agriculture); Meteorology; Engineering; Computer science; Geotechnical engineering; Telecommunications; Geography; Mechanical engineering","score_opus":0.01380977688724961,"score_gpt":0.1984203848145807,"score_spread":0.1846106079273311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313050355","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":[0.99195564,0.000049961393,0.0056788935,0.00006521634,0.000005540677,0.00003470739,0.00052771834,0.000450208,0.0012320704],"genre_scores_gemma":[0.99829847,0.000024629366,0.0011985246,0.000006400747,0.0000023673126,0.000010429496,0.00025047027,0.000007135356,0.00020157028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995963,0.00006941958,0.000020797925,0.00007543304,0.00021108318,0.000027011432],"domain_scores_gemma":[0.9991897,0.0003137908,0.00015698349,0.00004359336,0.0002505311,0.00004542201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037879474,0.00023823918,0.0002101971,0.00082069775,0.00030647058,0.0004238676,0.00037027043,0.00035613275,0.0006740893],"category_scores_gemma":[0.0015458305,0.00013075434,0.00012648519,0.0008842629,0.0002451922,0.00060590706,0.0002695172,0.00036640107,0.00012231283],"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":[0.0015067339,0.00078167464,0.4345115,0.0003794013,0.00022526341,0.0023351568,0.0019577525,0.17256598,0.2254511,0.00086800824,0.0061165616,0.15330087],"study_design_scores_gemma":[0.00005949303,0.0010486693,0.46799907,0.000020284151,0.00005812624,0.00045663898,0.0005515395,0.48500204,0.043106176,0.00030495846,0.0013237561,0.00006925212],"about_ca_topic_score_codex":0.011496277,"about_ca_topic_score_gemma":0.008472416,"teacher_disagreement_score":0.9885037,"about_ca_system_score_codex":0.0004770849,"about_ca_system_score_gemma":0.0003405665,"threshold_uncertainty_score":0.022858739},"labels":[],"label_agreement":null},{"id":"W4318700574","doi":"10.2166/ws.2023.020","title":"Full-scale determination of pipe wall and bulk chlorine degradation coefficients for different pipe categories","year":2023,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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 Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Degradation (telecommunications); Chlorine; Residual; Environmental science; Materials science; Waste management; Engineering; Metallurgy; Computer science","score_opus":0.007686065672155731,"score_gpt":0.20575579963478874,"score_spread":0.198069733962633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318700574","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":[0.99492973,0.000051682167,0.004136222,0.000003440154,0.0000013494777,0.000018850658,0.00054700393,0.00007273585,0.0002389447],"genre_scores_gemma":[0.99724054,0.000033062886,0.0017992153,0.0000023474192,5.042075e-7,0.000017981483,0.00044984673,0.000012195759,0.0004442814],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972063,0.000031224645,0.000017435175,0.00007305933,0.00012325865,0.00003438854],"domain_scores_gemma":[0.9992674,0.00024522812,0.00013637773,0.00007442229,0.00025029315,0.000026234737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045306952,0.00028599825,0.0003539118,0.00053566,0.00014774334,0.00022025622,0.00019075931,0.00024626596,0.0011107266],"category_scores_gemma":[0.00078810705,0.00015503188,0.0003333274,0.00051513605,0.00018535313,0.00024040598,0.00018423567,0.00020569279,0.00023978743],"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":[0.00052616635,0.00014943903,0.048244745,0.0002830173,0.00007242413,0.00007811664,0.00013534816,0.02034264,0.9048461,0.000051754254,0.00019881519,0.02507158],"study_design_scores_gemma":[0.000015992282,0.0012909992,0.40822718,0.000015886968,0.00008613275,0.0001229607,0.00015604422,0.0471526,0.54194206,0.00006575433,0.00088604947,0.000038402366],"about_ca_topic_score_codex":0.0035651217,"about_ca_topic_score_gemma":0.0049571786,"teacher_disagreement_score":0.0035651217,"about_ca_system_score_codex":0.00029230493,"about_ca_system_score_gemma":0.00016685328,"threshold_uncertainty_score":0.0070887804},"labels":[],"label_agreement":null},{"id":"W4319159879","doi":"10.3390/en16031501","title":"Comparison on Hydraulic Characteristics of Vertical and Horizontal Air-Cushion Surge Chambers in the Hydropower Station under Load Disturbances","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Water Systems and 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":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Surge; Water hammer; Hydropower; Surge tank; Marine engineering; Environmental science; Renewable energy; Flow (mathematics); Hydroelectricity; Volume of fluid method; Engineering; Geotechnical engineering; Mechanics; Mechanical engineering; Electrical engineering","score_opus":0.013260796894464064,"score_gpt":0.23809127978846797,"score_spread":0.22483048289400392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319159879","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":[0.992919,0.000071358656,0.0053342003,0.000044687313,0.000012035596,0.000013204153,0.0001429345,0.00010724286,0.001355338],"genre_scores_gemma":[0.9993383,0.000029300378,0.00029001923,0.0000034033967,0.0000020098068,0.000005371782,0.00006802648,0.000004677052,0.00025891658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972767,0.00003916595,0.000014254995,0.00005624599,0.000088961366,0.00007368646],"domain_scores_gemma":[0.99939096,0.00025802554,0.00007173356,0.000057517245,0.00015576974,0.000066004875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034037206,0.000257762,0.00036732017,0.000637673,0.00037926217,0.00062405353,0.00033455246,0.00046899397,0.001308473],"category_scores_gemma":[0.0008392688,0.0001957092,0.00037018908,0.00046145916,0.0005981555,0.0005687783,0.0003344874,0.00024885402,0.00015895965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001937708,0.00034436135,0.11386242,0.00047695742,0.00008754327,0.0011274958,0.0011721106,0.52040505,0.2815757,0.0033705395,0.0026458064,0.07299432],"study_design_scores_gemma":[0.00007329214,0.0010789098,0.09976817,0.0000268447,0.00006729463,0.0001233291,0.00097044895,0.8254869,0.06977842,0.0005548002,0.0019475153,0.00012413219],"about_ca_topic_score_codex":0.0063670785,"about_ca_topic_score_gemma":0.004065032,"teacher_disagreement_score":0.0063670785,"about_ca_system_score_codex":0.00046511728,"about_ca_system_score_gemma":0.0005867749,"threshold_uncertainty_score":0.012660027},"labels":[],"label_agreement":null},{"id":"W4321336776","doi":"10.1016/j.jenvman.2023.117463","title":"Multi-objective optimization of hydrant flushing in a water distribution system using a fast hybrid technique","year":2023,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Flushing; Multi-objective optimization; Decision support system; CVAR; Contamination; Pareto principle; Computer science; Operations research; Environmental science; Engineering; Risk management; Operations management; Expected shortfall; Data mining","score_opus":0.006141705174629328,"score_gpt":0.17949835207357603,"score_spread":0.1733566468989467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321336776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45212987,0.0006456477,0.53794366,0.00032724248,0.00010790484,0.00015256341,0.0001347296,0.0003286422,0.008229732],"genre_scores_gemma":[0.97323203,0.00009555426,0.024496948,0.000035251986,0.000017650325,0.0000990451,0.000046627996,0.000028770133,0.001948228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997501,0.00010639492,0.000010389148,0.00003375142,0.000047971545,0.000051403018],"domain_scores_gemma":[0.99933964,0.0004813769,0.00004827865,0.000016866748,0.00007824009,0.000035631354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010185009,0.0008388942,0.0013082823,0.00067050324,0.00074790255,0.0011123625,0.0006767945,0.0015005519,0.001810909],"category_scores_gemma":[0.0010141578,0.00073507184,0.001037391,0.00049120653,0.00055516977,0.00067121035,0.0007530686,0.00070912484,0.00011740556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034739383,0.000020502006,0.00015171674,0.00002153322,0.00002081567,0.000020685497,0.000007977563,0.9961098,0.00067073957,0.00015482317,0.000044053122,0.0027425701],"study_design_scores_gemma":[0.000005052768,0.000027171955,0.00007317743,9.100566e-7,0.000004092274,0.0000021120075,0.0000037507184,0.99966586,0.00014520269,0.0000462095,0.000024996909,0.0000016440271],"about_ca_topic_score_codex":0.012024282,"about_ca_topic_score_gemma":0.009885309,"teacher_disagreement_score":0.012024282,"about_ca_system_score_codex":0.0007557044,"about_ca_system_score_gemma":0.00096468127,"threshold_uncertainty_score":0.023908615},"labels":[],"label_agreement":null},{"id":"W4321457662","doi":"10.3390/su15053875","title":"Contribution of Air Management to the Energy Efficiency of Water Pipelines","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Water Systems and 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 Toronto","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Pipeline transport; Environmental science; Work (physics); Electricity; Airflow; Environmental engineering; Energy consumption; Engineering; Mechanical engineering","score_opus":0.0035124916343125764,"score_gpt":0.2013776670631023,"score_spread":0.19786517542878973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321457662","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":[0.8379832,0.0031616027,0.14328831,0.000693623,0.000053894863,0.00005120509,0.00010612694,0.00017783736,0.014484262],"genre_scores_gemma":[0.99748445,0.0002718735,0.0016749384,0.0000054119205,0.000006073822,0.0000035560745,0.000008990346,0.00000799798,0.0005367263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996592,0.00009888063,0.00001812763,0.000055951532,0.00010017719,0.000067649664],"domain_scores_gemma":[0.9996476,0.00015231293,0.00009717463,0.00003425445,0.0000518278,0.000016855029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037457666,0.00046769838,0.00032821263,0.0004083663,0.00030982203,0.0013215182,0.00035648077,0.00056775275,0.00081024645],"category_scores_gemma":[0.0011890975,0.00016429916,0.00024466935,0.0003364052,0.00063333364,0.0012919386,0.00055270013,0.00028627104,0.0001202324],"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":[0.00016893832,0.000077684395,0.009946428,0.00028287043,0.000057051308,0.00026359182,0.00009783444,0.7945076,0.071575664,0.009589189,0.0003078537,0.11312523],"study_design_scores_gemma":[0.000019389083,0.00069757545,0.017306456,0.000052895124,0.00008560366,0.0002299091,0.00032485795,0.9020896,0.06296986,0.009756634,0.006412298,0.00005486267],"about_ca_topic_score_codex":0.0028069534,"about_ca_topic_score_gemma":0.0027621198,"teacher_disagreement_score":0.0028069534,"about_ca_system_score_codex":0.0006471358,"about_ca_system_score_gemma":0.00059060764,"threshold_uncertainty_score":0.0055812597},"labels":[],"label_agreement":null},{"id":"W4321788979","doi":"10.1063/5.0138342","title":"Modeling geysers triggered by an air pocket migrating with running water in a pipeline","year":2023,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Water Systems and 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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Storm; Geology; Petroleum engineering; Physics; Meteorology","score_opus":0.01281603490475655,"score_gpt":0.2084247233719711,"score_spread":0.19560868846721455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321788979","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":[0.9619578,0.00016124915,0.032745976,0.00022475426,0.000044488508,0.00008610457,0.0002702352,0.00040427726,0.0041050883],"genre_scores_gemma":[0.9947246,0.00009256354,0.0032379427,0.000015076123,0.0000060685175,0.000038358634,0.000115714254,0.000036587793,0.0017330393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983466,0.000021932176,0.000010450039,0.000038289407,0.00003537927,0.000059166752],"domain_scores_gemma":[0.9997522,0.000100237354,0.000046877634,0.000013328513,0.000040357518,0.000046970807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030099403,0.00081499154,0.00051911257,0.000546277,0.0007208298,0.0009265525,0.00072733616,0.001510753,0.0017605787],"category_scores_gemma":[0.00088572304,0.00040846967,0.00074924255,0.00035175317,0.00073899317,0.00082297117,0.0008639651,0.00068394025,0.00019038937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013240924,0.000093335846,0.0075602434,0.000051282528,0.000018587547,0.00051489845,0.00012512488,0.97614413,0.011139072,0.0007123331,0.00030083698,0.0032077145],"study_design_scores_gemma":[0.000011396382,0.000036431302,0.0013209664,0.0000026912,0.0000071823392,0.000021670943,0.00006573707,0.99747413,0.0007492697,0.00012299269,0.00017955455,0.00000804829],"about_ca_topic_score_codex":0.028729003,"about_ca_topic_score_gemma":0.012470452,"teacher_disagreement_score":0.028729003,"about_ca_system_score_codex":0.00095333275,"about_ca_system_score_gemma":0.0010040121,"threshold_uncertainty_score":0.05712354},"labels":[],"label_agreement":null},{"id":"W4322761317","doi":"10.18280/mmep.100129","title":"Modeling the Impact of Pipeline Topography on the Performance of Pumping System","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Water Systems and 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":"Pipeline (software); Petroleum engineering; Geology; Marine engineering; Environmental science; Computer science; Engineering; Mechanical engineering","score_opus":0.02269289325685479,"score_gpt":0.19539418924060453,"score_spread":0.17270129598374975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322761317","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":[0.9458817,0.00011070273,0.049561407,0.000080347454,0.000013917334,0.00005168422,0.0002824562,0.00029217426,0.0037256533],"genre_scores_gemma":[0.9977108,0.00004983938,0.0017243391,0.0000038379444,0.0000015475539,0.00001227422,0.000050214137,0.000014058084,0.0004330769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982893,0.000035563466,0.000010465756,0.000037979957,0.000037411926,0.000049597693],"domain_scores_gemma":[0.99958926,0.00020168119,0.000068321344,0.00004811781,0.000071818846,0.000020767951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024380891,0.00057245954,0.00040421396,0.00029851546,0.00024554992,0.0007445797,0.0005257201,0.00083054986,0.0011928793],"category_scores_gemma":[0.00084045914,0.00032689638,0.00049720873,0.00036861654,0.0003819531,0.0006234358,0.00036665893,0.00034470594,0.00020015615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018532923,0.000024103303,0.0032780212,0.000022242411,0.0000062676722,0.0000831815,0.000019692403,0.990382,0.0041203755,0.00014280307,0.000039423307,0.0018633337],"study_design_scores_gemma":[0.000003793781,0.000051133615,0.0023282096,0.000002452614,0.000007623222,0.000018651699,0.00001761193,0.995955,0.0014305474,0.0000877571,0.00009094019,0.000006333835],"about_ca_topic_score_codex":0.009615362,"about_ca_topic_score_gemma":0.005319623,"teacher_disagreement_score":0.009615362,"about_ca_system_score_codex":0.0005074745,"about_ca_system_score_gemma":0.00077925576,"threshold_uncertainty_score":0.019118786},"labels":[],"label_agreement":null},{"id":"W4323363936","doi":"10.1029/2021wr031745","title":"On‐Line Warning System for Pipe Burst Using Bayesian Dynamic Linear Models","year":2023,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water Systems and 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":true,"ca_institutions":"Institut National de la Recherche Scientifique; McGill University; McGill University Health Centre","funders":"Mitacs","keywords":"Outlier; Constant false alarm rate; Computer science; Data mining; Warning system; Anomaly detection; ALARM; Bayesian probability; Nonlinear system; Set (abstract data type); Data set; Time series; Flow (mathematics); Engineering; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.0698516885584046,"score_gpt":0.31729339592997047,"score_spread":0.24744170737156587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323363936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06672949,0.00016690743,0.9262641,0.0003174848,0.000043398875,0.00009458028,0.00023385214,0.0037475305,0.0024027063],"genre_scores_gemma":[0.947826,0.00007996015,0.049648106,0.00009034183,0.000023515102,0.00010635859,0.00025964074,0.00009562442,0.0018704843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993279,0.00025654884,0.000037001162,0.0001297733,0.00017419977,0.00007463606],"domain_scores_gemma":[0.99822205,0.0009760354,0.00028176594,0.00007930017,0.00037672283,0.00006412327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014395885,0.00084149855,0.0009806317,0.0010697682,0.00035880078,0.0009935764,0.0013294491,0.0007527195,0.002528967],"category_scores_gemma":[0.0041529434,0.0005523486,0.0005449784,0.00038294846,0.00026959248,0.0009760528,0.0010391996,0.001224424,0.000488936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025061023,0.0001582422,0.0031978686,0.00006193811,0.000065872126,0.00006209934,0.000068414956,0.9195255,0.0020691855,0.002000634,0.0014250888,0.07111458],"study_design_scores_gemma":[0.0000032512983,0.000010461932,0.00011079607,0.0000020473947,0.0000024798037,0.0000018977356,0.0000023445193,0.9993893,0.00013206713,0.00027691585,0.00006623821,0.0000022776476],"about_ca_topic_score_codex":0.020999728,"about_ca_topic_score_gemma":0.015537652,"teacher_disagreement_score":0.020999728,"about_ca_system_score_codex":0.001075302,"about_ca_system_score_gemma":0.0010551814,"threshold_uncertainty_score":0.04175502},"labels":[],"label_agreement":null},{"id":"W4353020477","doi":"10.1038/s41545-023-00222-4","title":"Optimal design of water reuse networks in cities through decision support tool development and testing","year":2023,"lang":"en","type":"article","venue":"npj Clean Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":10,"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":"Horizon 2020 Framework Programme; Ministerio de Ciencia e Innovación; Consejo Superior de Investigaciones Científicas; Ministerio de Economía y Competitividad; Generalitat de Catalunya; Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades; Universitat de Girona; Centres de Recerca de Catalunya; Canadian Institute for Advanced Research","keywords":"Reclaimed water; Reuse; Context (archaeology); Water scarcity; Payback period; Decision support system; Computer science; Scarcity; Population; Network planning and design; Water resources; Environmental science; Civil engineering; Engineering; Production (economics); Waste management; Geography; Data mining","score_opus":0.03352346450106221,"score_gpt":0.21394326224889232,"score_spread":0.18041979774783012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353020477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2173181,0.0001707689,0.7704062,0.00036017975,0.000042287742,0.00035437985,0.00039098336,0.0019381772,0.009018903],"genre_scores_gemma":[0.7877525,0.00007605347,0.21066877,0.000053867945,0.0000060048806,0.00025045656,0.00026562074,0.00008516659,0.0008416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988858,0.00059149263,0.000051987176,0.00013562175,0.00019247341,0.00014263742],"domain_scores_gemma":[0.99388593,0.0049142083,0.00030374987,0.00023212035,0.00051205687,0.00015185865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025889382,0.0010882834,0.0007248855,0.001251836,0.00038702335,0.0013823318,0.0011998678,0.0012268884,0.0041502505],"category_scores_gemma":[0.0063428814,0.0006071013,0.0008308102,0.00062949304,0.00086643087,0.0009058624,0.0011852229,0.0007530137,0.0003298727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040147715,0.000049521608,0.0005180124,0.000033617976,0.0000100803245,0.000037709855,0.000017831062,0.98743755,0.00043389646,0.0018684965,0.00016757243,0.00938538],"study_design_scores_gemma":[0.000008862676,0.00001555706,0.000039943192,0.0000041649196,0.000002666759,0.000002603039,0.000007675517,0.998432,0.0003426117,0.0009885965,0.00015340015,0.000001926608],"about_ca_topic_score_codex":0.005964087,"about_ca_topic_score_gemma":0.0047154343,"teacher_disagreement_score":0.005964087,"about_ca_system_score_codex":0.0011589911,"about_ca_system_score_gemma":0.0015504084,"threshold_uncertainty_score":0.013883948},"labels":[],"label_agreement":null},{"id":"W4360615578","doi":"10.1017/jfm.2023.99","title":"Hydrodynamics of finite-length pipes at intermediate Reynolds numbers","year":2023,"lang":"en","type":"article","venue":"Journal of Fluid Mechanics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"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 Cambridge; York University; National Science Foundation","keywords":"Turbulence; Reynolds number; Laminar flow; Mechanics; Pressure drop; Pipe flow; Body orifice; Dimensionless quantity; Flow conditioning; Hydraulics; Flow (mathematics); Physics; Thermodynamics; Mechanical engineering; Engineering","score_opus":0.007143898696330941,"score_gpt":0.19034509201306596,"score_spread":0.183201193316735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360615578","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":[0.9377739,0.00021837479,0.05582439,0.0001626353,0.000013426743,0.000020977868,0.00013387535,0.00031783982,0.005534615],"genre_scores_gemma":[0.99702483,0.00007335036,0.002207825,0.000009822559,0.0000032279543,0.000011641928,0.000034438508,0.00001398275,0.0006208605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999027,0.000017482911,0.0000045370384,0.000025692896,0.000021406562,0.000028058406],"domain_scores_gemma":[0.99973124,0.00010422837,0.000058688205,0.00002825594,0.00003764049,0.00003992288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022404159,0.00019597719,0.00024355935,0.00040646244,0.0002862896,0.00051948434,0.00029419828,0.0003812134,0.0006839617],"category_scores_gemma":[0.0010378284,0.000163085,0.00016669538,0.00018317539,0.0010935939,0.00071246846,0.00041712864,0.0003298966,0.00014754709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032086068,0.00013247992,0.022173274,0.00011416008,0.00002263529,0.0005194219,0.0005592327,0.6614305,0.25006804,0.045841455,0.0008515586,0.017966352],"study_design_scores_gemma":[0.000015927944,0.00015881992,0.0151529275,0.000015571963,0.0000070030605,0.00008340975,0.000098195174,0.9634535,0.013584953,0.0068021314,0.0005898451,0.00003773247],"about_ca_topic_score_codex":0.0017362919,"about_ca_topic_score_gemma":0.00065619743,"teacher_disagreement_score":0.0017362919,"about_ca_system_score_codex":0.0006216241,"about_ca_system_score_gemma":0.00036880403,"threshold_uncertainty_score":0.0045101643},"labels":[],"label_agreement":null},{"id":"W4361215746","doi":"10.1016/j.jenvman.2023.117734","title":"Spatiotemporal optimization of water quality degradation monitoring in water distribution systems supplied by surface sources: A chronological and critical review","year":2023,"lang":"en","type":"review","venue":"Journal of Environmental Management","topic":"Water Systems and 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":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Water quality; Environmental science; Quality (philosophy); Sampling (signal processing); Surface water; Water supply; Distribution (mathematics); Current (fluid); Risk analysis (engineering); Computer science; Environmental engineering; Engineering; Business; Mathematics; Ecology","score_opus":0.0372737450414608,"score_gpt":0.271127435762432,"score_spread":0.23385369072097117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361215746","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":[0.00017737008,0.99919814,0.0002784479,0.00010074555,0.00006215401,0.0000036257727,0.000010010314,0.000002613183,0.00016687057],"genre_scores_gemma":[0.0015966386,0.9977366,0.0004079814,0.000073779884,0.00009085333,0.0000048965653,0.000014891051,0.0000014331644,0.00007303683],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993285,0.00011240026,0.000105765735,0.00016138796,0.0002369247,0.00005496209],"domain_scores_gemma":[0.9972247,0.001592742,0.0004264661,0.000076940865,0.00061272917,0.00006652153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002400383,0.001592716,0.0030236463,0.002371014,0.00025530445,0.0017263942,0.0017586742,0.0016573408,0.0014085418],"category_scores_gemma":[0.003101212,0.0006932767,0.0014479203,0.0041241427,0.000858054,0.0022634387,0.0009972217,0.0014779768,0.00051392807],"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":[0.00012055518,0.0001352386,0.000644702,0.057495486,0.00046622337,0.00015506084,0.00008956501,0.0029413933,0.0029713323,0.0062992643,0.00877387,0.9199073],"study_design_scores_gemma":[0.00006495829,0.0008476049,0.007015789,0.023063472,0.0022516737,0.0016233533,0.00032040788,0.0029001276,0.005837677,0.006814673,0.9490341,0.00022616262],"about_ca_topic_score_codex":0.0027674697,"about_ca_topic_score_gemma":0.0041061584,"teacher_disagreement_score":0.0030236463,"about_ca_system_score_codex":0.0010922232,"about_ca_system_score_gemma":0.0023770537,"threshold_uncertainty_score":0.012694597},"labels":[],"label_agreement":null},{"id":"W4362663514","doi":"10.2166/ws.2023.085","title":"Mobile DMA testing for leakage assessment: perspectives from Ontario, Canada","year":2023,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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":true,"ca_institutions":"University of Toronto; HydraTek (Canada)","funders":"","keywords":"Leakage (economics); Software deployment; Computer science; Context (archaeology); Reliability engineering; Environmental science; Engineering; Geography; Operating system","score_opus":0.008016543288366979,"score_gpt":0.20549678051564607,"score_spread":0.1974802372272791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362663514","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":[0.5209098,0.057129834,0.0351116,0.05630194,0.0008709294,0.0014301554,0.010011667,0.0009955022,0.31723863],"genre_scores_gemma":[0.9067696,0.023006536,0.02940746,0.003323528,0.00010067383,0.00020598955,0.0016046651,0.0001298756,0.035451688],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.996181,0.00046817554,0.000120892466,0.0002592958,0.0021627333,0.00080789666],"domain_scores_gemma":[0.9923287,0.00053138955,0.00029200668,0.00015090607,0.0061746463,0.00052241795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028342635,0.0007758437,0.0005652366,0.0016044939,0.003652237,0.0037892086,0.0018968063,0.000897197,0.004772128],"category_scores_gemma":[0.0035379534,0.00033836422,0.00045635222,0.0027681515,0.00201206,0.0010781485,0.0013814291,0.00092249207,0.00047971754],"study_design_candidate":"observational","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":[0.0016668809,0.0006156841,0.22941199,0.0042974856,0.000244739,0.003915585,0.010526218,0.018843796,0.06555484,0.03333383,0.08280008,0.5487889],"study_design_scores_gemma":[0.00020483232,0.0015295995,0.28171462,0.0028156417,0.00034567865,0.00132615,0.03026422,0.015110049,0.029063333,0.0043011704,0.63290447,0.0004203123],"about_ca_topic_score_codex":0.99097615,"about_ca_topic_score_gemma":0.9965138,"teacher_disagreement_score":0.06850527,"about_ca_system_score_codex":0.06850527,"about_ca_system_score_gemma":0.12633464,"threshold_uncertainty_score":0.4970429},"labels":[],"label_agreement":null},{"id":"W4365563283","doi":"10.1016/j.ymssp.2023.110346","title":"An AI-based monitoring system for external disturbance detection and classification near a buried pipeline","year":2023,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Support vector machine; Wavelet; Artificial intelligence; Accelerometer; Pattern recognition (psychology); Computer science; Engineering; Robustness (evolution)","score_opus":0.020974294517691523,"score_gpt":0.2406173345192113,"score_spread":0.21964304000151977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365563283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1686635,0.00067850057,0.8078797,0.0006393758,0.0008604669,0.00026334444,0.0006331436,0.009403008,0.010978934],"genre_scores_gemma":[0.86411357,0.00019274293,0.12704836,0.00050103874,0.00022558527,0.00019457596,0.00036806354,0.0000590976,0.0072969845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973494,0.000027566559,0.000017792301,0.00008467114,0.00011032883,0.000024751022],"domain_scores_gemma":[0.9995586,0.00006869151,0.00004512346,0.00003565314,0.00024353228,0.00004827491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029072713,0.0005366036,0.00047429334,0.00083963753,0.0005801956,0.0005753938,0.0009225774,0.0007875691,0.0024146743],"category_scores_gemma":[0.0006508717,0.00021543076,0.00018458135,0.0006201497,0.0002013717,0.00054806744,0.0004850888,0.0005621432,0.0010621768],"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":[0.0008916029,0.0004953529,0.0057499846,0.00029766772,0.000089659996,0.00038895383,0.0002271941,0.017870856,0.42914385,0.0011651318,0.008445404,0.53523433],"study_design_scores_gemma":[0.00008579895,0.0007348582,0.012362759,0.000026771088,0.00011485747,0.00034524951,0.000092123744,0.9157185,0.06236558,0.0010633708,0.0070224926,0.00006758719],"about_ca_topic_score_codex":0.002353215,"about_ca_topic_score_gemma":0.0035223572,"teacher_disagreement_score":0.0024146743,"about_ca_system_score_codex":0.0003148411,"about_ca_system_score_gemma":0.0004990877,"threshold_uncertainty_score":0.00807786},"labels":[],"label_agreement":null},{"id":"W4366198683","doi":"10.1186/s40068-023-00287-7","title":"Asset management analytics for urban water mains: a literature review","year":2023,"lang":"en","type":"review","venue":"ENVIRONMENTAL SYSTEMS RESEARCH","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Asset management; Mains electricity; Analytics; Risk analysis (engineering); Categorization; Identification (biology); Computer science; Asset (computer security); Reliability (semiconductor); Data science; Engineering; Business; Artificial intelligence; Computer security","score_opus":0.09589328446070594,"score_gpt":0.35242466219080815,"score_spread":0.2565313777301022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366198683","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":[0.0005043994,0.99657,0.0009105663,0.00038824236,0.00011764543,0.000014234697,0.00006913651,0.000014854065,0.0014109471],"genre_scores_gemma":[0.0041544517,0.9942503,0.0009242051,0.00013482835,0.00012559295,0.000013782203,0.00008992175,0.000004937058,0.00030204712],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9993304,0.00015602255,0.00010787297,0.00011897811,0.00024719653,0.00003944973],"domain_scores_gemma":[0.9950696,0.0036167437,0.00032208103,0.00008292956,0.0008459219,0.00006278602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014003883,0.00090792077,0.0010669109,0.0047659446,0.0003096912,0.001794511,0.000899389,0.0009861229,0.003692004],"category_scores_gemma":[0.0045915223,0.00043626077,0.0010229708,0.0058123246,0.00037892192,0.0018767918,0.000715588,0.00081226137,0.00086200057],"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":[0.00005076142,0.00006982891,0.0007779284,0.060397737,0.00021920433,0.00016412277,0.00023430155,0.0021713995,0.00069009134,0.009249722,0.017886942,0.9080879],"study_design_scores_gemma":[0.000017136737,0.00020822855,0.0042363526,0.08290926,0.00090148783,0.0010634006,0.00068604015,0.0025223906,0.001276578,0.0096275555,0.89646703,0.00008451489],"about_ca_topic_score_codex":0.0032053355,"about_ca_topic_score_gemma":0.0034478516,"teacher_disagreement_score":0.0047659446,"about_ca_system_score_codex":0.0006874917,"about_ca_system_score_gemma":0.0027240291,"threshold_uncertainty_score":0.0123509765},"labels":[],"label_agreement":null},{"id":"W4366385266","doi":"10.1016/j.oceaneng.2023.114167","title":"Interaction of wave motion and water hammer on the response of the risers in deep-sea mining","year":2023,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Water Systems and 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 Ottawa","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Fluid–structure interaction; Seabed; Economic shortage; Drilling riser; Engineering; Marine engineering; Deep water; Water hammer; Offshore geotechnical engineering; Stiffness; Geology; Geotechnical engineering; Structural engineering; Finite element method; Mechanical engineering","score_opus":0.011264767828567905,"score_gpt":0.17923103333736057,"score_spread":0.16796626550879265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366385266","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":[0.99448603,0.000048799637,0.0033219424,0.00017428586,0.000017171044,0.0000071015475,0.00002181868,0.000028116376,0.0018947126],"genre_scores_gemma":[0.9990689,0.000019614306,0.00012107365,0.000007742541,0.0000023723524,0.0000015393514,0.000007487129,0.0000055756263,0.000765796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998436,0.000053140793,0.0000046543814,0.000016019783,0.00002382013,0.000058691778],"domain_scores_gemma":[0.9993112,0.00048328107,0.00003718424,0.000018521268,0.000079122605,0.000070682094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003528878,0.0002512438,0.00052075076,0.00029045035,0.0003505121,0.00056423433,0.00028874047,0.00089795986,0.0019625898],"category_scores_gemma":[0.0017638366,0.00023982015,0.0002893495,0.0002178184,0.0005147495,0.00052215863,0.00047348157,0.0005920697,0.00021938598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010293763,0.00031352075,0.023282293,0.00010162554,0.000071709204,0.0011008475,0.0003206476,0.91811746,0.037582733,0.0021711562,0.0010772247,0.01483134],"study_design_scores_gemma":[0.000017171866,0.00011493709,0.01167167,0.000004813689,0.000010701668,0.00004973623,0.00033725734,0.9847936,0.0022968212,0.00054726633,0.0001390288,0.000017072472],"about_ca_topic_score_codex":0.0059199356,"about_ca_topic_score_gemma":0.004649753,"teacher_disagreement_score":0.0059199356,"about_ca_system_score_codex":0.00030473297,"about_ca_system_score_gemma":0.00028987025,"threshold_uncertainty_score":0.011770964},"labels":[],"label_agreement":null},{"id":"W4367145160","doi":"10.1121/10.0018930","title":"Maximum likelihood estimation for leak localization in water distribution networks using in-pipe acoustic sensing","year":2023,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Water Systems and 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":"Leak; Pipe network analysis; Leak detection; Computer science; Pipeline transport; Hydrophone; Acoustics; Pipeline (software); Wireless sensor network; Real-time computing; Noise (video); Environmental science; Artificial intelligence; Computer network; Physics","score_opus":0.009980180537311385,"score_gpt":0.22331233844386977,"score_spread":0.21333215790655838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367145160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011647326,0.00017403506,0.987402,0.00013971791,0.000011241903,0.000018819426,0.000046596746,0.00014547943,0.00041479533],"genre_scores_gemma":[0.7616606,0.00065846473,0.23289204,0.00013685212,0.00008647617,0.0002336456,0.00045357307,0.00010541661,0.0037729477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935323,0.00032856426,0.000027763472,0.00012777849,0.00009920243,0.0000634257],"domain_scores_gemma":[0.99677354,0.0025962393,0.0002587283,0.00009115226,0.00022319243,0.0000572639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015365887,0.0011976326,0.0011527939,0.00070054654,0.00032337822,0.0007483681,0.0010754593,0.0010992896,0.0012005961],"category_scores_gemma":[0.007332487,0.000847319,0.0006616325,0.0008757439,0.0010613337,0.0017715261,0.0010944695,0.001097858,0.0004151452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007415729,0.000028210163,0.0008692521,0.00006911745,0.000031765878,0.00005219767,0.00004616728,0.97751254,0.0012122348,0.0031870098,0.00040252708,0.01651471],"study_design_scores_gemma":[0.0000045920387,0.000011268455,0.00012684704,0.000004017489,0.000002462845,0.0000059620897,0.000005848501,0.9977946,0.00020836836,0.0017329985,0.00009860015,0.000004386767],"about_ca_topic_score_codex":0.004826196,"about_ca_topic_score_gemma":0.0034098772,"teacher_disagreement_score":0.004826196,"about_ca_system_score_codex":0.000769646,"about_ca_system_score_gemma":0.0009831069,"threshold_uncertainty_score":0.009596169},"labels":[],"label_agreement":null},{"id":"W4367154841","doi":"10.36487/acg_repo/2355_12","title":"Pressure instrument slack flow detection – three methods to determine flow status of a paste reticulation system","year":2023,"lang":"en","type":"article","venue":"Paste/˜Pœaste","topic":"Water Systems and 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":"Banff Centre; University of Alberta; Geomechanica (Canada)","funders":"","keywords":"Flow (mathematics); Computer science; Flow measurement; Mechanics; Physics","score_opus":0.020229851668691276,"score_gpt":0.24155032755925854,"score_spread":0.22132047589056725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367154841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26211664,0.0007443934,0.7235174,0.0001785472,0.00014009785,0.0007617806,0.0005535506,0.0050809383,0.006906649],"genre_scores_gemma":[0.6449564,0.00037673407,0.35009277,0.00007911278,0.000044451142,0.0004603533,0.00031300038,0.00015028653,0.0035269498],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981542,0.00018381812,0.00012488104,0.00035423995,0.0010765136,0.00010626409],"domain_scores_gemma":[0.9965957,0.0009627583,0.00072173454,0.00032159727,0.0012588571,0.00013932727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010684477,0.0010988546,0.00073967304,0.0029191233,0.00041038392,0.0010588191,0.0013096742,0.000708393,0.0014778564],"category_scores_gemma":[0.0038831555,0.0006544209,0.00052346074,0.0012916741,0.0005534864,0.0010539859,0.00095164625,0.00097177847,0.00065247127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008459074,0.0005546652,0.05167772,0.0006686632,0.00013752887,0.00018670868,0.0008428036,0.007955206,0.27101144,0.000884874,0.0015605019,0.663674],"study_design_scores_gemma":[0.0001646806,0.0022247261,0.10155083,0.0000995359,0.0002068915,0.0010046397,0.00051021244,0.26157144,0.6216076,0.0012859227,0.009402935,0.00037060597],"about_ca_topic_score_codex":0.0013473455,"about_ca_topic_score_gemma":0.0023823641,"teacher_disagreement_score":0.0029191233,"about_ca_system_score_codex":0.0005028493,"about_ca_system_score_gemma":0.00046713586,"threshold_uncertainty_score":0.0056505203},"labels":[],"label_agreement":null},{"id":"W4376278917","doi":"10.21203/rs.3.rs-2899695/v1","title":"Semi and Self-supervised Learning for Multi-label Classification for an Underwater Inspection Imagery Application","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Water Systems and 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":"Pontifícia Universidade Católica do Rio de Janeiro","keywords":"Underwater; Computer science; Task (project management); Machine learning; Artificial intelligence; Inference; Process (computing); Data mining; Engineering; Systems engineering","score_opus":0.16689234495703115,"score_gpt":0.382446967727246,"score_spread":0.21555462277021487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376278917","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":[0.12953584,0.00073596474,0.85849905,0.0008145606,0.00021801986,0.00020700214,0.0006053936,0.0072279726,0.002156191],"genre_scores_gemma":[0.6437823,0.0001834012,0.34669873,0.0004019125,0.00021834625,0.00026199454,0.0028003776,0.00031301632,0.0053398963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877805,0.00037025954,0.0000663335,0.00038680888,0.00026404485,0.00013444651],"domain_scores_gemma":[0.9966595,0.0016178574,0.00029032375,0.00048306445,0.0007826592,0.00016662848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021118785,0.0010385935,0.0009900139,0.0008725222,0.00060258433,0.0009751177,0.0023214214,0.0017575258,0.0020165883],"category_scores_gemma":[0.0042736097,0.00037917338,0.001154297,0.0007851667,0.000587293,0.001353793,0.0010341026,0.0021477942,0.0011404288],"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":[0.000710167,0.0010298545,0.0061066137,0.0002753355,0.00018790348,0.00020805288,0.00028462824,0.28901973,0.011802888,0.0020570266,0.0156414,0.6726764],"study_design_scores_gemma":[0.0000056560843,0.000024884705,0.00032421606,0.00000403712,0.0000052764663,0.000012498325,0.000017720946,0.9969969,0.0015199347,0.00072380586,0.00035973286,0.0000053902627],"about_ca_topic_score_codex":0.005714961,"about_ca_topic_score_gemma":0.009390407,"teacher_disagreement_score":0.005714961,"about_ca_system_score_codex":0.00089279935,"about_ca_system_score_gemma":0.0011468907,"threshold_uncertainty_score":0.011363387},"labels":[],"label_agreement":null},{"id":"W4377028224","doi":"10.1061/9780784484852.079","title":"Asset Management Perspective in Long-Term Rehabilitation of Aged Water Distribution Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"","keywords":"Asset management; Process (computing); Interdependence; Risk analysis (engineering); Asset (computer security); Rehabilitation; Computer science; Reliability (semiconductor); Upgrade; Flood myth; Business; Computer security","score_opus":0.004960345721180247,"score_gpt":0.20938570861338623,"score_spread":0.20442536289220598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377028224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19345859,0.002491374,0.7853724,0.000672048,0.0000642618,0.00008217469,0.00013129751,0.00008309356,0.0176448],"genre_scores_gemma":[0.95880085,0.00092972035,0.036655564,0.000048263966,0.000033226715,0.000046295027,0.00005565802,0.000024638883,0.0034057186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99967754,0.00016017268,0.00000959989,0.000046986606,0.000049775208,0.00005590013],"domain_scores_gemma":[0.9997583,0.00010124376,0.000047823025,0.00001537985,0.00005063784,0.000026591084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000684204,0.0006369887,0.00060634664,0.0004991902,0.00036831287,0.0011940673,0.00071729114,0.0007176908,0.0023402036],"category_scores_gemma":[0.0008822217,0.00021950755,0.00052241125,0.0005377085,0.00052164297,0.0011100364,0.0005503438,0.0005675729,0.00011371026],"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":[0.000023907169,0.000031457283,0.0007197407,0.000042946598,0.000023428,0.00007647069,0.000024564932,0.9792662,0.00072731695,0.008726785,0.00013409779,0.010203107],"study_design_scores_gemma":[0.0000050879253,0.00007917369,0.0011474306,0.000019428917,0.000030478042,0.000032082247,0.0000849008,0.9901273,0.0005433452,0.006973334,0.00094969524,0.00000786036],"about_ca_topic_score_codex":0.0067369095,"about_ca_topic_score_gemma":0.006278042,"teacher_disagreement_score":0.0067369095,"about_ca_system_score_codex":0.00092969055,"about_ca_system_score_gemma":0.0008963665,"threshold_uncertainty_score":0.013395369},"labels":[],"label_agreement":null},{"id":"W4377028399","doi":"10.1061/9780784484852.085","title":"Impact of Data Preparation on the Performance of Pipe Break Status Prediction Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Undersampling; Sampling (signal processing); Statistics; Logistic regression; Random forest; Decision tree; Computer science; Engineering; Environmental science; Data mining; Machine learning; Mathematics; Artificial intelligence","score_opus":0.04083835750851412,"score_gpt":0.2603414429169294,"score_spread":0.2195030854084153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377028399","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":[0.97203237,0.0010802984,0.022578226,0.0008984865,0.00019802242,0.00019486909,0.0011170692,0.0007698613,0.0011308837],"genre_scores_gemma":[0.98051673,0.00019306641,0.015534641,0.00017417462,0.000026128895,0.000104916544,0.0029599254,0.000065067296,0.00042530184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99081826,0.0043334914,0.0012278295,0.0017426678,0.0013387223,0.00053914683],"domain_scores_gemma":[0.9557941,0.03412716,0.0023014718,0.0034099144,0.0035976449,0.00076977175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020448377,0.0015775603,0.0011806525,0.0011083448,0.0009790694,0.0018749072,0.001168207,0.0014205505,0.0005398724],"category_scores_gemma":[0.051420633,0.0005865149,0.002025397,0.0012416205,0.0009508839,0.0024615242,0.001533468,0.002551587,0.0003163606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003909258,0.0012585118,0.20131989,0.0004535279,0.00095353945,0.00063321315,0.00072556955,0.64015394,0.008146125,0.00084555167,0.0027449396,0.1388559],"study_design_scores_gemma":[0.00018392869,0.0020478312,0.07211677,0.00018974785,0.00032707915,0.0002316069,0.0011999115,0.90073055,0.01815267,0.0016180277,0.0030709736,0.00013091408],"about_ca_topic_score_codex":0.017508974,"about_ca_topic_score_gemma":0.009913384,"teacher_disagreement_score":0.020448377,"about_ca_system_score_codex":0.001255039,"about_ca_system_score_gemma":0.0020474915,"threshold_uncertainty_score":0.108142614},"labels":[],"label_agreement":null},{"id":"W4378087062","doi":"10.5206/mase/15591","title":"A Conceptual model of mixed-integer linear programming water distribution system","year":2023,"lang":"en","type":"article","venue":"Mathematics in Applied Sciences and Engineering","topic":"Water Systems and 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":"Mathematical optimization; Reliability (semiconductor); Linear programming; Integer programming; Function (biology); Computer science; Integer (computer science); Population; Conceptual model; Water supply; Distribution (mathematics); Mathematics; Engineering; Environmental engineering","score_opus":0.02028244014523352,"score_gpt":0.20018276974681345,"score_spread":0.17990032960157992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378087062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010953688,0.00082675327,0.9321151,0.0016331613,0.00017332152,0.00018193013,0.0006612042,0.00026560627,0.05318913],"genre_scores_gemma":[0.74368876,0.0023424758,0.20602906,0.0004544721,0.00019804669,0.0014744146,0.0007840843,0.000100120444,0.044928495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99935395,0.00024768183,0.000027853022,0.00014212569,0.000153137,0.00007525941],"domain_scores_gemma":[0.99965584,0.00014492523,0.000058012705,0.000016206117,0.00009759344,0.000027386515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006203697,0.00093833194,0.0007526639,0.00054052885,0.0007073783,0.0027965927,0.002534101,0.0020537553,0.009169359],"category_scores_gemma":[0.000942642,0.0004545509,0.0007724687,0.0011471082,0.0009832742,0.0017546571,0.0010889838,0.0016776148,0.001039164],"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":[0.00003301972,0.000055699977,0.00025385144,0.00018181087,0.00002099706,0.00026299382,0.00013299804,0.7848638,0.0010973366,0.20317328,0.0017345166,0.008189644],"study_design_scores_gemma":[0.000023099961,0.000046389567,0.00007395005,0.000020132029,0.000011827934,0.000056872443,0.000051295418,0.9690018,0.00017873611,0.025043102,0.0054810555,0.000011737736],"about_ca_topic_score_codex":0.0065222736,"about_ca_topic_score_gemma":0.00491296,"teacher_disagreement_score":0.009169359,"about_ca_system_score_codex":0.0015714653,"about_ca_system_score_gemma":0.001683429,"threshold_uncertainty_score":0.030674577},"labels":[],"label_agreement":null},{"id":"W4382786633","doi":"10.1142/s2529732523400023","title":"Whole-Community Water Management: Inspiration for Water Utilities from Around the Globe","year":2023,"lang":"en","type":"article","venue":"Molecular Frontiers Journal","topic":"Water Systems and 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":"","keywords":"Globe; Reuse; Water infrastructure; Potable water; Environmental planning; Water resources; Business; Water scarcity; Water conservation; Water saving; Clean water; Water utility; Water supply; Environmental resource management; Natural resource economics; Water resource management; Environmental science; Engineering; Environmental engineering; Economics; Waste management","score_opus":0.012512625276691842,"score_gpt":0.19552368277606869,"score_spread":0.18301105749937685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382786633","genre_codex":"commentary","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":[0.060294464,0.046602502,0.11221565,0.4755036,0.0049591786,0.00032581092,0.0001834751,0.0006132362,0.2993022],"genre_scores_gemma":[0.73471236,0.04108649,0.10036642,0.04479617,0.0018958943,0.00029748853,0.00021550307,0.00054326386,0.07608641],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993569,0.0003061308,0.000014820264,0.0000750704,0.00012144882,0.00012576411],"domain_scores_gemma":[0.9989807,0.0002999036,0.00003865742,0.00009166374,0.00023553737,0.00035347746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017748137,0.00053920713,0.00029880533,0.0006469088,0.002660714,0.0032738817,0.0011339292,0.0025227114,0.007267971],"category_scores_gemma":[0.00282704,0.00014624653,0.00029907952,0.0015888361,0.002706444,0.005933164,0.0054148547,0.002181398,0.000919895],"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":[0.000057015248,0.00029474607,0.0024005608,0.00062784366,0.000040794977,0.00044434378,0.011039243,0.0030810263,0.0016239515,0.39284718,0.124677755,0.4628655],"study_design_scores_gemma":[0.000028355262,0.000085173386,0.0012361794,0.00057728146,0.000015846024,0.0001403087,0.018718347,0.0031073517,0.0005731349,0.24244402,0.733047,0.000027055798],"about_ca_topic_score_codex":0.004369485,"about_ca_topic_score_gemma":0.01434733,"teacher_disagreement_score":0.007267971,"about_ca_system_score_codex":0.0019266567,"about_ca_system_score_gemma":0.002938814,"threshold_uncertainty_score":0.024313807},"labels":[],"label_agreement":null},{"id":"W4384344829","doi":"10.1109/eic55835.2023.10177346","title":"Temperature Impact on On-load Tap Changers Vibro-Acoustic Signals","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Hydro-Québec","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Log-normal distribution; Probabilistic logic; Work (physics); Probability distribution; Acoustics; Maximum temperature; Statistics; Mathematics; Physics; Thermodynamics","score_opus":0.01088743104542422,"score_gpt":0.22418149418192307,"score_spread":0.21329406313649885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384344829","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":[0.9639515,0.00021500284,0.033082876,0.000055927885,0.000044624863,0.000040080784,0.0002824405,0.00032744178,0.0020001442],"genre_scores_gemma":[0.9983621,0.000054029406,0.0010950069,0.0000121627345,0.0000055173514,0.0000073891542,0.0000695609,0.00002737529,0.00036695454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992149,0.00010511081,0.000029327395,0.00011314166,0.00043915244,0.000098376455],"domain_scores_gemma":[0.99816704,0.0010762698,0.00017447762,0.00013456664,0.00041023936,0.000037334215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048021894,0.0004642033,0.00039303492,0.0007046037,0.00018414838,0.00043547194,0.00035713607,0.00041835,0.0018297622],"category_scores_gemma":[0.0028827444,0.00014634809,0.00027950684,0.00042222242,0.00046594357,0.000633881,0.00028877737,0.0003542529,0.00038038718],"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":[0.0026363235,0.00027620725,0.04601802,0.00064230646,0.0001231004,0.0013217411,0.0009222793,0.074539915,0.71746343,0.0005740897,0.00094484,0.1545378],"study_design_scores_gemma":[0.000026931293,0.0010494735,0.23333861,0.0000668168,0.00013544559,0.000627193,0.0008579279,0.3484299,0.41264048,0.0007545703,0.001955487,0.00011723639],"about_ca_topic_score_codex":0.0013004525,"about_ca_topic_score_gemma":0.0013768924,"teacher_disagreement_score":0.0018297622,"about_ca_system_score_codex":0.0002691415,"about_ca_system_score_gemma":0.00012789728,"threshold_uncertainty_score":0.006121218},"labels":[],"label_agreement":null},{"id":"W4384564592","doi":"10.1016/j.measurement.2023.113336","title":"Using adaptive neuro-fuzzy inference system and imperialist competitive algorithm for leak detection in pipe networks","year":2023,"lang":"en","type":"article","venue":"Measurement","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Adaptive neuro fuzzy inference system; Leakage (economics); Imperialist competitive algorithm; Computer science; Algorithm; Fuzzy logic; Nonlinear system; Inference system; Convergence (economics); Artificial intelligence; Pattern recognition (psychology); Data mining; Fuzzy control system; Particle swarm optimization","score_opus":0.05283481349668538,"score_gpt":0.23087953662794453,"score_spread":0.17804472313125913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384564592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07898057,0.0004568569,0.9180384,0.00014259251,0.00005164837,0.00003773008,0.000018402397,0.0002629277,0.0020109133],"genre_scores_gemma":[0.93549114,0.0001289143,0.06285924,0.00004116158,0.000026689982,0.000043385542,0.0000352539,0.00001844844,0.001355713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994338,0.00017451921,0.000036799487,0.00011603897,0.00017257535,0.0000662387],"domain_scores_gemma":[0.99892235,0.00061971776,0.000082911545,0.000039815477,0.00030741867,0.000027794464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012689299,0.00058452704,0.0010670135,0.0007066362,0.0006268627,0.0010023816,0.0010340224,0.0012933249,0.0006509934],"category_scores_gemma":[0.0033879182,0.00032381198,0.0005146705,0.00062149524,0.0005736409,0.0009930695,0.0006376736,0.000719997,0.0001192923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022832799,0.00011557876,0.0017177693,0.00011628724,0.00009925443,0.000087174056,0.000104861276,0.86835295,0.005142924,0.0033691311,0.00072820747,0.11993752],"study_design_scores_gemma":[0.0000024077194,0.0000114269205,0.00010273524,0.0000011285786,0.0000038917974,0.000005204291,0.0000027115752,0.99928695,0.00033719433,0.0002039875,0.000040342424,0.000002126897],"about_ca_topic_score_codex":0.013322327,"about_ca_topic_score_gemma":0.00821445,"teacher_disagreement_score":0.013322327,"about_ca_system_score_codex":0.00083362625,"about_ca_system_score_gemma":0.0009611309,"threshold_uncertainty_score":0.026489556},"labels":[],"label_agreement":null},{"id":"W4385436758","doi":"10.18280/ijsdp.180708","title":"Operational Efficiency Assessment of the Sewage Pumping Station Using the Performance Index under Real Conditions","year":2023,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Water Systems and 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":"Environmental science; Index (typography); Sewage; Environmental engineering; Computer science","score_opus":0.017000867694172882,"score_gpt":0.2737076098218082,"score_spread":0.25670674212763533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385436758","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":[0.98453426,0.0000767524,0.011879855,0.000044531545,0.0000113246515,0.00003976148,0.0003911725,0.00012654501,0.0028958488],"genre_scores_gemma":[0.9965449,0.00006213588,0.0024076696,0.000003880031,0.000002735971,0.000019628631,0.00027270804,0.0000096373615,0.0006766465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990908,0.0001448809,0.00009501294,0.00012213187,0.00046892618,0.00007824352],"domain_scores_gemma":[0.998982,0.0002774243,0.00024242973,0.000096193624,0.00036431453,0.000037567646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088836683,0.0004562918,0.00041240835,0.00133862,0.00030229567,0.001146528,0.000263905,0.00031533107,0.00091113936],"category_scores_gemma":[0.0018464846,0.00016353221,0.0003792611,0.0014830586,0.0002929762,0.0007850758,0.00045683756,0.00027621925,0.00031007023],"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":[0.00039304432,0.00049795414,0.6495331,0.00037372197,0.00026194108,0.0005776143,0.0012400557,0.11765304,0.06017452,0.0012566363,0.0016011657,0.16643718],"study_design_scores_gemma":[0.000017248234,0.0013467938,0.6285913,0.000058059828,0.00013185409,0.00033695722,0.0024913468,0.3122839,0.05000537,0.0010455769,0.0035916197,0.00010000412],"about_ca_topic_score_codex":0.00262536,"about_ca_topic_score_gemma":0.0035731788,"teacher_disagreement_score":0.00262536,"about_ca_system_score_codex":0.0005893281,"about_ca_system_score_gemma":0.0004802505,"threshold_uncertainty_score":0.005220115},"labels":[],"label_agreement":null},{"id":"W4385462868","doi":"10.32920/23811228.v1","title":"Thermal Analysis of Insulated Concrete Form (ICF) Walls","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Thermal; Inlet; Transient (computer programming); Materials science; Heat transfer; Heat transmission; Mechanics; Thermal energy storage; Structural engineering; Composite material; Thermodynamics; Mechanical engineering; Engineering; Physics; Computer science","score_opus":0.021991353689913767,"score_gpt":0.21652208846833024,"score_spread":0.19453073477841648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385462868","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":[0.93794817,0.00016286546,0.04963586,0.00006863313,0.00002093244,0.000025784986,0.00018898274,0.00011919707,0.0118296],"genre_scores_gemma":[0.9914159,0.00009153833,0.0054335333,0.000007846002,0.0000023278915,0.000015855074,0.00007465991,0.000019064775,0.0029392894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992204,0.000011984452,0.0000027736535,0.000016444743,0.000027980814,0.000018799159],"domain_scores_gemma":[0.9998766,0.000042316038,0.000026395957,0.00001568373,0.000027968179,0.000010994188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012626515,0.00031990642,0.00037747304,0.00018754278,0.00024714874,0.0004666514,0.00040053774,0.00047282968,0.0010361217],"category_scores_gemma":[0.0003512403,0.00021834529,0.00035404376,0.00019753222,0.0005398973,0.00029165353,0.00019645125,0.00027802115,0.00017803042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030794847,0.00001560677,0.0006468616,0.00003467959,0.000005616139,0.00010255497,0.000034953286,0.96649784,0.028794508,0.0014581326,0.00008560522,0.0022928542],"study_design_scores_gemma":[0.0000072125335,0.00004252772,0.0011556399,0.000008830149,0.0000048664874,0.000028677921,0.000024690693,0.9883167,0.009713541,0.00034727485,0.00034015285,0.000009886054],"about_ca_topic_score_codex":0.0046109115,"about_ca_topic_score_gemma":0.002697369,"teacher_disagreement_score":0.0046109115,"about_ca_system_score_codex":0.0005321649,"about_ca_system_score_gemma":0.0005225338,"threshold_uncertainty_score":0.009168148},"labels":[],"label_agreement":null},{"id":"W4385549909","doi":"10.1061/jwrmd5.wreng-5896","title":"Review of Critical Factors Affecting the Failure of Water Pipeline Infrastructure","year":2023,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Pipeline transport; Urbanization; Environmental science; Water supply; Pipeline (software); Water resource management; Population; Risk analysis (engineering); Environmental resource management; Natural resource economics; Business; Environmental engineering; Computer science; Economics","score_opus":0.010037095286628505,"score_gpt":0.230742377345581,"score_spread":0.2207052820589525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385549909","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":[0.00079433445,0.9980648,0.00022030679,0.0002478001,0.00010623724,0.00009440586,0.0002119096,0.000005200911,0.00025507336],"genre_scores_gemma":[0.00847797,0.99036694,0.0004713232,0.00022356732,0.000083518855,0.00010776977,0.000182972,0.000003701879,0.00008220159],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951834,0.0015645303,0.0018989113,0.00039549466,0.0008364489,0.00012122582],"domain_scores_gemma":[0.97050416,0.02084547,0.0052208775,0.00040349373,0.002743205,0.00028279866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008089206,0.0016635362,0.004989556,0.011250516,0.0005682007,0.0018943064,0.0017945973,0.0014458183,0.0032235933],"category_scores_gemma":[0.030065553,0.000880686,0.008223978,0.009332447,0.00072611443,0.001453966,0.0009843713,0.0007806416,0.0003115967],"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":[0.00025359655,0.000039572667,0.0016600812,0.8577733,0.02041478,0.00021317045,0.00018694116,0.00042528176,0.0003592215,0.0005662577,0.0037616375,0.11434615],"study_design_scores_gemma":[0.0002112025,0.00061923283,0.014750287,0.6982564,0.19477919,0.0012053377,0.0003728404,0.00036862725,0.0007341743,0.0014619292,0.08714108,0.000099610595],"about_ca_topic_score_codex":0.009229702,"about_ca_topic_score_gemma":0.019822614,"teacher_disagreement_score":0.011250516,"about_ca_system_score_codex":0.00251797,"about_ca_system_score_gemma":0.010246276,"threshold_uncertainty_score":0.04278028},"labels":[],"label_agreement":null},{"id":"W4385720516","doi":"10.1061/9780784485033.015","title":"Prediction of Sewer Pipelines Using Machine Learning Techniques: A Case Study on the City of Hamilton Sewer Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Blackberry (Canada); Burnaby Hospital","funders":"","keywords":"Pipeline transport; Computer science; Sanitary sewer; Marine engineering; Civil engineering; Environmental science; Engineering; Environmental engineering","score_opus":0.05491992794702852,"score_gpt":0.243782075510735,"score_spread":0.18886214756370648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385720516","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":[0.99503446,0.000054179567,0.0035088563,0.00023598787,0.0000092566725,0.00005896575,0.00038920177,0.0000987321,0.00061038515],"genre_scores_gemma":[0.9943751,0.000059999173,0.004016263,0.000019531186,0.0000041528615,0.000025083777,0.0006158087,0.000008659038,0.00087544054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954695,0.000117278745,0.00002970125,0.0001100242,0.00010608618,0.000090012625],"domain_scores_gemma":[0.99859804,0.0007674901,0.00011906555,0.000080174264,0.00034194163,0.000093317576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008416661,0.00081927644,0.00049816107,0.00092703337,0.0007444541,0.0008627651,0.0012071196,0.0010423547,0.0006957103],"category_scores_gemma":[0.0018274046,0.00036137586,0.000608703,0.0013350785,0.0005732223,0.0006238763,0.0005635937,0.00086189463,0.0001545326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002264169,0.00075268524,0.11079899,0.000119863485,0.000092331764,0.0027798647,0.00038153073,0.8511585,0.0023582634,0.0006226231,0.00260353,0.028105415],"study_design_scores_gemma":[0.00001295182,0.0000694314,0.023080267,0.000008160691,0.000012904623,0.000041063526,0.0004997557,0.9746875,0.0010498668,0.00016290438,0.00036248338,0.000012703272],"about_ca_topic_score_codex":0.37522796,"about_ca_topic_score_gemma":0.44429567,"teacher_disagreement_score":0.6247721,"about_ca_system_score_codex":0.0036362042,"about_ca_system_score_gemma":0.0022205452,"threshold_uncertainty_score":0.7460878},"labels":[],"label_agreement":null},{"id":"W4385720540","doi":"10.1061/9780784485019.033","title":"What Is a Safe Filling Velocity in Water and Wastewater Pipelines?","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"WSP (Canada); Centre for Global Health Research; Thornhill Medical (Canada)","funders":"","keywords":"Pipeline transport; Transient (computer programming); Overpressure; Pipeline (software); Mechanics; Transient analysis; Wastewater; Transient flow; Environmental science; Petroleum engineering; Water hammer; Materials science; Transient response; Geology; Engineering; Environmental engineering; Mechanical engineering; Surge; Computer science; Physics; Electrical engineering; Thermodynamics","score_opus":0.012142131946578288,"score_gpt":0.20113649077983173,"score_spread":0.18899435883325344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385720540","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":[0.5855732,0.002560762,0.38937864,0.00865326,0.00045498784,0.00011903261,0.00026118648,0.0006962858,0.01230267],"genre_scores_gemma":[0.98623866,0.00056117267,0.012336015,0.00010971972,0.000032030784,0.000021518626,0.00002377601,0.000037051937,0.0006400762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995454,0.00012630556,0.00002947049,0.000063463616,0.00012542224,0.00010985271],"domain_scores_gemma":[0.9983558,0.0006981357,0.0004899684,0.00009094345,0.00021803263,0.00014704085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00111388,0.00039112923,0.0006094664,0.00050194806,0.00064344244,0.0016641541,0.00080780184,0.0020850385,0.0012572943],"category_scores_gemma":[0.008804373,0.00043339736,0.00042828868,0.00034939914,0.0016621731,0.0030860354,0.00078548555,0.0008171717,0.00029258514],"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":[0.0007016625,0.0003297193,0.021203585,0.00064071675,0.000045829034,0.00079447613,0.00056892907,0.7735544,0.03290769,0.08185917,0.0030535448,0.08434036],"study_design_scores_gemma":[0.000073926014,0.0004832623,0.0052740215,0.00031539224,0.00003451851,0.00022844618,0.0017271846,0.90041834,0.01846211,0.0676984,0.005169387,0.000115020564],"about_ca_topic_score_codex":0.0048585264,"about_ca_topic_score_gemma":0.0031413126,"teacher_disagreement_score":0.0048585264,"about_ca_system_score_codex":0.000707225,"about_ca_system_score_gemma":0.0019971812,"threshold_uncertainty_score":0.009660482},"labels":[],"label_agreement":null},{"id":"W4385720545","doi":"10.1061/9780784485019.005","title":"Air in Water Distribution Systems—Challenging the Status Quo","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Hatch (Canada)","funders":"","keywords":"Status quo; Distribution (mathematics); Environmental science; Computer science; Water resource management; Political science; Mathematics","score_opus":0.008594273540951587,"score_gpt":0.18113059709389295,"score_spread":0.17253632355294138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385720545","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":[0.06376572,0.16596487,0.22206129,0.22694723,0.009148218,0.00014000374,0.0004716138,0.0005041163,0.31099695],"genre_scores_gemma":[0.82964987,0.068417445,0.040176667,0.0069838036,0.00447009,0.00010168396,0.00013199933,0.0002172162,0.04985122],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976525,0.0009067179,0.00009226177,0.00035708124,0.0007891744,0.00020221244],"domain_scores_gemma":[0.997053,0.0012819804,0.0002405214,0.00027201284,0.0009834423,0.00016893029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003989655,0.00041097472,0.0005042088,0.00051235384,0.001343724,0.0057592993,0.001312347,0.002534148,0.0059663006],"category_scores_gemma":[0.005244906,0.00025698708,0.00025355638,0.0006915132,0.0035885982,0.01081947,0.0019252708,0.002225457,0.0012004394],"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":[0.000074845135,0.00006056573,0.001029288,0.0009849656,0.000022868964,0.00012262438,0.00026131168,0.020632342,0.0021403243,0.67972684,0.027933775,0.2670104],"study_design_scores_gemma":[0.000021067728,0.00022675877,0.0010041615,0.0014028226,0.000026679794,0.00015404508,0.0015440932,0.04260247,0.0031205467,0.47629517,0.47354865,0.000053558495],"about_ca_topic_score_codex":0.0066790567,"about_ca_topic_score_gemma":0.008303506,"teacher_disagreement_score":0.0066790567,"about_ca_system_score_codex":0.0029630768,"about_ca_system_score_gemma":0.0028537754,"threshold_uncertainty_score":0.02149874},"labels":[],"label_agreement":null},{"id":"W4385724280","doi":"10.1061/9780784485019.056","title":"Review of Trenchless Installations at Willamette Water Supply Program","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Stantec (Canada)","funders":"","keywords":"Trenchless technology; Water supply; Computer science; Construction engineering; Engineering; Pipeline transport; Environmental engineering","score_opus":0.01133945352697267,"score_gpt":0.22407795296671712,"score_spread":0.21273849943974446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385724280","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":[0.028228438,0.8198091,0.0075502153,0.005366035,0.0039740144,0.0005634134,0.0023977736,0.00048163615,0.13162938],"genre_scores_gemma":[0.05182081,0.8848498,0.00804222,0.0016587434,0.000921185,0.00024228134,0.003341769,0.0002704071,0.04885276],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965641,0.0005317286,0.0003790133,0.0002922543,0.0021002677,0.00013268896],"domain_scores_gemma":[0.9901776,0.0018695333,0.0012033404,0.0002809997,0.006173707,0.000294898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002883457,0.00055066333,0.00046960905,0.005728174,0.0008851692,0.001587667,0.0016912507,0.0006244917,0.012773515],"category_scores_gemma":[0.0073687476,0.00065043545,0.00042232568,0.009346256,0.000537728,0.0014117245,0.0006557404,0.000763921,0.0025313136],"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":[0.000051832452,0.00007705209,0.0033290728,0.010646842,0.00005115316,0.0005270923,0.0007225308,0.0011099423,0.0014698592,0.0019824202,0.10570093,0.87433136],"study_design_scores_gemma":[0.0000032856506,0.00008156782,0.0134037165,0.0040570535,0.00003744054,0.00044228657,0.00052792655,0.00010345081,0.0007649034,0.0001074615,0.9804528,0.000018085148],"about_ca_topic_score_codex":0.035287723,"about_ca_topic_score_gemma":0.092912525,"teacher_disagreement_score":0.035287723,"about_ca_system_score_codex":0.0021248893,"about_ca_system_score_gemma":0.005697103,"threshold_uncertainty_score":0.07016468},"labels":[],"label_agreement":null},{"id":"W4385859930","doi":"10.1007/978-3-031-34593-7_46","title":"Real-Time Water Distribution System Calibration Using Genetic Algorithm","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Calibration; Nonlinear system; Genetic algorithm; Interval (graph theory); Mathematical optimization; Function (biology); Conservation of mass; Boundary (topology); Set (abstract data type); Algorithm; Computer science; Flow (mathematics); Applied mathematics; Control theory (sociology); Mathematics; Mechanics; Mathematical analysis; Statistics","score_opus":0.007344425698064566,"score_gpt":0.17341976217729388,"score_spread":0.1660753364792293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385859930","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":[0.04656225,0.00018952093,0.94536316,0.000109568056,0.000044886092,0.000057350753,0.000059037786,0.0027376607,0.004876602],"genre_scores_gemma":[0.77985805,0.00008330364,0.21713464,0.000056555164,0.000012621666,0.000080901904,0.00011209259,0.00021742299,0.0024444347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997143,0.000087883775,0.000009545741,0.0000853333,0.00007159121,0.000031317344],"domain_scores_gemma":[0.9994678,0.00026748056,0.00006243876,0.000053715943,0.00013382813,0.000014772321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006599976,0.00087050966,0.00080568163,0.0006955214,0.0005466202,0.00084256724,0.0008414095,0.0010660839,0.0026212789],"category_scores_gemma":[0.0017488245,0.0005069064,0.0005116101,0.0011238519,0.0004082227,0.0008952419,0.0005842012,0.0009636126,0.00056111417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000662737,0.000031429892,0.00039840443,0.00002851492,0.000025352094,0.00002353512,0.0000290905,0.92651564,0.0038312022,0.0006550153,0.0004428356,0.06795272],"study_design_scores_gemma":[0.000008608684,0.000010391395,0.00017859813,0.0000028699126,0.0000046160203,0.0000065262907,0.0000042397733,0.998317,0.000893342,0.00039282185,0.00017685929,0.000004121382],"about_ca_topic_score_codex":0.011794704,"about_ca_topic_score_gemma":0.008394989,"teacher_disagreement_score":0.011794704,"about_ca_system_score_codex":0.0009903744,"about_ca_system_score_gemma":0.0010470154,"threshold_uncertainty_score":0.023452103},"labels":[],"label_agreement":null},{"id":"W4385873659","doi":"10.55274/r0012054","title":"DTPH56-13-T-000003 INO Technologies Assessment of Leak Detection","year":2015,"lang":"en","type":"report","venue":"","topic":"Water Systems and 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":"TransCanada (Canada)","funders":"","keywords":"Leak detection; Leak; Pipeline transport; Pipeline (software); Computer science; Engineering; Computer security; Operating system; Mechanical engineering","score_opus":0.030662807859953078,"score_gpt":0.26813356414412765,"score_spread":0.23747075628417458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385873659","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":[0.012888106,0.0002248464,0.011953518,0.0007832598,0.00033374777,0.0013019573,0.019596795,0.0038242207,0.9490936],"genre_scores_gemma":[0.081950255,0.00045718602,0.03006059,0.00045030535,0.000078039244,0.0009342007,0.04121664,0.0015862605,0.8432666],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99741685,0.0003038932,0.00007794574,0.00019851913,0.0017242314,0.00027852814],"domain_scores_gemma":[0.9959245,0.00045830783,0.00013179392,0.0005211832,0.002676371,0.00028785228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001649359,0.00065081933,0.00053968583,0.0021668824,0.0014470539,0.002418393,0.0013023515,0.0019550743,0.18295284],"category_scores_gemma":[0.0036877953,0.00044538543,0.00048767834,0.0012911409,0.0005547492,0.0011260288,0.0014647187,0.001046611,0.117056504],"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":[0.0008898951,0.001937916,0.017594835,0.00048534054,0.00007894373,0.000598151,0.0002810151,0.007386539,0.042095177,0.019528287,0.5928825,0.31624135],"study_design_scores_gemma":[0.00012374255,0.00086126,0.010875813,0.00014475803,0.00002623789,0.00048540634,0.00025643012,0.008388855,0.0408922,0.0015001,0.9364002,0.00004510524],"about_ca_topic_score_codex":0.028686006,"about_ca_topic_score_gemma":0.03888247,"teacher_disagreement_score":0.18295284,"about_ca_system_score_codex":0.0031112274,"about_ca_system_score_gemma":0.006350712,"threshold_uncertainty_score":0.6120382},"labels":[],"label_agreement":null},{"id":"W4385884183","doi":"10.1039/d3ew00159h","title":"Examining the conditioning factors that influence material shear strength of particle deposits in a full-scale drinking water distribution laboratory","year":2023,"lang":"en","type":"article","venue":"Environmental Science Water Research & Technology","topic":"Water Systems and 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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Foundation for Innovation","keywords":"Materials science; Shear strength (soil); Full scale; Surface finish; Geotechnical engineering; Particulates; Conditioning; Shear (geology); Composite material; Particle (ecology); Geology; Engineering; Structural engineering; Chemistry; Soil science","score_opus":0.01657905682110589,"score_gpt":0.23912707485887832,"score_spread":0.22254801803777244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385884183","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":[0.9989459,0.000008985471,0.0008826428,0.0000061049273,0.0000016249488,0.00002012626,0.00002650934,0.000015958016,0.00009206528],"genre_scores_gemma":[0.9977316,0.00002614156,0.0018154641,0.00000649044,0.0000020838163,0.000022698929,0.000052958196,0.000007674098,0.00033479344],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997148,0.0000530664,0.000016107748,0.00007210033,0.00009412484,0.000049823462],"domain_scores_gemma":[0.9993749,0.00024055468,0.00007112213,0.000054387627,0.00018968398,0.00006937475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069079985,0.00031749698,0.00031129946,0.00018044269,0.0004065512,0.00029464375,0.0003291712,0.0002268006,0.0011883848],"category_scores_gemma":[0.0011030708,0.000212863,0.00021177004,0.00017683565,0.0003930621,0.00029792637,0.00033582025,0.00031412634,0.00019984953],"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":[0.000456169,0.00030518867,0.00514384,0.000031705757,0.000009823624,0.000027461061,0.00008954348,0.0011389847,0.98896945,0.000028925988,0.00003587922,0.0037631344],"study_design_scores_gemma":[0.000054671338,0.004363013,0.03569086,0.000004341138,0.00004148314,0.000036476664,0.00015857695,0.0075038653,0.9517219,0.000048234953,0.00036052644,0.000016012375],"about_ca_topic_score_codex":0.0034812286,"about_ca_topic_score_gemma":0.0040600314,"teacher_disagreement_score":0.0034812286,"about_ca_system_score_codex":0.00041102487,"about_ca_system_score_gemma":0.00050633925,"threshold_uncertainty_score":0.006921947},"labels":[],"label_agreement":null},{"id":"W4386170192","doi":"10.1016/j.applthermaleng.2023.121428","title":"Thermodynamic behaviour and heat transfer characteristics of air vessels in water delivery systems","year":2023,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Water Systems and 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 Alberta","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Polytropic process; Mechanics; Water hammer; Turbulence; Heat transfer; Airflow; Thermodynamics; Materials science; Physics","score_opus":0.004220766600762812,"score_gpt":0.1508105268714822,"score_spread":0.14658976027071938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386170192","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":[0.99794716,0.00017254376,0.0008123367,0.000017910537,0.000003713481,0.0000047136373,0.00007490311,0.000017073999,0.0009497214],"genre_scores_gemma":[0.9993407,0.000039646333,0.00009614835,0.0000021560818,0.0000020551943,0.0000025885618,0.00004733619,0.0000050985636,0.00046440357],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998702,0.000029850502,0.0000069239186,0.000023594603,0.000038418868,0.00003102739],"domain_scores_gemma":[0.9997726,0.000117741096,0.000023730783,0.000009938135,0.000059616752,0.000016447648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025514554,0.000099269855,0.00022947717,0.0003120789,0.00032283328,0.00038957695,0.00020382344,0.00020592002,0.001982897],"category_scores_gemma":[0.0005142598,0.00011215974,0.00017552369,0.00029011714,0.00032812005,0.0003882493,0.00013313026,0.0002316091,0.00031617182],"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":[0.0025611888,0.00025632302,0.016666109,0.00014802166,0.00003593909,0.00020771692,0.00034463443,0.033570677,0.9241925,0.001683915,0.00055067096,0.0197824],"study_design_scores_gemma":[0.000026327178,0.001113799,0.048460174,0.000010993677,0.000030928597,0.00011345592,0.00026532414,0.12732053,0.8205983,0.0004364334,0.0015911981,0.000032575892],"about_ca_topic_score_codex":0.002426525,"about_ca_topic_score_gemma":0.0016357399,"teacher_disagreement_score":0.002426525,"about_ca_system_score_codex":0.00036771153,"about_ca_system_score_gemma":0.00018885579,"threshold_uncertainty_score":0.0066334605},"labels":[],"label_agreement":null},{"id":"W4386325141","doi":"10.2166/ws.2023.227","title":"How different regional approaches to the network design result in key differences in burst event severity and failure vulnerability","year":2023,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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":"Intrusion; Flooding (psychology); Environmental science; Flexibility (engineering); Vulnerability (computing); Network planning and design; Key (lock); Pipe network analysis; Computer science; Computer security; Computer network; Geology","score_opus":0.04499359231412089,"score_gpt":0.20025122473556223,"score_spread":0.15525763242144133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386325141","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":[0.8363047,0.00026585383,0.13766961,0.00047179637,0.000045014334,0.00014516969,0.00040704448,0.00023909341,0.02445168],"genre_scores_gemma":[0.98263896,0.00014271794,0.0150479,0.000023919307,0.000004792505,0.000058862788,0.0001262649,0.00008194461,0.0018745319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989349,0.0005522977,0.000043833596,0.00017629059,0.00014461396,0.00014810002],"domain_scores_gemma":[0.99846303,0.0005510971,0.0002422682,0.00020031218,0.00043851996,0.00010484038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017973328,0.00036788717,0.00028588268,0.0006487024,0.00039898828,0.0016957151,0.00083819544,0.00041303312,0.0035018225],"category_scores_gemma":[0.0048653595,0.00021809916,0.00046382227,0.0005720911,0.0006526772,0.0010229085,0.00093736197,0.00043104682,0.0006468105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005502935,0.00014733298,0.0481154,0.0001981066,0.00019210436,0.0004067098,0.0008458523,0.824617,0.034814317,0.030772181,0.0017167956,0.05762395],"study_design_scores_gemma":[0.00013174595,0.00089217635,0.09252337,0.00010654846,0.00044993844,0.00045144485,0.0051184567,0.83217,0.025038393,0.02492368,0.018006762,0.00018753344],"about_ca_topic_score_codex":0.009869128,"about_ca_topic_score_gemma":0.016297333,"teacher_disagreement_score":0.009869128,"about_ca_system_score_codex":0.0015082663,"about_ca_system_score_gemma":0.0007713101,"threshold_uncertainty_score":0.01962334},"labels":[],"label_agreement":null},{"id":"W4386414625","doi":"10.15402/esj.v9i1.70844","title":"Filtering","year":2023,"lang":"en","type":"article","venue":"Engaged Scholar Journal Community-Engaged Research Teaching and Learning","topic":"Water Systems and 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":"Computer science","score_opus":0.12104152107486615,"score_gpt":0.3388640153081702,"score_spread":0.21782249423330402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386414625","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":[0.015585038,0.0013774901,0.41227126,0.002111541,0.0035529223,0.001358249,0.23630293,0.1584793,0.16896123],"genre_scores_gemma":[0.075568184,0.0008496949,0.2712764,0.0018123772,0.0011884704,0.0011137082,0.31530538,0.02340376,0.309482],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972222,0.0003296551,0.0002329373,0.0009255295,0.00093367056,0.00035609864],"domain_scores_gemma":[0.99352187,0.0013395095,0.0001406327,0.0029317935,0.001920789,0.00014543987],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019416774,0.0017404361,0.001551169,0.0041539175,0.001354272,0.0049114544,0.0015914866,0.001960869,0.2969194],"category_scores_gemma":[0.01562834,0.0006179638,0.0028907037,0.003349193,0.0005203665,0.002836991,0.0020380672,0.0015816151,0.2671877],"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":[0.00069112284,0.00018355936,0.003908024,0.00052398973,0.00017518348,0.00013531398,0.00011317234,0.002343958,0.0038756297,0.01588426,0.58781904,0.38434678],"study_design_scores_gemma":[0.0002553839,0.00018170774,0.0091114575,0.00035855322,0.00018696066,0.0005741334,0.00030060642,0.057252306,0.024950208,0.05680072,0.8498542,0.00017375305],"about_ca_topic_score_codex":0.0077421875,"about_ca_topic_score_gemma":0.010348125,"teacher_disagreement_score":0.7030806,"about_ca_system_score_codex":0.0009192046,"about_ca_system_score_gemma":0.0017968273,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4386490939","doi":"10.1061/jpsea2.pseng-1494","title":"Maximum Likelihood Estimation to Localize Leaks in Water Distribution Networks","year":2023,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and 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; Canadian Water Network","funders":"","keywords":"Multilateration; Leak; Pipe network analysis; Acoustics; Hydrophone; Computer science; Algorithm; Accelerometer; Conditional probability; Leak detection; Simulation; Engineering; Statistics; Mathematics; Mechanics; Physics","score_opus":0.007376630732503296,"score_gpt":0.22016973236746054,"score_spread":0.21279310163495724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386490939","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":[0.009283433,0.00030583903,0.9894394,0.00008341207,0.000012505316,0.000022003196,0.000047741687,0.00044449503,0.00036116294],"genre_scores_gemma":[0.6043583,0.0008413708,0.39069128,0.00011652342,0.00010273805,0.00022926592,0.00056902174,0.00021185682,0.002879529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911314,0.00038869522,0.000044272034,0.00018299306,0.00018726026,0.000083627856],"domain_scores_gemma":[0.9966158,0.002458035,0.00038180835,0.00012509161,0.00034753472,0.00007172905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016061325,0.0013617288,0.0013253556,0.0013603456,0.00042189774,0.0010546789,0.0012026506,0.0011413107,0.0011325695],"category_scores_gemma":[0.009748847,0.0009120326,0.0007417215,0.0015330709,0.00084992894,0.0018799586,0.0013419951,0.0013130358,0.0005573712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009570039,0.000028069104,0.0011421321,0.00009491807,0.000034759192,0.00008986088,0.000071454946,0.94693774,0.001927359,0.002398126,0.000773008,0.046406873],"study_design_scores_gemma":[0.0000062859353,0.000009062865,0.00016481959,0.0000054133766,0.0000032357257,0.000013368548,0.000010638411,0.9969957,0.00036057975,0.0022366764,0.00018747387,0.0000067286574],"about_ca_topic_score_codex":0.0047466657,"about_ca_topic_score_gemma":0.0028495004,"teacher_disagreement_score":0.0047466657,"about_ca_system_score_codex":0.00088890275,"about_ca_system_score_gemma":0.0011317574,"threshold_uncertainty_score":0.0094380975},"labels":[],"label_agreement":null},{"id":"W4386542211","doi":"10.2139/ssrn.4565937","title":"Understanding Fog and Dew Dynamics for Assessing  Non-Rainfall Water Potential Uses in the Atacama","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Water Systems and 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 Saskatchewan","funders":"","keywords":"Dew; Environmental science; Meteorology; Climatology; Hydrology (agriculture); Atmospheric sciences; Geography; Geology","score_opus":0.04088822797661067,"score_gpt":0.24658164611461436,"score_spread":0.20569341813800368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386542211","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":[0.982065,0.00032909156,0.011789763,0.00022966144,0.000021528174,0.000034170713,0.000977318,0.00015020026,0.0044031544],"genre_scores_gemma":[0.9956505,0.00011975615,0.0032117954,0.000023551154,0.000008280955,0.000011058403,0.00032793646,0.000020019233,0.0006271803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99993443,0.000007781983,0.000003749083,0.000022198848,0.000013039878,0.00001893477],"domain_scores_gemma":[0.99990165,0.00004026266,0.000013224194,0.0000073893916,0.000023655975,0.000013813839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017917277,0.0004550894,0.00044029934,0.00066427764,0.00033449824,0.0010236616,0.00048773063,0.00078438787,0.0011554274],"category_scores_gemma":[0.00060691417,0.0003132023,0.0003273458,0.0008005919,0.00026045647,0.0009857424,0.00041352757,0.00038966868,0.00014330796],"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":[0.00046100328,0.00029727758,0.27554476,0.0003474745,0.0003315609,0.0008123514,0.0006668841,0.5123535,0.06042148,0.0026698464,0.002606387,0.14348754],"study_design_scores_gemma":[0.00001950923,0.000025060765,0.16114055,0.000015691454,0.000038535854,0.000028799,0.0002698385,0.83369094,0.0027174568,0.0012190451,0.000812099,0.000022455886],"about_ca_topic_score_codex":0.08788097,"about_ca_topic_score_gemma":0.07757117,"teacher_disagreement_score":0.08788097,"about_ca_system_score_codex":0.00073898287,"about_ca_system_score_gemma":0.00073595985,"threshold_uncertainty_score":0.17473888},"labels":[],"label_agreement":null},{"id":"W4386649013","doi":"10.1139/cjce-2023-0176","title":"Efficiency enhancement of leakage detection and localization methods using leakage gradient and most affected sensors","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Leakage (economics); Leak detection; Leak; Calibration; Computer science; Engineering; Mathematics; Statistics; Environmental engineering","score_opus":0.009851027101202853,"score_gpt":0.2105650087431616,"score_spread":0.20071398164195875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386649013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15604305,0.00030438192,0.8411408,0.000079179124,0.000018591063,0.000037587368,0.000024964716,0.0007486102,0.0016028072],"genre_scores_gemma":[0.89785,0.00012853906,0.10128315,0.0000284352,0.000008028728,0.00002659882,0.000034971396,0.0000311367,0.0006090723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995747,0.0001038225,0.000022699347,0.000081303595,0.00016923645,0.000048287286],"domain_scores_gemma":[0.99933237,0.0002712022,0.00009507508,0.00007517924,0.00020567694,0.000020507887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071415765,0.00065053743,0.0006307205,0.00077415654,0.0002277695,0.0004424156,0.00058678974,0.00039644743,0.000526309],"category_scores_gemma":[0.0016242989,0.00025454708,0.00038108404,0.00047040606,0.00032817447,0.00095418567,0.0005442921,0.00029166188,0.00015030462],"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":[0.00048209488,0.0002959984,0.008206644,0.00030032915,0.00008760488,0.00013694806,0.0002478929,0.39789903,0.13211615,0.0027959298,0.0009501479,0.4564812],"study_design_scores_gemma":[0.00001779404,0.00012029795,0.002190357,0.00000946788,0.000026553778,0.00006920487,0.000039382063,0.9594412,0.0369385,0.0004528652,0.00067671435,0.00001778111],"about_ca_topic_score_codex":0.0018770107,"about_ca_topic_score_gemma":0.001944085,"teacher_disagreement_score":0.0018770107,"about_ca_system_score_codex":0.00045111557,"about_ca_system_score_gemma":0.00048451967,"threshold_uncertainty_score":0.0037768483},"labels":[],"label_agreement":null},{"id":"W4386746341","doi":"10.1201/9781003323020-217","title":"Optimization of sewer flushing programs: A deep reinforcement learning approach","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Water Systems and 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":"Flushing; Reinforcement; Reinforcement learning; Computer science; Environmental science; Engineering; Artificial intelligence; Psychology; Structural engineering","score_opus":0.01811440243744353,"score_gpt":0.18470321561511413,"score_spread":0.1665888131776706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386746341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06370623,0.0016267052,0.9228341,0.0007785287,0.000088255074,0.000049787857,0.00012254195,0.0005400523,0.010253852],"genre_scores_gemma":[0.87507373,0.0006459613,0.115676336,0.00031126145,0.000067698136,0.00012970297,0.00022861022,0.000090931484,0.007775766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998287,0.00005520598,0.000006983533,0.00003699461,0.000033773103,0.000038312195],"domain_scores_gemma":[0.9993358,0.00046653533,0.000053239783,0.000018368757,0.00009471811,0.000031311247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062621804,0.0006941411,0.00083534734,0.00029239414,0.00019521869,0.00064295816,0.0008733211,0.0010143892,0.0023873567],"category_scores_gemma":[0.0014243773,0.00039369136,0.00045482613,0.00037515326,0.0004124571,0.0005814357,0.0006238471,0.001211063,0.00023262273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013991039,0.00002281211,0.00022923257,0.00001995336,0.000014029923,0.000016012133,0.0000088370325,0.98008245,0.00027138574,0.0013812791,0.00045391035,0.017486107],"study_design_scores_gemma":[0.000001344582,0.0000046089694,0.000026996788,0.0000021514143,0.0000013564787,0.0000013260182,0.0000011957591,0.9993012,0.000042584415,0.00053468527,0.00008187901,7.2616086e-7],"about_ca_topic_score_codex":0.009638735,"about_ca_topic_score_gemma":0.0091224555,"teacher_disagreement_score":0.009638735,"about_ca_system_score_codex":0.0010074961,"about_ca_system_score_gemma":0.0011550467,"threshold_uncertainty_score":0.019165218},"labels":[],"label_agreement":null},{"id":"W4387258465","doi":"10.26226/m.634060663701db4fe35304b0","title":"Improving Electricity Conservation in Small-Medium Municipal Water Distribution Systems in Ontario, Canada","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Electricity; Distribution (mathematics); Water conservation; Electric power distribution; Environmental science; Electricity system; Mains electricity; Business; Environmental protection; Environmental planning; Water resource management; Geography; Electricity generation; Water resources; Engineering; Ecology; Mathematics; Electrical engineering; Power (physics); Physics","score_opus":0.011678031846531323,"score_gpt":0.1704399519353805,"score_spread":0.15876192008884918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387258465","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":[0.9819803,0.00030472403,0.002264779,0.000886589,0.000017957153,0.000096443895,0.0011853604,0.00007896612,0.013184915],"genre_scores_gemma":[0.98856807,0.00015532241,0.0014075038,0.00002790581,0.000003198128,0.000010282651,0.00040879415,0.000016284208,0.009402763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995797,0.000054270422,0.000012757771,0.00005040285,0.00012512453,0.00017776743],"domain_scores_gemma":[0.9990976,0.00018291865,0.000049420243,0.000024071265,0.0004966077,0.00014941265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004236737,0.00025655815,0.00032952664,0.00047126735,0.0016233673,0.0015024066,0.0006641282,0.000338132,0.0034046911],"category_scores_gemma":[0.0018017062,0.00019259461,0.0002620154,0.0015159436,0.0005908042,0.00042377546,0.00031378723,0.00034404342,0.00013703361],"study_design_candidate":"observational","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":[0.0009955232,0.00050703785,0.19054057,0.00041569097,0.00015114744,0.0007944503,0.0010412646,0.64110065,0.006883498,0.014200184,0.03326391,0.11010611],"study_design_scores_gemma":[0.00021784588,0.00017831176,0.27973083,0.000074976706,0.000111717214,0.00007250524,0.004290354,0.6833385,0.0050431597,0.0032572888,0.023619486,0.00006504499],"about_ca_topic_score_codex":0.9938626,"about_ca_topic_score_gemma":0.9974152,"teacher_disagreement_score":0.036287546,"about_ca_system_score_codex":0.036287546,"about_ca_system_score_gemma":0.027146159,"threshold_uncertainty_score":0.26328582},"labels":[],"label_agreement":null},{"id":"W4387267952","doi":"10.3390/w15193476","title":"Exploring the Sensitivity of the Transient Response following Power Failure to Air Valve and Pipeline Characteristics","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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 Toronto","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Outflow; Transient (computer programming); Water hammer; Environmental science; Body orifice; Overpressure; Mechanics; Surge; Sensitivity (control systems); Meteorology; Engineering; Mechanical engineering; Computer science; Geography; Physics","score_opus":0.0184308102432466,"score_gpt":0.19220473045078684,"score_spread":0.17377392020754023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387267952","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":[0.9732712,0.0001555554,0.021844618,0.00014659224,0.000025441159,0.00007295667,0.00028111573,0.00013550042,0.00406706],"genre_scores_gemma":[0.99900275,0.00003182291,0.00054776727,0.00000881633,0.0000012156264,0.0000136734125,0.00005125811,0.000008604137,0.00033414256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974877,0.0000633737,0.000012401598,0.000045280693,0.000045747387,0.000084468105],"domain_scores_gemma":[0.9979803,0.0016594367,0.00014388356,0.00008390381,0.00008992316,0.00004265958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077614986,0.00042845466,0.00044719287,0.00043305216,0.00027847002,0.0005726778,0.00041461954,0.0010239196,0.0017321605],"category_scores_gemma":[0.0041542295,0.0002479687,0.0006143369,0.00026919798,0.00058162306,0.0005688236,0.0005343408,0.00072809967,0.000141689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013358609,0.000048353766,0.004313797,0.00010915692,0.00003656543,0.000261103,0.000060623945,0.9818875,0.009549294,0.0004619699,0.0001440348,0.002993969],"study_design_scores_gemma":[0.000012309745,0.00041307817,0.006919787,0.000020363039,0.00003152133,0.000101315694,0.00017513431,0.9807287,0.010572311,0.00061888853,0.00038286482,0.00002382434],"about_ca_topic_score_codex":0.004754936,"about_ca_topic_score_gemma":0.0024007782,"teacher_disagreement_score":0.004754936,"about_ca_system_score_codex":0.00043953172,"about_ca_system_score_gemma":0.00039286708,"threshold_uncertainty_score":0.009454489},"labels":[],"label_agreement":null},{"id":"W4387501121","doi":"10.3390/w15193525","title":"Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Water hammer; Surge; Sizing; Head (geology); Volume (thermodynamics); Surge tank; Engineering; Marine engineering; Computer science; Mechanical engineering; Geology","score_opus":0.020870544943576513,"score_gpt":0.2546102792949694,"score_spread":0.23373973435139286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387501121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19391163,0.0001454111,0.8011471,0.00016751773,0.00003192245,0.000064819855,0.00007491349,0.00035728814,0.004099432],"genre_scores_gemma":[0.95056933,0.000064890846,0.048374005,0.000018000852,0.000008937178,0.00006119113,0.00004697489,0.000024318246,0.00083232985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997969,0.00006727513,0.0000130176595,0.000037327267,0.00005925351,0.00002630131],"domain_scores_gemma":[0.99904925,0.0006468928,0.00014185964,0.000033808916,0.00010991395,0.00001822884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000614279,0.0004900801,0.00056728,0.0004994003,0.00031018557,0.00057800906,0.00047796065,0.0006235523,0.0008756892],"category_scores_gemma":[0.0015969534,0.00038240344,0.0006160373,0.00033225215,0.0005510358,0.00036550307,0.00034097323,0.0005524752,0.00010215362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043675086,0.0000049480777,0.00021655612,0.000007502183,0.0000021315216,0.00000874088,0.0000051509796,0.99698275,0.0004446181,0.00040217943,0.00001800178,0.0019030641],"study_design_scores_gemma":[4.068305e-7,0.0000024764927,0.00002352613,5.967653e-7,4.0694692e-7,8.4450363e-7,7.388292e-7,0.99973625,0.000120434306,0.00009881227,0.000014994346,5.1734014e-7],"about_ca_topic_score_codex":0.00742316,"about_ca_topic_score_gemma":0.0047014863,"teacher_disagreement_score":0.00742316,"about_ca_system_score_codex":0.0007790249,"about_ca_system_score_gemma":0.0009434048,"threshold_uncertainty_score":0.014759898},"labels":[],"label_agreement":null},{"id":"W4387732344","doi":"10.1061/jwrmd5.wreng-6090","title":"How to Model an Intermittent Water Supply: Comparing Modeling Choices and Their Impact on Inequality","year":2023,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Hudbay Minerals (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Economics; Water supply; Inequality; Environmental science; Natural resource economics; Econometrics; Environmental economics; Mathematics; Environmental engineering","score_opus":0.03754018866994906,"score_gpt":0.25359222271731535,"score_spread":0.21605203404736628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387732344","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":[0.80561787,0.00051567814,0.1762663,0.0017675171,0.000082710896,0.00016670181,0.0021795325,0.00038075232,0.013022916],"genre_scores_gemma":[0.9484526,0.00036919885,0.04872304,0.00016454392,0.000018211882,0.00020600537,0.0009620791,0.00012648173,0.0009779289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990664,0.0005035063,0.00004816769,0.00015094645,0.0001351699,0.00009589471],"domain_scores_gemma":[0.99233776,0.006101095,0.0004878186,0.00038724983,0.00053562236,0.0001503832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024309799,0.00083220395,0.00056706107,0.0005936468,0.00047566497,0.0014593404,0.001209221,0.00085408066,0.002172765],"category_scores_gemma":[0.012651308,0.0003342942,0.000976016,0.0006930494,0.00076261937,0.0025320782,0.0009792728,0.0012439935,0.00014703796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014250653,0.00009512683,0.012533102,0.00017530992,0.00007264901,0.000044390057,0.00019950862,0.9613039,0.0005852398,0.011470187,0.0007209689,0.012657073],"study_design_scores_gemma":[0.000025216663,0.000058540656,0.0016982455,0.0000476588,0.000030253414,0.000010102169,0.00018974862,0.9895168,0.0006098087,0.0069040474,0.00089419127,0.000015319934],"about_ca_topic_score_codex":0.02972044,"about_ca_topic_score_gemma":0.0251607,"teacher_disagreement_score":0.02972044,"about_ca_system_score_codex":0.0016530205,"about_ca_system_score_gemma":0.00147539,"threshold_uncertainty_score":0.059094846},"labels":[],"label_agreement":null},{"id":"W4387763031","doi":"10.1061/jhend8.hyeng-13493","title":"Surge Analysis of Air Vessel with Different Connection Types in Pressurized Water Delivery Systems","year":2023,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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":"Surge; Connection (principal bundle); Environmental science; Hydraulics; Marine engineering; Hydrology (agriculture); Geotechnical engineering; Engineering; Civil engineering; Geology; Mechanical engineering; Electrical engineering; Aerospace engineering","score_opus":0.005940051292689286,"score_gpt":0.17291380344147245,"score_spread":0.16697375214878316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387763031","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":[0.9490344,0.0001503048,0.04857716,0.00006424231,0.000017938632,0.000028455905,0.000051694435,0.00022411095,0.0018517036],"genre_scores_gemma":[0.9986859,0.000034274046,0.0009827773,0.0000023762104,0.0000026301122,0.0000048116817,0.000015528982,0.0000052640557,0.00026654865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998248,0.000050746552,0.000008167162,0.000023827475,0.000067538,0.000024880908],"domain_scores_gemma":[0.9997644,0.00011793331,0.000041836814,0.000014904222,0.000048961287,0.000012019862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020477577,0.00027689186,0.00032851598,0.00035696485,0.00020048583,0.00032622827,0.0002647346,0.00036023065,0.00056526705],"category_scores_gemma":[0.0005410273,0.00018681568,0.0003199461,0.00026971873,0.0003728813,0.0004556135,0.00024862186,0.0002598297,0.00008472932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001019667,0.00011683808,0.01229961,0.00042981835,0.00007826935,0.001627714,0.00039905476,0.537618,0.3992003,0.0026440837,0.0007759074,0.04379079],"study_design_scores_gemma":[0.000019721432,0.00041353868,0.007650616,0.0000068459144,0.000026326596,0.000121348,0.00011051602,0.9661579,0.02473946,0.0003312881,0.0004056554,0.000016802758],"about_ca_topic_score_codex":0.0016845721,"about_ca_topic_score_gemma":0.0011129986,"teacher_disagreement_score":0.0016845721,"about_ca_system_score_codex":0.0002836626,"about_ca_system_score_gemma":0.00021917878,"threshold_uncertainty_score":0.003349483},"labels":[],"label_agreement":null},{"id":"W4388000914","doi":"10.1061/jpsea2.pseng-1414","title":"Discussion of “Estimating Particle Froude Number of Sewer Pipes by Boosting Machine-Learning Models”","year":2023,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"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 Ottawa; Université Laval; Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Froude number; Boosting (machine learning); Environmental science; Mathematical model; Civil engineering; Computer science; Geotechnical engineering; Engineering; Marine engineering; Artificial intelligence; Mechanics; Mathematics; Statistics; Physics; Flow (mathematics)","score_opus":0.01606504883729515,"score_gpt":0.2415664378580418,"score_spread":0.22550138902074665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388000914","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006807582,0.0011945599,0.9651446,0.016856162,0.002020234,0.00004332931,0.00012313062,0.00022610543,0.0075843353],"genre_scores_gemma":[0.4925739,0.003055956,0.44143605,0.014928756,0.010301883,0.00026475269,0.0004139792,0.00039087853,0.036633816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99859565,0.0006189263,0.00006251125,0.00033210986,0.00033223975,0.00005855941],"domain_scores_gemma":[0.9974498,0.0016980666,0.00012862221,0.0002369398,0.0004360713,0.000050482045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005101984,0.00059068593,0.0008925186,0.0005839404,0.00087180256,0.0016363778,0.0022347334,0.004247996,0.0019951987],"category_scores_gemma":[0.015242823,0.000583796,0.0011732477,0.00058028137,0.0022298542,0.0027995794,0.00131069,0.003091687,0.00060486875],"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":[0.000117377764,0.00005163117,0.0023864051,0.00023953718,0.00013022269,0.00039496,0.00022297149,0.37747827,0.0045174193,0.52808774,0.023395391,0.06297814],"study_design_scores_gemma":[0.000016603448,0.000036371977,0.0009944536,0.00004897326,0.000024770037,0.00012386021,0.000029890594,0.8372512,0.004219771,0.1369038,0.020304982,0.000045309625],"about_ca_topic_score_codex":0.006655186,"about_ca_topic_score_gemma":0.004325961,"teacher_disagreement_score":0.006655186,"about_ca_system_score_codex":0.001119701,"about_ca_system_score_gemma":0.0007860104,"threshold_uncertainty_score":0.026982188},"labels":[],"label_agreement":null},{"id":"W4388551961","doi":"10.18280/ijdne.180507","title":"The Resilience of Numerical Applications to Design Drinking Water Networks","year":2023,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Resilience (materials science); Environmental science; Computer science; Risk analysis (engineering); Engineering; Environmental planning; Environmental resource management; Business; Materials science","score_opus":0.008244292438243095,"score_gpt":0.23051809783192984,"score_spread":0.22227380539368674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388551961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0814226,0.00077208364,0.8926028,0.0012215305,0.00019081526,0.0002058598,0.000083786705,0.0013744099,0.022126118],"genre_scores_gemma":[0.80872566,0.00056177593,0.18611418,0.00021447074,0.000070992886,0.00025617552,0.000099065095,0.0003199694,0.003637705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982821,0.000865264,0.000113281014,0.00017541875,0.00043076224,0.00013318799],"domain_scores_gemma":[0.9912232,0.006386422,0.0006016038,0.00090845133,0.000710035,0.00017034568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038493816,0.0010124877,0.00065712485,0.0010979458,0.0010066878,0.0017739631,0.0013353085,0.0012221081,0.004445703],"category_scores_gemma":[0.01929396,0.001019771,0.0007576798,0.0006223607,0.0022464916,0.001447743,0.003070933,0.001862274,0.00083436526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046676818,0.000029407753,0.0016615618,0.00008795481,0.00002342141,0.000067973524,0.00016418884,0.95801586,0.0016895151,0.015037605,0.0003994131,0.022776393],"study_design_scores_gemma":[0.0000037441482,0.000013136913,0.00006676,0.00001929028,0.000003647537,0.000012689506,0.000023831883,0.9924084,0.0005926203,0.005587689,0.0012648874,0.0000032479975],"about_ca_topic_score_codex":0.004050137,"about_ca_topic_score_gemma":0.0023583567,"teacher_disagreement_score":0.004445703,"about_ca_system_score_codex":0.0011492157,"about_ca_system_score_gemma":0.0013415292,"threshold_uncertainty_score":0.020357728},"labels":[],"label_agreement":null},{"id":"W4388645442","doi":"10.2166/wrd.2023.063","title":"Smart water network infrastructures","year":2023,"lang":"en","type":"article","venue":"Water Reuse","topic":"Water Systems and 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":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ontario Institute of Technology","keywords":"Multiphysics; Fluent; Environmental science; Water flow; Water consumption; Computer science; Water pressure; Civil engineering; Environmental engineering; Engineering; Simulation; Computer simulation; Finite element method","score_opus":0.006555739723887461,"score_gpt":0.17408123468984676,"score_spread":0.1675254949659593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388645442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11774509,0.0006482092,0.688119,0.0024152873,0.00045727546,0.00041773412,0.0014176633,0.003707087,0.18507275],"genre_scores_gemma":[0.88631696,0.0007398483,0.0674147,0.0002753418,0.000057975984,0.00018838057,0.0011852139,0.00019277948,0.043628693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996835,0.00006349854,0.0000127047715,0.000075779535,0.00009951726,0.000064934844],"domain_scores_gemma":[0.99984205,0.000022795239,0.000017483886,0.000044152697,0.000051959883,0.000021445027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023718641,0.0005313936,0.00027660534,0.00041697937,0.0004819133,0.0009582311,0.000696601,0.00051939767,0.015360985],"category_scores_gemma":[0.00046750178,0.00020050074,0.00036308836,0.00065365044,0.00037410366,0.0016473595,0.0013548738,0.00049070345,0.0019059977],"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":[0.00017848158,0.00012765531,0.0022755726,0.00037940827,0.000060691313,0.0004095841,0.000120386285,0.533743,0.026215566,0.119211,0.026971288,0.29030734],"study_design_scores_gemma":[0.000060641356,0.00019530722,0.0014243295,0.0000893544,0.00004075635,0.00020433209,0.00030284515,0.6927596,0.014930716,0.06892722,0.22103016,0.000034665383],"about_ca_topic_score_codex":0.0042131264,"about_ca_topic_score_gemma":0.005340872,"teacher_disagreement_score":0.015360985,"about_ca_system_score_codex":0.0009992764,"about_ca_system_score_gemma":0.00087644765,"threshold_uncertainty_score":0.051387608},"labels":[],"label_agreement":null},{"id":"W4388723747","doi":"10.3390/axioms12111055","title":"Assessment of the Water Distribution Networks in the Kingdom of Saudi Arabia: A Mathematical Model","year":2023,"lang":"en","type":"article","venue":"Axioms","topic":"Water Systems and 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 Victoria","funders":"","keywords":"Betweenness centrality; Centrality; Vulnerability (computing); Adjacency matrix; Vulnerability assessment; Graph theory; Node (physics); Graph; Computer science; Event (particle physics); Adjacency list; Mathematics; Engineering; Theoretical computer science; Algorithm; Computer security; Statistics; Combinatorics","score_opus":0.01596564790347468,"score_gpt":0.23262843314671647,"score_spread":0.21666278524324178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388723747","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":[0.63322777,0.0008249065,0.32470062,0.0019028969,0.00003965915,0.00015582636,0.0011931567,0.00016708134,0.03778815],"genre_scores_gemma":[0.9800869,0.00033541492,0.015542624,0.000030832914,0.000008492607,0.000044413882,0.00014987127,0.000010490291,0.0037908703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997322,0.00010921779,0.000010345716,0.000052390325,0.00005001714,0.000045788525],"domain_scores_gemma":[0.99938667,0.0003119456,0.00010772062,0.000022376313,0.00014001838,0.00003126742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004949777,0.00052558986,0.00028449,0.0009715909,0.0004645362,0.0014219899,0.00065529556,0.00083505723,0.0014480492],"category_scores_gemma":[0.0013289194,0.00022184178,0.00044249973,0.0006824245,0.0006607361,0.0012543256,0.0008193194,0.00049220584,0.00013987461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010126169,0.0000100101715,0.001629166,0.000021357599,0.000009579853,0.00009949608,0.000059597383,0.9777548,0.00034708768,0.017735234,0.0002013381,0.002122264],"study_design_scores_gemma":[0.0000019765523,0.0000113281785,0.0004664181,0.000006795828,0.000007128789,0.000023012364,0.00007996595,0.9935595,0.00013585266,0.00509912,0.00060461415,0.000004179575],"about_ca_topic_score_codex":0.048617173,"about_ca_topic_score_gemma":0.035254743,"teacher_disagreement_score":0.048617173,"about_ca_system_score_codex":0.0030864214,"about_ca_system_score_gemma":0.0012989356,"threshold_uncertainty_score":0.09666842},"labels":[],"label_agreement":null},{"id":"W4388947887","doi":"10.1016/j.jclepro.2023.139876","title":"Transport of non-flushable wipes in sewers and its application in sewer management","year":2023,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Water Systems and 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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Alberta","keywords":"Sanitary sewer; Froude number; Environmental science; Flow velocity; Critical ionization velocity; Flow (mathematics); Geotechnical engineering; Mechanics; Environmental engineering; Geology; Physics","score_opus":0.005476460727117603,"score_gpt":0.19535529050506473,"score_spread":0.18987882977794712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388947887","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":[0.8456304,0.008110761,0.13609841,0.00045402278,0.00023952634,0.00012000366,0.00033562715,0.00016186156,0.008849469],"genre_scores_gemma":[0.9866044,0.002331201,0.006238174,0.000011136673,0.000035607456,0.0000189383,0.00009539032,0.00001049245,0.004654639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99984324,0.0000447192,0.0000096415615,0.00003934164,0.000040018156,0.000022925158],"domain_scores_gemma":[0.999645,0.00016993702,0.000038569924,0.000025227442,0.00010306277,0.000018154917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039358178,0.00040794726,0.00034758795,0.00086851045,0.00036625421,0.0011571898,0.0004884525,0.001025951,0.0015693592],"category_scores_gemma":[0.00081825245,0.00015351613,0.00072414463,0.00083414145,0.00033620564,0.0008227454,0.00031474148,0.00027848672,0.00022950629],"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":[0.0012818461,0.0007498786,0.028947065,0.0010386811,0.00015871743,0.0010357398,0.00028306345,0.6681761,0.11600054,0.01543844,0.0012954326,0.16559444],"study_design_scores_gemma":[0.000014239159,0.00051724643,0.009026295,0.000041567608,0.000077839424,0.00015091927,0.00031166876,0.95122,0.032917254,0.0026126236,0.0030768365,0.00003347449],"about_ca_topic_score_codex":0.006582484,"about_ca_topic_score_gemma":0.0032911974,"teacher_disagreement_score":0.006582484,"about_ca_system_score_codex":0.0005786595,"about_ca_system_score_gemma":0.00052471465,"threshold_uncertainty_score":0.0130883455},"labels":[],"label_agreement":null},{"id":"W4389295282","doi":"10.1021/acsapm.3c02209","title":"Deep Learning Analysis of the Propagation of Stabilizing Additive Hydrolysis in a Cross-Linked Polyethylene Pipe","year":2023,"lang":"en","type":"article","venue":"ACS Applied Polymer Materials","topic":"Water Systems and 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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrolysis; Scaling; Activation energy; Materials science; Polyethylene; Penetration (warfare); Molecule; Chemistry; Analytical Chemistry (journal); Composite material; Chromatography; Organic chemistry; Mathematics; Geometry","score_opus":0.007766862813285181,"score_gpt":0.21606262663978956,"score_spread":0.2082957638265044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389295282","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":[0.9394552,0.00017524468,0.05915846,0.00018494725,0.00001886638,0.00001351735,0.00017461841,0.00035402834,0.00046514536],"genre_scores_gemma":[0.99131304,0.000070851405,0.0074127004,0.000030596657,0.000005031057,0.000011337846,0.00031354648,0.00002467488,0.000818249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989116,0.000015261638,0.000004830186,0.000033860168,0.000025408728,0.00002948698],"domain_scores_gemma":[0.9994836,0.0002518778,0.00007090319,0.000026131156,0.00013190351,0.000035621197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000617982,0.00060395023,0.00031848592,0.0004556509,0.00015990218,0.00033823197,0.0005389803,0.0006519495,0.0006247638],"category_scores_gemma":[0.0012005463,0.00027789688,0.00058420765,0.0002900237,0.0004784215,0.00046607258,0.00031875595,0.0007783715,0.00012764093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021791157,0.00019193983,0.011095513,0.000075184355,0.00006781894,0.00023802521,0.00008739389,0.92851114,0.034927104,0.00084578944,0.00057654094,0.023165613],"study_design_scores_gemma":[0.0000014699857,0.000018732277,0.0013207144,0.0000015557745,0.000002665006,0.000005928558,0.0000049858236,0.9961204,0.0023822961,0.00010865542,0.000029465367,0.000003079106],"about_ca_topic_score_codex":0.008487474,"about_ca_topic_score_gemma":0.0045214724,"teacher_disagreement_score":0.008487474,"about_ca_system_score_codex":0.0007279845,"about_ca_system_score_gemma":0.0005647296,"threshold_uncertainty_score":0.016876101},"labels":[],"label_agreement":null},{"id":"W4389584972","doi":"10.17118/11143/21042","title":"Analysis of the modal behavior of a Francis model with differentsensors","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Modal; Computer science; Modal analysis; Engineering; Finite element method; Structural engineering; Materials science","score_opus":0.01014404007100621,"score_gpt":0.18664758649885763,"score_spread":0.1765035464278514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389584972","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":[0.5956469,0.00032771792,0.37674785,0.00045410436,0.000065594046,0.000054520722,0.0002917506,0.00063285214,0.025778765],"genre_scores_gemma":[0.9919155,0.00004905474,0.0041956124,0.000021733287,0.00000490776,0.000019343375,0.00006758845,0.000032512908,0.0036935862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998821,0.000028241828,0.000003293739,0.000020798516,0.000038896673,0.00002668925],"domain_scores_gemma":[0.99966013,0.00019751537,0.000029970011,0.00002628718,0.00006958932,0.000016605065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035070314,0.00048406955,0.00029069246,0.00033023552,0.00035137628,0.00048487517,0.00052852853,0.0012282998,0.003648054],"category_scores_gemma":[0.000983018,0.00017720649,0.0005414274,0.00018599453,0.0005200038,0.00053811946,0.00034413443,0.0006203227,0.00034248424],"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":[0.000108449014,0.00004011516,0.0007002055,0.00004806351,0.000020888541,0.00006835595,0.00009711809,0.97466147,0.014275598,0.0039494303,0.0003340449,0.0056963507],"study_design_scores_gemma":[0.0000016109748,0.000015737714,0.00021750804,0.000002413567,0.0000031552577,0.0000059328154,0.00001207525,0.998494,0.00093159237,0.00018889392,0.00012327378,0.0000038247463],"about_ca_topic_score_codex":0.012211295,"about_ca_topic_score_gemma":0.008308212,"teacher_disagreement_score":0.012211295,"about_ca_system_score_codex":0.000460427,"about_ca_system_score_gemma":0.0003997769,"threshold_uncertainty_score":0.024280429},"labels":[],"label_agreement":null},{"id":"W4390580432","doi":"10.1080/1573062x.2023.2295310","title":"A fuzzy group decision-making model for Water Distribution Network rehabilitation","year":2024,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Viewpoints; Prioritization; Rehabilitation; Fuzzy logic; Pipeline (software); Group decision-making; Computer science; Operations research; Engineering; Psychology; Management science; Medicine; Artificial intelligence; Physical therapy; Social psychology; Mechanical engineering","score_opus":0.006286326004036844,"score_gpt":0.20872206266581964,"score_spread":0.2024357366617828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390580432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124229,0.00055492617,0.85430354,0.0011264079,0.00017090769,0.0004087927,0.00062930596,0.00022233538,0.018354759],"genre_scores_gemma":[0.9137954,0.00037484043,0.076553844,0.000119054355,0.00003856697,0.00068818044,0.00027605493,0.000019437053,0.008134598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986897,0.00061269867,0.000050618844,0.00022305133,0.0001957705,0.00022818605],"domain_scores_gemma":[0.9986842,0.00076724536,0.00014652919,0.000029768984,0.00026574134,0.00010650928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027584683,0.0013168555,0.0015305418,0.0012154221,0.0014820427,0.0022516083,0.0022276868,0.002882987,0.006715377],"category_scores_gemma":[0.0027482493,0.0006890357,0.0014176659,0.0014386058,0.0010872388,0.0012782996,0.00115605,0.0018595922,0.0005217201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047674228,0.000029136918,0.00030130323,0.000031223983,0.00002379615,0.00009351561,0.00007429985,0.9903347,0.00023123452,0.005920685,0.00021191729,0.002700441],"study_design_scores_gemma":[0.000016088401,0.000026278438,0.00006788849,0.0000059967597,0.000008946528,0.000007008275,0.000024453306,0.99683577,0.000064985325,0.0026981444,0.00023808441,0.0000063490506],"about_ca_topic_score_codex":0.026242083,"about_ca_topic_score_gemma":0.01652645,"teacher_disagreement_score":0.026242083,"about_ca_system_score_codex":0.00315487,"about_ca_system_score_gemma":0.0027606967,"threshold_uncertainty_score":0.05217868},"labels":[],"label_agreement":null},{"id":"W4390671010","doi":"10.25286/repa.v9i1.2784","title":"Sistema de Recomendação de Hidrômetro e Análise Preditiva de Perdas em Redes de Telemetria","year":2023,"lang":"pt","type":"article","venue":"Revista de Engenharia e Pesquisa Aplicada","topic":"Water Systems and 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":"Discovery Air (Canada)","funders":"","keywords":"Physics; Humanities; Art","score_opus":0.020147725005140605,"score_gpt":0.25558285988395957,"score_spread":0.23543513487881895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390671010","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":[0.6847309,0.006924574,0.25845864,0.0019621272,0.00035147276,0.0025894349,0.008754579,0.006111195,0.030117009],"genre_scores_gemma":[0.8751565,0.0019532724,0.11252802,0.00022284777,0.0000646178,0.00073038245,0.002729098,0.00019045916,0.006424884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99167037,0.0024443057,0.0009922828,0.001669777,0.0028720582,0.0003511609],"domain_scores_gemma":[0.9759369,0.009359432,0.004261657,0.0026749924,0.007334231,0.00043276936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009376774,0.0012931611,0.0012478193,0.004633745,0.0007178105,0.0038567004,0.0015221664,0.0012124156,0.003905578],"category_scores_gemma":[0.024019325,0.00080405024,0.0011539079,0.0038210538,0.00079247385,0.0028133458,0.0011241281,0.0008322837,0.0016109837],"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":[0.0010612161,0.0004994556,0.42938524,0.0029028102,0.0006978141,0.00026439663,0.0029292218,0.02006664,0.02650801,0.003280676,0.003936372,0.5084682],"study_design_scores_gemma":[0.00020151994,0.0030193378,0.6880595,0.0017655761,0.0013932168,0.0008257976,0.006363255,0.15458603,0.06694051,0.008906885,0.06748575,0.00045267155],"about_ca_topic_score_codex":0.014099323,"about_ca_topic_score_gemma":0.015980134,"teacher_disagreement_score":0.014099323,"about_ca_system_score_codex":0.0019058228,"about_ca_system_score_gemma":0.0025131376,"threshold_uncertainty_score":0.049589753},"labels":[],"label_agreement":null},{"id":"W4390679114","doi":"10.46254/ev01.20230117","title":"Municipal Water Pipeline Leak Detection System","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Leak detection; Pipeline (software); Leak; Computer science; Pipeline transport; Computer security; Environmental science; Operating system; Environmental engineering","score_opus":0.009554660773998195,"score_gpt":0.17915975001283674,"score_spread":0.16960508923883855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390679114","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":[0.5513417,0.0003874121,0.30337548,0.0008441159,0.00018574306,0.0006844346,0.02246184,0.08379318,0.03692602],"genre_scores_gemma":[0.9595546,0.00011557603,0.021922138,0.000056405956,0.000019394598,0.00014074908,0.007456825,0.00016766795,0.010566591],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968076,0.000026009626,0.000020647918,0.00011178522,0.00011484042,0.000045927776],"domain_scores_gemma":[0.9996592,0.000027631531,0.00004597368,0.00005814561,0.00018547705,0.000023657458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021568438,0.000557799,0.00051151554,0.00089263474,0.000449233,0.0006884033,0.0005645208,0.0003925343,0.009734472],"category_scores_gemma":[0.0008383088,0.0002001199,0.00024571572,0.0012373668,0.00012915618,0.0009440082,0.00068747695,0.00034441915,0.003469251],"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":[0.0014036108,0.0004743633,0.07787973,0.00057039346,0.0001371754,0.00079311006,0.00042512934,0.17367151,0.066139154,0.0033417686,0.07838576,0.5967782],"study_design_scores_gemma":[0.00007862179,0.00020323049,0.039669544,0.00003413083,0.000064443615,0.0001805953,0.00027162637,0.8915121,0.034266427,0.0015283684,0.032114003,0.00007687775],"about_ca_topic_score_codex":0.027012942,"about_ca_topic_score_gemma":0.020110132,"teacher_disagreement_score":0.027012942,"about_ca_system_score_codex":0.0011262343,"about_ca_system_score_gemma":0.0011234643,"threshold_uncertainty_score":0.053711414},"labels":[],"label_agreement":null},{"id":"W4390974155","doi":"10.2166/hydro.2024.041","title":"Improving incomplete mixing modeling for junctions of water distribution networks","year":2024,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixing (physics); Mechanics; Flow (mathematics); Volumetric flow rate; Distribution (mathematics); Pipe network analysis; Software; Materials science; Mathematics; Simulation; Computer science; Physics; Mathematical analysis","score_opus":0.008826252977722401,"score_gpt":0.19132060315688137,"score_spread":0.18249435017915897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390974155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24786705,0.0002721739,0.7490382,0.0001281813,0.000034654575,0.000048644713,0.00019129472,0.00041216626,0.0020076474],"genre_scores_gemma":[0.95981,0.00010023673,0.038842294,0.000022074088,0.000013870322,0.000047141668,0.0002025077,0.000047895825,0.00091396226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999196,0.00032172623,0.00003888274,0.00020931353,0.00013058643,0.000103565944],"domain_scores_gemma":[0.9981006,0.0011294772,0.00034411828,0.0001066999,0.00022437329,0.00009473854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019558046,0.0008415338,0.0013440178,0.00076752645,0.0005066794,0.0012589289,0.0015080335,0.0012352426,0.00078712904],"category_scores_gemma":[0.004744887,0.00067804893,0.0012834161,0.000562426,0.00075004983,0.0014504672,0.0012451854,0.0012757034,0.0001263885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014152096,0.0000073997853,0.0006246616,0.0000054321013,0.000007662304,0.000013914258,0.0000091630845,0.99703634,0.00033108852,0.0008874858,0.000022532575,0.0010402397],"study_design_scores_gemma":[8.608376e-7,0.0000031161705,0.000046835838,5.676641e-7,0.0000012321643,8.720296e-7,0.000001226579,0.9996251,0.00010769526,0.00018724048,0.000024170951,0.000001046708],"about_ca_topic_score_codex":0.018426426,"about_ca_topic_score_gemma":0.0070389095,"teacher_disagreement_score":0.018426426,"about_ca_system_score_codex":0.0012899485,"about_ca_system_score_gemma":0.0010171991,"threshold_uncertainty_score":0.03663832},"labels":[],"label_agreement":null},{"id":"W4391063736","doi":"10.1016/j.heliyon.2024.e25036","title":"An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Water Systems and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Concordia University","funders":"Technická Univerzita v Liberci; Ministerstvo Školství, Mládeže a Tělovýchovy","keywords":"Delphi method; Computer science; Decision support system; Big data; Sustainable management; Groundwater; Bridging (networking); Intelligent decision support system; Analytics; Data management; Data mining; Engineering; Artificial intelligence; Sustainability","score_opus":0.00511149155794467,"score_gpt":0.2128367881841862,"score_spread":0.20772529662624153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391063736","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":[0.021245254,0.0003418103,0.9631353,0.00060733344,0.00018394829,0.00019068221,0.00039807757,0.0054428377,0.008454749],"genre_scores_gemma":[0.7669009,0.00036246923,0.22376595,0.00030164229,0.00012060429,0.00030872016,0.00069404236,0.00010282163,0.0074429456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945456,0.00010050852,0.000058361682,0.00015004733,0.00018414977,0.0000523054],"domain_scores_gemma":[0.9995671,0.00014887226,0.000057110465,0.000048713886,0.00014536333,0.000032877328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075673027,0.0006978888,0.000588099,0.0007980733,0.00056172366,0.0017253491,0.0010558576,0.00094633695,0.006197411],"category_scores_gemma":[0.0016016542,0.00021734332,0.00036313976,0.0006073508,0.00030523862,0.0011638863,0.0009216053,0.00065157184,0.0013305069],"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":[0.0006358323,0.00038105703,0.0035090477,0.00038881516,0.000114865376,0.00061005965,0.0003038692,0.28756604,0.03316773,0.028665405,0.016064415,0.62859285],"study_design_scores_gemma":[0.0000508644,0.00014735729,0.0007672232,0.000044593653,0.00003844928,0.00008628932,0.00006713582,0.964327,0.008109777,0.009354726,0.016976798,0.000029798162],"about_ca_topic_score_codex":0.0023464162,"about_ca_topic_score_gemma":0.0018284433,"teacher_disagreement_score":0.006197411,"about_ca_system_score_codex":0.0006226072,"about_ca_system_score_gemma":0.00092238013,"threshold_uncertainty_score":0.020732462},"labels":[],"label_agreement":null},{"id":"W4391351621","doi":"10.2175/193864718825159016","title":"Wastewater Disinfection Using Peracids: Simulation of System Performance and Process Control Alternatives Using Batch Inactivation Kinetics from a 10-Plant North American Study","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Water Environment Federation","topic":"Water Systems and 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":"Wastewater; Kinetics; Process engineering; Process (computing); Sewage treatment; Process control; Environmental science; Waste management; Environmental engineering; Pulp and paper industry; Computer science; Engineering","score_opus":0.010590288042723453,"score_gpt":0.19573053924369485,"score_spread":0.18514025120097138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391351621","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":[0.9949968,0.00007826952,0.0021808797,0.00012678847,0.000012099053,0.000061818224,0.00020023528,0.000028537703,0.0023145222],"genre_scores_gemma":[0.9949586,0.00013391515,0.002216268,0.00004166049,0.0000057104135,0.00011127118,0.00025370275,0.000010039689,0.002268875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966776,0.00011506783,0.000017332166,0.00005499606,0.000060343664,0.00008465164],"domain_scores_gemma":[0.998314,0.0011087934,0.00013929041,0.000064607004,0.00027505166,0.00009826565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009335841,0.0009285486,0.00096489274,0.00048431646,0.0006442146,0.0007702557,0.0011778184,0.0017722263,0.0034053188],"category_scores_gemma":[0.0011876713,0.00058394903,0.0013700878,0.00053475687,0.0005219287,0.0006339886,0.000638274,0.0013058952,0.0002792562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005248376,0.00057928584,0.0037419845,0.0000652067,0.000043525608,0.000094698305,0.00006032125,0.98754686,0.0041175117,0.0003619653,0.0003010835,0.0025627525],"study_design_scores_gemma":[0.00008647942,0.0006316697,0.0017446923,0.0000033792062,0.000027172515,0.000010741615,0.000084755666,0.9945411,0.0024463432,0.00013484385,0.00027376195,0.000014998776],"about_ca_topic_score_codex":0.04325269,"about_ca_topic_score_gemma":0.031284057,"teacher_disagreement_score":0.04325269,"about_ca_system_score_codex":0.0019237568,"about_ca_system_score_gemma":0.0014662894,"threshold_uncertainty_score":0.08600187},"labels":[],"label_agreement":null},{"id":"W4391424811","doi":"10.1007/978-3-031-35471-7_67","title":"Evaluation of Process-Based Ensemble Models for Forecasting Point-of-Consumption Free Residual Chlorine in Refugee Settlements","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Water Systems and 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":"Centre for Global Health Research; York University","funders":"","keywords":"Residual; Refugee; Human settlement; Econometrics; Computer science; Environmental science; Geography; Mathematics; Algorithm; Archaeology","score_opus":0.042936435567245156,"score_gpt":0.25531566020245844,"score_spread":0.21237922463521328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391424811","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":[0.9500627,0.00053145294,0.04605448,0.00023187678,0.00011983441,0.00004698407,0.000515783,0.00046665565,0.0019701745],"genre_scores_gemma":[0.9940135,0.0000986528,0.0051373835,0.00001644254,0.000016799075,0.000022730903,0.0003900202,0.000017673277,0.00028690457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955827,0.0001734567,0.00003474097,0.000107951084,0.000067138906,0.00005844026],"domain_scores_gemma":[0.99790156,0.0014138089,0.00009220264,0.00013546216,0.0003791325,0.000077857134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025853086,0.0013513596,0.0016220487,0.0006768884,0.0004724728,0.001137767,0.0010281316,0.0014642983,0.00066380895],"category_scores_gemma":[0.0028524813,0.0005471475,0.001266047,0.00060577336,0.00032747563,0.0012114045,0.0006709107,0.0010868561,0.00017953174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019879449,0.00012638411,0.0026384862,0.00001991236,0.00010954065,0.000020083198,0.000014994029,0.9831728,0.00065344275,0.00014811565,0.00022435078,0.012673161],"study_design_scores_gemma":[0.000004706954,0.000023850414,0.00039135088,0.0000010500519,0.000008517601,0.0000014071846,0.000003414761,0.9993625,0.00014208097,0.00004541332,0.0000127344465,0.000002954446],"about_ca_topic_score_codex":0.03707174,"about_ca_topic_score_gemma":0.016469758,"teacher_disagreement_score":0.03707174,"about_ca_system_score_codex":0.0009798174,"about_ca_system_score_gemma":0.0008099708,"threshold_uncertainty_score":0.07371193},"labels":[],"label_agreement":null},{"id":"W4391444982","doi":"10.52202/072781-0010","title":"Enhancing Pipeline Assessments: Answering the Challenge of Unpiggable Liquid Assets with Self-Propelled Solutions","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Imperial Oil (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Programming language","score_opus":0.01266114309362914,"score_gpt":0.22546959692848906,"score_spread":0.2128084538348599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391444982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12849057,0.0013010582,0.83303285,0.0061338576,0.00025877287,0.00030576796,0.00031623978,0.0013992277,0.028761653],"genre_scores_gemma":[0.8684833,0.00076209044,0.12325866,0.00040537457,0.00006462413,0.00008396725,0.00016315925,0.00019513682,0.006583758],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99811065,0.00080862443,0.000046283043,0.00023002572,0.00065652083,0.00014783988],"domain_scores_gemma":[0.99517304,0.0022123104,0.00048030776,0.00047948761,0.0013945232,0.0002603056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032199381,0.001033056,0.000780134,0.0008787305,0.0008015296,0.003681801,0.0017077706,0.0023819199,0.0051811775],"category_scores_gemma":[0.013082219,0.0004263643,0.00039822768,0.000767747,0.0011766163,0.0063434104,0.003769095,0.0021240674,0.0011110943],"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":[0.00044642756,0.00040578598,0.0053122514,0.0007098707,0.00011006139,0.0003985481,0.0005979692,0.360044,0.024035702,0.06957235,0.009671995,0.52869517],"study_design_scores_gemma":[0.000050388022,0.0004888847,0.0012680982,0.00015749916,0.000050150193,0.00014960035,0.0006952748,0.86195666,0.022374023,0.093567625,0.019168286,0.00007345151],"about_ca_topic_score_codex":0.0025878143,"about_ca_topic_score_gemma":0.0037988364,"teacher_disagreement_score":0.0051811775,"about_ca_system_score_codex":0.0009951598,"about_ca_system_score_gemma":0.0027979533,"threshold_uncertainty_score":0.017332733},"labels":[],"label_agreement":null},{"id":"W4391469854","doi":"10.21203/rs.3.rs-3918815/v1","title":"Analysis of water losses in seven small and medium-sized water distribution networks in the south region of Cameroon (Central Africa)","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Water Systems and 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":"Université de Saint-Boniface","funders":"","keywords":"Distribution (mathematics); Geography; Water resource management; Environmental science; Physical geography; Mathematics","score_opus":0.036347827513490624,"score_gpt":0.2733981211793956,"score_spread":0.23705029366590497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391469854","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":[0.9979553,0.000058512927,0.0009699907,0.000053936827,0.0000034482666,0.000020889767,0.000344717,0.000011640643,0.00058146624],"genre_scores_gemma":[0.99903524,0.00003423905,0.00033892685,0.0000033906003,0.0000012461599,0.000010675373,0.00022766605,0.0000027525089,0.00034585854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99963534,0.00012288417,0.000016106005,0.00005743346,0.00004435507,0.00012391403],"domain_scores_gemma":[0.99911505,0.00050592155,0.00012152984,0.000024828983,0.00015250295,0.000080306556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060076226,0.00047514949,0.00035292094,0.0012979753,0.000553397,0.0008638889,0.0005237814,0.00045839438,0.0016000591],"category_scores_gemma":[0.0015576584,0.0002485126,0.00039796127,0.0019289927,0.0006180068,0.00067438104,0.00040663703,0.0003284117,0.0000756776],"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":[0.00089221826,0.00025811637,0.30921245,0.00016347542,0.00027846094,0.0025619946,0.00053552934,0.65325886,0.0047155065,0.0031699198,0.0017580042,0.02319556],"study_design_scores_gemma":[0.000046711662,0.00025795758,0.43153945,0.00003387865,0.00015106893,0.00035376978,0.003014605,0.5592202,0.002941858,0.001073029,0.0013280059,0.000039477218],"about_ca_topic_score_codex":0.09324335,"about_ca_topic_score_gemma":0.06874041,"teacher_disagreement_score":0.09324335,"about_ca_system_score_codex":0.0039092978,"about_ca_system_score_gemma":0.0007197751,"threshold_uncertainty_score":0.18540126},"labels":[],"label_agreement":null},{"id":"W4391503595","doi":"10.1061/jwrmd5.wreng-6171","title":"Roles of Value in the Evaluation and Modeling of Decision Strategies for Pipe Maintenance in Water Distribution Networks","year":2024,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Value (mathematics); Distribution (mathematics); Computer science; Environmental science; Operations research; Engineering; Mathematics","score_opus":0.01835343392755979,"score_gpt":0.2502096553833209,"score_spread":0.23185622145576112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391503595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3135465,0.0013679774,0.6401663,0.0062232763,0.0001498319,0.00029289146,0.000358829,0.000099524696,0.037794992],"genre_scores_gemma":[0.9656236,0.0005331482,0.031272687,0.00015571795,0.000044321077,0.00016920154,0.00005799675,0.00002480238,0.0021184944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678016,0.0023147885,0.00007361144,0.00020945085,0.00032705543,0.00029498374],"domain_scores_gemma":[0.98533857,0.012362826,0.0010820737,0.00027026702,0.0005837088,0.0003626228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054605952,0.00082635955,0.0010541014,0.001158834,0.0009956103,0.0039198063,0.0018127295,0.0024330379,0.0019189616],"category_scores_gemma":[0.021029035,0.0006893901,0.0009605844,0.0012593636,0.0027319046,0.0038157534,0.0016326763,0.0020638697,0.00012780036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003076218,0.000035222565,0.0017837632,0.000023457018,0.000022484297,0.00006525932,0.00010425626,0.9240665,0.0000934236,0.070710704,0.00016300639,0.00290107],"study_design_scores_gemma":[0.000008722289,0.000023580069,0.0002545674,0.000022933145,0.000008835058,0.000012188636,0.000080940816,0.96116877,0.000061455976,0.03795031,0.00039659836,0.000011061584],"about_ca_topic_score_codex":0.015515159,"about_ca_topic_score_gemma":0.013539714,"teacher_disagreement_score":0.015515159,"about_ca_system_score_codex":0.0049943826,"about_ca_system_score_gemma":0.0029356873,"threshold_uncertainty_score":0.03623694},"labels":[],"label_agreement":null},{"id":"W4391607876","doi":"10.1029/2023wr035291","title":"Advanced Analytical Model for Interpreting Oscillatory Pumping Tests With Wellbore Skin and Rate‐Dependent Skin Effects","year":2024,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water Systems and 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":"Wellbore; Skin effect; Geology; Skin conductance; Petroleum engineering; Geotechnical engineering; Mechanics; Engineering; Physics; Biomedical engineering","score_opus":0.0168585697826866,"score_gpt":0.28564301583818386,"score_spread":0.26878444605549723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391607876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013351968,0.00007691953,0.9802002,0.00016794507,0.000047142323,0.00011723148,0.00012589498,0.0009158928,0.00499688],"genre_scores_gemma":[0.7515233,0.00048089065,0.22680834,0.00023937285,0.00006860352,0.00097132206,0.00027974552,0.00042018856,0.019208157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971217,0.000065732,0.000019292705,0.000059082147,0.00010820974,0.000035521356],"domain_scores_gemma":[0.99939394,0.00024047829,0.00006955879,0.0000463919,0.00023772831,0.000011943457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006134599,0.00090291235,0.0006075117,0.00072039646,0.0004448684,0.0010395383,0.0016451684,0.0015595676,0.004325444],"category_scores_gemma":[0.0025604214,0.0005189088,0.0007577658,0.00046269508,0.0004537364,0.00088138354,0.00057268934,0.0008268908,0.0010962897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030995056,0.000043079835,0.000519244,0.00006893415,0.000014322068,0.00015659105,0.00008833838,0.9603603,0.01162319,0.0055271895,0.0010763712,0.020491473],"study_design_scores_gemma":[0.0000020890384,0.0000054999527,0.00003792048,0.0000031026436,0.0000025362144,0.000008633303,0.0000044505286,0.99880123,0.00047778702,0.00035243854,0.0003018699,0.0000024013677],"about_ca_topic_score_codex":0.008462703,"about_ca_topic_score_gemma":0.006053085,"teacher_disagreement_score":0.008462703,"about_ca_system_score_codex":0.00091956946,"about_ca_system_score_gemma":0.0015213325,"threshold_uncertainty_score":0.016826868},"labels":[],"label_agreement":null},{"id":"W4391627408","doi":"10.1061/jpsea2.pseng-1521","title":"Discussion of “Predicting Solid-Particle Erosion Rate of Pipelines Using Support Vector Machine with Improved Sparrow Search Algorithm”","year":2024,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"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 Ottawa; Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Pipeline transport; Erosion; Sparrow; Algorithm; Computer science; Particle (ecology); Geology; Marine engineering; Mathematical optimization; Geotechnical engineering; Engineering; Mathematics; Mechanical engineering; Artificial intelligence; Geomorphology; Oceanography; Ecology","score_opus":0.013488364172935363,"score_gpt":0.25860765445653916,"score_spread":0.2451192902836038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391627408","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022548331,0.0021078966,0.9526132,0.010172011,0.0020652236,0.0000662132,0.00016505859,0.00039738833,0.00986469],"genre_scores_gemma":[0.54250497,0.003144653,0.40660095,0.00665591,0.0036011066,0.00020736389,0.00043413325,0.00024039266,0.036610525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934214,0.00020427404,0.000056622,0.00015010686,0.00021467476,0.0000321992],"domain_scores_gemma":[0.9989512,0.0005678246,0.000047659443,0.00008925432,0.00032534983,0.000018752664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001979016,0.0004431422,0.00060632935,0.0003532668,0.0005243666,0.0010674039,0.001584504,0.002225708,0.0022215734],"category_scores_gemma":[0.005916661,0.00028758385,0.0009528152,0.0005134196,0.0010364717,0.0017615545,0.0005561636,0.0016064093,0.00048558772],"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":[0.00018941706,0.000084666994,0.002729017,0.00037413693,0.00017200479,0.00049400894,0.00015526124,0.67302966,0.008345478,0.1978835,0.016517604,0.10002537],"study_design_scores_gemma":[0.000014274974,0.000056822635,0.0006891025,0.000026951075,0.000019173882,0.000092719856,0.000027321452,0.95655274,0.005191246,0.02629845,0.011010504,0.000020689899],"about_ca_topic_score_codex":0.005608063,"about_ca_topic_score_gemma":0.0036234704,"teacher_disagreement_score":0.005608063,"about_ca_system_score_codex":0.00051184936,"about_ca_system_score_gemma":0.00064227585,"threshold_uncertainty_score":0.011150837},"labels":[],"label_agreement":null},{"id":"W4391644236","doi":"10.2478/johh-2023-0036","title":"Simulation and prediction of water temperature in a water transfer channel during winter periods using a new approach based on the wavelet noise reduction-deep learning method","year":2024,"lang":"en","type":"article","venue":"Journal of Hydrology and Hydromechanics","topic":"Water Systems and 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 Northern British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Wavelet; Noise reduction; Noise (video); Heat transfer; Environmental science; Temperature measurement; Artificial neural network; Air temperature; Range (aeronautics); Channel (broadcasting); Atmospheric temperature range; Meteorology; Computer science; Mechanics; Materials science; Artificial intelligence; Thermodynamics; Physics","score_opus":0.01192638521456799,"score_gpt":0.2158879333723481,"score_spread":0.20396154815778011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391644236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42748937,0.00018557104,0.5689373,0.00024062247,0.000063700325,0.000050082952,0.00009877613,0.0004899274,0.0024446396],"genre_scores_gemma":[0.9765679,0.00006470533,0.022381373,0.000026402417,0.000010870949,0.000046991285,0.00007446345,0.00001411933,0.00081320095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998721,0.000029250567,0.000008958602,0.000035187823,0.00002841114,0.000026164884],"domain_scores_gemma":[0.99982005,0.00007823793,0.000023003773,0.000011077551,0.000048235845,0.000019415693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004362433,0.0005547492,0.0005919729,0.00036956233,0.00035612934,0.00045633878,0.00065812044,0.0008118443,0.00060342235],"category_scores_gemma":[0.0005881708,0.0003760384,0.0006940378,0.0003340957,0.00048179063,0.0005541563,0.00049471925,0.0006497533,0.000065601314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015071764,0.00002477089,0.001065354,0.000008669658,0.000011960453,0.000020138381,0.000008027595,0.99376005,0.0008294682,0.00023299313,0.00005169797,0.003971803],"study_design_scores_gemma":[6.1200024e-7,0.0000018418292,0.00003373182,1.4448709e-7,4.7200427e-7,3.6314492e-7,4.2325604e-7,0.9998958,0.000042670265,0.000019076935,0.0000044655644,3.8043032e-7],"about_ca_topic_score_codex":0.022399087,"about_ca_topic_score_gemma":0.01055614,"teacher_disagreement_score":0.022399087,"about_ca_system_score_codex":0.0005816613,"about_ca_system_score_gemma":0.00089322263,"threshold_uncertainty_score":0.044537425},"labels":[],"label_agreement":null},{"id":"W4391800488","doi":"","title":"Chapter 16. Model capacity extension methods","year":2023,"lang":"en","type":"book-chapter","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Water Systems and 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":"Agriculture and Agri-Food Canada","funders":"","keywords":"Extension (predicate logic); Computer science; Programming language","score_opus":0.034608720319133694,"score_gpt":0.22820405434149552,"score_spread":0.19359533402236181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391800488","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":[0.0013376487,0.009853671,0.8423724,0.0019661377,0.0015030485,0.00007196731,0.0011504298,0.0006715404,0.14107323],"genre_scores_gemma":[0.15641491,0.026624683,0.37386924,0.00234543,0.004288466,0.00075155357,0.004814496,0.0031453143,0.42774588],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963176,0.00012944029,0.000015801948,0.00007104081,0.00012771029,0.000024110657],"domain_scores_gemma":[0.999366,0.0003885727,0.000024999756,0.000087572254,0.000116020594,0.000016828537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086810964,0.0014413904,0.00094702654,0.000618248,0.00043612378,0.00147157,0.0014022319,0.0012084927,0.029294815],"category_scores_gemma":[0.002636053,0.0006890282,0.0013897681,0.00071720366,0.00062644127,0.002057109,0.001153052,0.002767038,0.0081697265],"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":[0.000034631827,0.0000987353,0.0003062093,0.00045484555,0.00010674976,0.00010651749,0.00011615237,0.20590606,0.0013915166,0.4911691,0.1465157,0.15379386],"study_design_scores_gemma":[0.0000111668705,0.000016834381,0.00021918298,0.00024230313,0.000036590747,0.00010650489,0.000032082273,0.33922645,0.0008338875,0.50207317,0.15717624,0.000025713347],"about_ca_topic_score_codex":0.0035924313,"about_ca_topic_score_gemma":0.0035245304,"teacher_disagreement_score":0.029294815,"about_ca_system_score_codex":0.001070283,"about_ca_system_score_gemma":0.00088550954,"threshold_uncertainty_score":0.09800094},"labels":[],"label_agreement":null},{"id":"W4391930090","doi":"10.1109/oncon60463.2023.10430759","title":"Nonlinear filtering based on lattice trajectory piecewise linear approximation with application to a wastewater treatment plant","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Alberta","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Nonlinear system; Trajectory; Piecewise linear function; Lattice (music); Piecewise; Applied mathematics; Mathematics; Control theory (sociology); Computer science; Mathematical optimization; Mathematical analysis; Physics; Artificial intelligence; Acoustics","score_opus":0.012923312405293626,"score_gpt":0.20338058278380794,"score_spread":0.19045727037851432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391930090","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":[0.15227094,0.00048820424,0.8441623,0.00026455004,0.000048472775,0.000034835506,0.000050719063,0.0004955425,0.0021843954],"genre_scores_gemma":[0.96698374,0.0002854915,0.03172388,0.000017304848,0.0000112024145,0.000030676194,0.000037871374,0.000012623364,0.000897299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998498,0.00004401497,0.000008850555,0.00002705051,0.0000513471,0.000018940536],"domain_scores_gemma":[0.9996364,0.00018975986,0.000047130212,0.00002144403,0.00009240408,0.000012768623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032311442,0.00036451378,0.00051783706,0.00022875454,0.00041463005,0.00038162063,0.00031878936,0.00062402437,0.00066669186],"category_scores_gemma":[0.0008693682,0.00016638842,0.00043993728,0.00046179863,0.0002612461,0.00037943356,0.00033425726,0.00057776313,0.00009017955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007503468,0.000024012024,0.00087830913,0.00007462477,0.000017279846,0.00014185975,0.000058209855,0.9667873,0.005897074,0.0012860454,0.00015697547,0.024603255],"study_design_scores_gemma":[0.0000018597456,0.000019993215,0.00011494629,8.8861316e-7,0.0000022188092,0.0000069110056,0.0000047780964,0.99917775,0.00049831095,0.0001026864,0.00006666502,0.000002903653],"about_ca_topic_score_codex":0.024552196,"about_ca_topic_score_gemma":0.0071944008,"teacher_disagreement_score":0.024552196,"about_ca_system_score_codex":0.00043214328,"about_ca_system_score_gemma":0.0005452748,"threshold_uncertainty_score":0.04881859},"labels":[],"label_agreement":null},{"id":"W4391993024","doi":"10.32920/25262803","title":"Identifying Source Hotspots of “Non-Flushables” in Sewer Systems Through Machine Learning and Imaging Sensors","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Sanitary sewer; Installation; Raspberry pi; Computer science; Outreach; Environmental science; Embedded system; Environmental engineering; Operating system; Internet of Things","score_opus":0.009399253004610942,"score_gpt":0.2272964376402757,"score_spread":0.21789718463566476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391993024","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":[0.92172885,0.00011918776,0.07615556,0.00007205859,0.000009926494,0.000022738534,0.00007894361,0.00033824157,0.0014745605],"genre_scores_gemma":[0.98072547,0.000063955136,0.018232837,0.000011977031,0.000004122055,0.000008595477,0.000066279244,0.000017162069,0.00086954836],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99984145,0.000024164381,0.000006578399,0.00005284519,0.00004795493,0.00002698821],"domain_scores_gemma":[0.9996841,0.00013746782,0.000077853605,0.000019738989,0.00006914815,0.000011728438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021146615,0.0003866955,0.00034570726,0.00074127666,0.00022371885,0.0005688144,0.00031107725,0.00042133927,0.000935696],"category_scores_gemma":[0.00061420666,0.00021058228,0.0002395932,0.00046639214,0.00026746598,0.00059434597,0.0002409767,0.00023907085,0.0002272608],"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":[0.00065726525,0.0005137006,0.10421269,0.00026338315,0.00009640216,0.0004290218,0.0004099173,0.29906932,0.2912878,0.0010202768,0.0010361422,0.3010041],"study_design_scores_gemma":[0.000008663006,0.00014189552,0.0405909,0.000011287038,0.000030307045,0.00007173483,0.00019088447,0.85617757,0.10161662,0.00055864715,0.0005837053,0.000017742886],"about_ca_topic_score_codex":0.004328895,"about_ca_topic_score_gemma":0.006892627,"teacher_disagreement_score":0.004328895,"about_ca_system_score_codex":0.00039588643,"about_ca_system_score_gemma":0.00029426167,"threshold_uncertainty_score":0.008607447},"labels":[],"label_agreement":null},{"id":"W4391993185","doi":"10.32920/25262803.v1","title":"Identifying Source Hotspots of “Non-Flushables” in Sewer Systems Through Machine Learning and Imaging Sensors","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Open source; Computer science; Environmental science; Operating system","score_opus":0.009399253004610942,"score_gpt":0.2272964376402757,"score_spread":0.21789718463566476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391993185","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":[0.92172885,0.00011918776,0.07615556,0.00007205859,0.000009926494,0.000022738534,0.00007894361,0.00033824157,0.0014745605],"genre_scores_gemma":[0.98072547,0.000063955136,0.018232837,0.000011977031,0.000004122055,0.000008595477,0.000066279244,0.000017162069,0.00086954836],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99984145,0.000024164381,0.000006578399,0.00005284519,0.00004795493,0.00002698821],"domain_scores_gemma":[0.9996841,0.00013746782,0.000077853605,0.000019738989,0.00006914815,0.000011728438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021146615,0.0003866955,0.00034570726,0.00074127666,0.00022371885,0.0005688144,0.00031107725,0.00042133927,0.000935696],"category_scores_gemma":[0.00061420666,0.00021058228,0.0002395932,0.00046639214,0.00026746598,0.00059434597,0.0002409767,0.00023907085,0.0002272608],"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":[0.00065726525,0.0005137006,0.10421269,0.00026338315,0.00009640216,0.0004290218,0.0004099173,0.29906932,0.2912878,0.0010202768,0.0010361422,0.3010041],"study_design_scores_gemma":[0.000008663006,0.00014189552,0.0405909,0.000011287038,0.000030307045,0.00007173483,0.00019088447,0.85617757,0.10161662,0.00055864715,0.0005837053,0.000017742886],"about_ca_topic_score_codex":0.004328895,"about_ca_topic_score_gemma":0.006892627,"teacher_disagreement_score":0.004328895,"about_ca_system_score_codex":0.00039588643,"about_ca_system_score_gemma":0.00029426167,"threshold_uncertainty_score":0.008607447},"labels":[],"label_agreement":null},{"id":"W4392187386","doi":"10.2523/iptc-24574-ms","title":"Development of a Mechanistic and Data-Driven Model for Multiphase Flow Leak Detection in Pipeline","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"","keywords":"Pipeline (software); Computer science; Leak; Leak detection; Data-flow analysis; Data modeling; Multiphase flow; Pipeline transport; Data flow diagram; Engineering; Software engineering; Database; Operating system; Mechanics","score_opus":0.03611230844164974,"score_gpt":0.2543751192380319,"score_spread":0.21826281079638216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392187386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09923969,0.00012325139,0.89459497,0.0003128887,0.000036461053,0.00023098444,0.00044024218,0.00082868565,0.0041928277],"genre_scores_gemma":[0.9372517,0.00014042117,0.05689521,0.00007978966,0.0000137767365,0.0007566631,0.00032888877,0.000116568895,0.004416981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997372,0.00005492639,0.000017500215,0.00007150033,0.00008063023,0.00003816443],"domain_scores_gemma":[0.9990182,0.00056401605,0.00012391347,0.00004291661,0.00021778647,0.000033246637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011193197,0.0007680279,0.00086502807,0.0005589616,0.00054974115,0.0011018866,0.0017176111,0.002041114,0.002099939],"category_scores_gemma":[0.0022073132,0.0007304679,0.0011630673,0.0003735801,0.000708145,0.0009037662,0.00086938194,0.0013643418,0.0003377831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010659668,0.000017689534,0.00034229277,0.000019180943,0.000005626955,0.000026764075,0.00001870648,0.99664307,0.0011772651,0.00062498474,0.0000460051,0.0010676271],"study_design_scores_gemma":[0.0000017655647,0.000003911663,0.00002993971,9.693641e-7,9.903164e-7,0.0000016520368,0.0000013373409,0.99965084,0.00016307594,0.000107064436,0.000036981466,0.0000015615236],"about_ca_topic_score_codex":0.016192757,"about_ca_topic_score_gemma":0.0067937663,"teacher_disagreement_score":0.016192757,"about_ca_system_score_codex":0.0014037484,"about_ca_system_score_gemma":0.0018460635,"threshold_uncertainty_score":0.032197},"labels":[],"label_agreement":null},{"id":"W4392291599","doi":"10.18280/ijdne.190113","title":"Evaluation of Rainwater Harvesting Systems for Drinking Water Quality in Iraq","year":2024,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Water Systems and 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":"Rainwater harvesting; Environmental science; Water quality; Water resource management; Environmental engineering; Quality (philosophy); Environmental planning; Engineering; Ecology; Biology","score_opus":0.029208829264750828,"score_gpt":0.290170174311612,"score_spread":0.2609613450468612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392291599","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":[0.99555063,0.00022822326,0.0018250175,0.00007641694,0.000015633337,0.00022763788,0.0003739846,0.000027340351,0.0016751974],"genre_scores_gemma":[0.9955408,0.00039402413,0.002838269,0.000029293105,0.0000059253407,0.000083532206,0.0003378971,0.000008721581,0.0007615969],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99846363,0.00036305576,0.00009659422,0.00017525534,0.00075650733,0.0001449487],"domain_scores_gemma":[0.99861336,0.0002148849,0.00024698852,0.000066170054,0.00078091916,0.00007765838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021219107,0.00048530824,0.00053808076,0.0007512337,0.0005343233,0.0010483959,0.00064480864,0.0004347451,0.0011616218],"category_scores_gemma":[0.0019365794,0.00016294711,0.0007506855,0.0011391194,0.00040858187,0.0005582625,0.0004137671,0.0003378786,0.00030125558],"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":[0.0034139194,0.004690367,0.2662455,0.002929854,0.0005199793,0.0008573062,0.0019696616,0.055657487,0.48596606,0.0013158381,0.002386145,0.17404792],"study_design_scores_gemma":[0.00013737158,0.023233874,0.6223087,0.00019421686,0.0005350449,0.00025312204,0.0071958625,0.059870165,0.27646977,0.00058665336,0.009095498,0.00011959445],"about_ca_topic_score_codex":0.014601981,"about_ca_topic_score_gemma":0.0152875,"teacher_disagreement_score":0.014601981,"about_ca_system_score_codex":0.0017745831,"about_ca_system_score_gemma":0.0010232243,"threshold_uncertainty_score":0.029033959},"labels":[],"label_agreement":null},{"id":"W4392420693","doi":"10.4995/wdsa-ccwi2022.2022.14094","title":"The identification of variable shear strength of particles deposited on drinking water PVC pipes after the passage of a suspended particle plume in a full-scale laboratory system","year":2022,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Full scale; Plume; Particle (ecology); Shear (geology); Materials science; Variable (mathematics); Environmental science; Geotechnical engineering; Composite material; Structural engineering; Engineering; Geology; Physics; Thermodynamics","score_opus":0.003928495777976827,"score_gpt":0.16723857242171147,"score_spread":0.16331007664373465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392420693","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":[0.99902,0.0000256604,0.0006944949,0.000012351757,0.0000067556234,0.00001961181,0.00010872237,0.000024833818,0.00008763139],"genre_scores_gemma":[0.9979291,0.000057443303,0.0014813212,0.000019822624,0.0000047308763,0.00003782224,0.00015723899,0.0000075301296,0.00030487822],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997286,0.000028861456,0.00001474698,0.00009555473,0.00008271931,0.000049407616],"domain_scores_gemma":[0.99967587,0.00006847181,0.00006753212,0.00003930164,0.0000870746,0.00006169252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031752515,0.00035035392,0.00041038642,0.0002482985,0.00043019318,0.00031334237,0.00038951676,0.00046861274,0.00047695736],"category_scores_gemma":[0.00041382172,0.00019635781,0.00034501927,0.00017579233,0.00044902216,0.00031048557,0.00044702555,0.0006313948,0.0001389678],"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":[0.000175382,0.0001621791,0.006147024,0.00004593006,0.000016078131,0.00007144034,0.00017095305,0.00046369914,0.9906137,0.000026649519,0.000063286054,0.0020436929],"study_design_scores_gemma":[0.00004565398,0.005501494,0.14197317,0.000012671451,0.00008127362,0.0001337167,0.0006661494,0.0142813325,0.8363822,0.00009449184,0.0007675252,0.000060343707],"about_ca_topic_score_codex":0.0029034337,"about_ca_topic_score_gemma":0.0030868778,"teacher_disagreement_score":0.0029034337,"about_ca_system_score_codex":0.00028448267,"about_ca_system_score_gemma":0.00028554053,"threshold_uncertainty_score":0.005773127},"labels":[],"label_agreement":null},{"id":"W4392420695","doi":"10.4995/wdsa-ccwi2022.2022.14740","title":"Modelling consumers in intermittent water supplies: a comparative review of EPANET - based methods","year":2022,"lang":"en","type":"review","venue":"","topic":"Water Systems and 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":"Computer science","score_opus":0.16039152791306288,"score_gpt":0.3745912159747735,"score_spread":0.21419968806171064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392420695","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":[0.040564753,0.19654045,0.71973515,0.0034877807,0.000801905,0.0006064682,0.0020895447,0.0015693196,0.034604598],"genre_scores_gemma":[0.3012743,0.3303614,0.3561147,0.000717623,0.00061622553,0.0012041086,0.003034893,0.0008007984,0.0058759926],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996947,0.0016214609,0.0002959673,0.0003124911,0.00073410664,0.00008907966],"domain_scores_gemma":[0.9869458,0.009970326,0.00045364548,0.0005231134,0.0019623013,0.00014470807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005061672,0.0014305953,0.0011802736,0.0024624963,0.0004745742,0.001806,0.0035328835,0.0012859667,0.0032817773],"category_scores_gemma":[0.014523194,0.0007971358,0.00240741,0.0027806775,0.0006131781,0.0023387347,0.0013109036,0.0010581994,0.0006665238],"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":[0.00021881357,0.00020605014,0.010370713,0.01215489,0.0007050716,0.00020892703,0.0010573593,0.38588083,0.0009191277,0.037900284,0.0087587,0.54161924],"study_design_scores_gemma":[0.000066119224,0.00031640247,0.006472196,0.009445096,0.0009784746,0.0003893007,0.0012833625,0.76837736,0.0025846402,0.025476128,0.18433164,0.0002792435],"about_ca_topic_score_codex":0.023384746,"about_ca_topic_score_gemma":0.014342255,"teacher_disagreement_score":0.023384746,"about_ca_system_score_codex":0.0018613207,"about_ca_system_score_gemma":0.0027634152,"threshold_uncertainty_score":0.046497285},"labels":[],"label_agreement":null},{"id":"W4392420726","doi":"10.4995/wdsa-ccwi2022.2022.14040","title":"Experimental study of mixing phenomenon in water distribution networks under real-world conditions","year":2022,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Mixing (physics); Phenomenon; Distribution (mathematics); Computer science; Physics; Mathematics; Quantum mechanics; Mathematical analysis","score_opus":0.012561865257787588,"score_gpt":0.22881511864748588,"score_spread":0.2162532533896983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392420726","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":[0.99192446,0.00014549436,0.005924418,0.0000457385,0.00003111074,0.00007056028,0.00042488656,0.00011724777,0.0013161174],"genre_scores_gemma":[0.99230933,0.00017761582,0.0063637225,0.000029598428,0.000011992745,0.00008821918,0.0003980905,0.000011327636,0.0006101647],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992717,0.00008725048,0.000048575686,0.000187806,0.00029250697,0.00011209896],"domain_scores_gemma":[0.9989525,0.00031934903,0.00018566384,0.000081859966,0.00038391445,0.00007667732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000653914,0.00038854268,0.0002779724,0.0005114324,0.0006833249,0.00031185642,0.00050083664,0.00046341657,0.0012426996],"category_scores_gemma":[0.0012169094,0.00016977168,0.00019177952,0.0006102939,0.00066119945,0.00060762954,0.00048385156,0.0006188715,0.0001893762],"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":[0.00076918904,0.0016435169,0.037325542,0.0006397597,0.00006164258,0.000695397,0.0009484414,0.019538036,0.90626115,0.0010669787,0.0010642019,0.029986104],"study_design_scores_gemma":[0.00012266399,0.004562238,0.08642989,0.000057367015,0.000097373275,0.00031349523,0.0021956386,0.07683047,0.8232822,0.00065417826,0.005330667,0.00012387625],"about_ca_topic_score_codex":0.0056619323,"about_ca_topic_score_gemma":0.010157746,"teacher_disagreement_score":0.0056619323,"about_ca_system_score_codex":0.000679363,"about_ca_system_score_gemma":0.00043291572,"threshold_uncertainty_score":0.0112579465},"labels":[],"label_agreement":null},{"id":"W4392420733","doi":"10.4995/wdsa-ccwi2022.2022.14173","title":"Household Behaviour and Energy Loss in Intermittent Water Supply Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Water supply; Energy supply; Leakage (economics); Energy (signal processing); Environmental science; Environmental engineering; Computer science; Economics; Mathematics","score_opus":0.005656881539900031,"score_gpt":0.1546625335786442,"score_spread":0.14900565203874416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392420733","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":[0.9903278,0.000070581795,0.007846522,0.00011020521,0.000006749999,0.000020899228,0.00031398406,0.00006128891,0.0012419512],"genre_scores_gemma":[0.998302,0.000035943594,0.00091632345,0.000011661689,0.0000014672321,0.000013552575,0.00023375016,0.000012167988,0.00047315526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99939287,0.00024060145,0.00004069255,0.00012371963,0.00008589231,0.000116184034],"domain_scores_gemma":[0.9966338,0.0019663314,0.00060532277,0.00027233185,0.00033152095,0.0001907379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092693703,0.00034556436,0.0003176853,0.0005432069,0.00033184982,0.0008055856,0.00089878036,0.00062011194,0.0018264775],"category_scores_gemma":[0.006350546,0.00027900655,0.00035552352,0.00073565973,0.00066971,0.0017882414,0.00091211824,0.00053740415,0.00018206795],"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":[0.00029063292,0.00017025296,0.103169695,0.00009694275,0.00008071355,0.00034099448,0.00050076144,0.8787868,0.0027823332,0.0033513065,0.00090087095,0.0095286835],"study_design_scores_gemma":[0.000025706282,0.0001683325,0.048130114,0.00002768607,0.00003502258,0.00016223414,0.00078290876,0.9439943,0.0018707346,0.0036983595,0.0010701675,0.000034472137],"about_ca_topic_score_codex":0.010938453,"about_ca_topic_score_gemma":0.009523354,"teacher_disagreement_score":0.010938453,"about_ca_system_score_codex":0.0012989634,"about_ca_system_score_gemma":0.00029249588,"threshold_uncertainty_score":0.021749616},"labels":[],"label_agreement":null},{"id":"W4392420753","doi":"10.4995/wdsa-ccwi2022.2022.14755","title":"Important Factors For Water Main Break Prediction Across 13 Canadian Systems","year":2022,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.0077783917796912195,"score_gpt":0.1784603810869459,"score_spread":0.17068198930725467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392420753","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":[0.9898101,0.0002402589,0.0037934987,0.00013962951,0.000011548782,0.00008800026,0.0045337332,0.00014278188,0.0012404257],"genre_scores_gemma":[0.9890904,0.00015702071,0.0051138042,0.000017035296,0.000003215514,0.00002671793,0.0048058876,0.000022606377,0.0007633128],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99926704,0.000056404922,0.00004427784,0.00015629336,0.00032338384,0.00015249386],"domain_scores_gemma":[0.99849486,0.00034726108,0.0002099737,0.00007059216,0.00074483827,0.0001324348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077248336,0.00057847373,0.0004319732,0.0018186382,0.001217117,0.0011788405,0.0005670205,0.00038930442,0.0010522457],"category_scores_gemma":[0.0029779447,0.00028486943,0.0009590152,0.0029114916,0.0005329738,0.00051692594,0.0007367002,0.0006463955,0.00016517565],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000156771,0.00006426178,0.9148019,0.00015143352,0.00017768818,0.00016955059,0.0005915104,0.031327836,0.0024850566,0.00029955685,0.0020642888,0.047710158],"study_design_scores_gemma":[0.0000056093622,0.00003276004,0.9261274,0.000024820436,0.0000532974,0.000024878593,0.0008406637,0.069787934,0.00079746335,0.00014645336,0.0021293187,0.000029369327],"about_ca_topic_score_codex":0.9353982,"about_ca_topic_score_gemma":0.96677935,"teacher_disagreement_score":0.06460178,"about_ca_system_score_codex":0.007951035,"about_ca_system_score_gemma":0.008681126,"threshold_uncertainty_score":0.12996441},"labels":[],"label_agreement":null},{"id":"W4392841530","doi":"10.2139/ssrn.4730270","title":"Moving Horizon Estimation for Pipeline Leak Detection, Localization, and Constrained Size Estimation","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Water Systems and 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":"Estimation; Horizon; Leak detection; Pipeline (software); Moving horizon estimation; Computer science; Leak; Econometrics; Statistics; Mathematics; Environmental science; Economics; Artificial intelligence; Kalman filter; Environmental engineering","score_opus":0.0048214351130894595,"score_gpt":0.2108868863778076,"score_spread":0.20606545126471812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392841530","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051661045,0.00023964915,0.99378115,0.000078493635,0.000031508152,0.0000074783907,0.00006578929,0.00022049296,0.00040930358],"genre_scores_gemma":[0.74529564,0.00091403205,0.24437743,0.00012468632,0.00018654355,0.00012633523,0.00054567325,0.00022376304,0.008205863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999673,0.00006910769,0.000015947166,0.000106350904,0.00008808295,0.00004747361],"domain_scores_gemma":[0.99909353,0.00053110556,0.00013028482,0.000064643646,0.00014249142,0.00003802085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006024193,0.00084917207,0.0012659975,0.0006475957,0.00029029726,0.00063195167,0.0010603499,0.0009907726,0.0022348312],"category_scores_gemma":[0.0043907934,0.00055998383,0.000564816,0.0008759758,0.00055798754,0.0012527991,0.00094344706,0.001237252,0.00038327003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001927596,0.000030315034,0.00044653355,0.00016335458,0.00004791458,0.00004856661,0.000037263242,0.8651696,0.005873006,0.013373713,0.0021472727,0.112469815],"study_design_scores_gemma":[0.000003396862,0.000011949043,0.00012807288,0.0000037344,0.0000038024264,0.0000054270677,0.000002247918,0.99573344,0.00060918153,0.0032208115,0.00027234334,0.0000056100153],"about_ca_topic_score_codex":0.012724253,"about_ca_topic_score_gemma":0.006803235,"teacher_disagreement_score":0.012724253,"about_ca_system_score_codex":0.00072787906,"about_ca_system_score_gemma":0.0010805442,"threshold_uncertainty_score":0.025300384},"labels":[],"label_agreement":null},{"id":"W4392853296","doi":"10.1186/s40068-024-00334-x","title":"Analysis of factors driving water main breaks across 13 Canadian utilities","year":2024,"lang":"en","type":"article","venue":"ENVIRONMENTAL SYSTEMS RESEARCH","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Environmental science; Transport engineering; Engineering","score_opus":0.021151449714271953,"score_gpt":0.2620521399888218,"score_spread":0.24090069027454986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392853296","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":[0.9980331,0.00003684125,0.00019011667,0.000024544815,0.0000012238058,0.000021396696,0.0011616031,0.000008184239,0.00052301167],"genre_scores_gemma":[0.9979334,0.000057561505,0.0001823798,0.0000057791285,7.5384787e-7,0.00000891317,0.0011476866,0.0000045601064,0.00065896765],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988374,0.000055425902,0.000058110058,0.00019182987,0.0004936035,0.00036371022],"domain_scores_gemma":[0.99568,0.00064326794,0.00094910397,0.00014003544,0.0020701217,0.00051750516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078861974,0.00043120628,0.0003595868,0.0024154799,0.0018427005,0.0011851517,0.0010124057,0.00040794123,0.0015790955],"category_scores_gemma":[0.0037075398,0.0003864156,0.0004903614,0.0046620173,0.00085131574,0.00040405447,0.0007969418,0.00053118885,0.00016694749],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048867794,0.000025146997,0.9916226,0.000021674572,0.000037366324,0.00011557672,0.0007279583,0.0013751673,0.00066473125,0.00007792045,0.00030194604,0.0049809865],"study_design_scores_gemma":[5.465261e-7,0.000015105397,0.9976584,0.0000040356094,0.000008789248,0.000013285841,0.0010287954,0.00084469124,0.00010780859,0.0000106486505,0.00030243688,0.0000054598668],"about_ca_topic_score_codex":0.9768792,"about_ca_topic_score_gemma":0.98901945,"teacher_disagreement_score":0.02312082,"about_ca_system_score_codex":0.014984152,"about_ca_system_score_gemma":0.012014407,"threshold_uncertainty_score":0.10871816},"labels":[],"label_agreement":null},{"id":"W4392909011","doi":"10.32920/25412755","title":"Analyzing Building-level Water Consumption Patterns Before and After the Pandemic: A Campus Case","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Toronto Metropolitan University; Toronto Public Health","funders":"","keywords":"Leak detection; Leak; Water consumption; Consumption (sociology); Leakage (economics); Occupancy; Coronavirus disease 2019 (COVID-19); Environmental science; Water supply; Computer science; Key (lock); Water resource management; Civil engineering; Environmental engineering; Engineering; Computer security","score_opus":0.018747402885271706,"score_gpt":0.23729304983661018,"score_spread":0.21854564695133846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392909011","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":[0.9977538,0.000013554476,0.00075675896,0.0001559051,0.0000031156005,0.000023428256,0.00032798052,0.00002274251,0.000942672],"genre_scores_gemma":[0.9976949,0.000026162894,0.0011987446,0.000015928304,0.0000024146689,0.000012606071,0.0004222371,0.000007816577,0.0006191417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99937683,0.00017735157,0.000030542462,0.000109984496,0.00013635623,0.00016897524],"domain_scores_gemma":[0.99857044,0.00054162345,0.00017837228,0.00019193266,0.00031042646,0.0002072508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064900384,0.00028722378,0.0002848059,0.00075613626,0.0007489435,0.00082899065,0.00077247014,0.0011680663,0.0012674545],"category_scores_gemma":[0.0021636721,0.00022875477,0.00039184093,0.0013548712,0.000953277,0.0006670843,0.0008561719,0.00074374396,0.00020098843],"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":[0.0011949169,0.0017310132,0.59838575,0.00017947452,0.00023968364,0.015686817,0.0050756633,0.31573433,0.0107079195,0.007980857,0.0057646157,0.037318897],"study_design_scores_gemma":[0.00007094836,0.00065137324,0.4497202,0.000044826866,0.0000873459,0.0013263491,0.027550763,0.5036302,0.007043062,0.003461703,0.006281068,0.00013211308],"about_ca_topic_score_codex":0.097785026,"about_ca_topic_score_gemma":0.10620867,"teacher_disagreement_score":0.097785026,"about_ca_system_score_codex":0.0021273599,"about_ca_system_score_gemma":0.00089198985,"threshold_uncertainty_score":0.19443172},"labels":[],"label_agreement":null},{"id":"W4392909017","doi":"10.32920/25412755.v1","title":"Analyzing Building-level Water Consumption Patterns Before and After the Pandemic: A Campus Case","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Toronto Metropolitan University; Toronto Public Health","funders":"","keywords":"Leak; Leak detection; Water consumption; Consumption (sociology); Leakage (economics); Occupancy; Coronavirus disease 2019 (COVID-19); Water supply; Computer science; Key (lock); Environmental science; Water resource management; Civil engineering; Engineering; Computer security; Environmental engineering","score_opus":0.018747402885271706,"score_gpt":0.23729304983661018,"score_spread":0.21854564695133846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392909017","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":[0.9977538,0.000013554476,0.00075675896,0.0001559051,0.0000031156005,0.000023428256,0.00032798052,0.00002274251,0.000942672],"genre_scores_gemma":[0.9976949,0.000026162894,0.0011987446,0.000015928304,0.0000024146689,0.000012606071,0.0004222371,0.000007816577,0.0006191417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99937683,0.00017735157,0.000030542462,0.000109984496,0.00013635623,0.00016897524],"domain_scores_gemma":[0.99857044,0.00054162345,0.00017837228,0.00019193266,0.00031042646,0.0002072508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064900384,0.00028722378,0.0002848059,0.00075613626,0.0007489435,0.00082899065,0.00077247014,0.0011680663,0.0012674545],"category_scores_gemma":[0.0021636721,0.00022875477,0.00039184093,0.0013548712,0.000953277,0.0006670843,0.0008561719,0.00074374396,0.00020098843],"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":[0.0011949169,0.0017310132,0.59838575,0.00017947452,0.00023968364,0.015686817,0.0050756633,0.31573433,0.0107079195,0.007980857,0.0057646157,0.037318897],"study_design_scores_gemma":[0.00007094836,0.00065137324,0.4497202,0.000044826866,0.0000873459,0.0013263491,0.027550763,0.5036302,0.007043062,0.003461703,0.006281068,0.00013211308],"about_ca_topic_score_codex":0.097785026,"about_ca_topic_score_gemma":0.10620867,"teacher_disagreement_score":0.097785026,"about_ca_system_score_codex":0.0021273599,"about_ca_system_score_gemma":0.00089198985,"threshold_uncertainty_score":0.19443172},"labels":[],"label_agreement":null},{"id":"W4393220024","doi":"10.2166/hydro.2024.299","title":"Detecting and locating chemical intrusion in water distribution systems using 911 calls","year":2024,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"National Research Council Canada","funders":"Health Canada","keywords":"Intrusion; Convolutional neural network; Event (particle physics); Computer science; Intrusion detection system; Warning system; Point (geometry); Function (biology); Artificial neural network; Computer security; Artificial intelligence; Data mining; Geology; Telecommunications; Mathematics; Biology","score_opus":0.006770629232566795,"score_gpt":0.1949111087882795,"score_spread":0.1881404795557127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393220024","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":[0.95461386,0.00007726437,0.04187183,0.00014778016,0.000028993873,0.000038052603,0.00047054188,0.00054825726,0.0022034126],"genre_scores_gemma":[0.99421096,0.000023605387,0.0050719255,0.000007989999,0.0000029823946,0.0000063909692,0.00022125265,0.0000060672355,0.00044884506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987245,0.000016924318,0.000007828043,0.000042764266,0.0000316816,0.000028305623],"domain_scores_gemma":[0.9997756,0.00009901105,0.000039305003,0.000019108944,0.000053077318,0.000013931132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001608825,0.0004299023,0.00023408135,0.0005349643,0.00019128206,0.00040748922,0.00032976957,0.00047173744,0.0008764012],"category_scores_gemma":[0.00082381855,0.00016927463,0.00025882586,0.00042304993,0.00020628692,0.00043004128,0.00029374677,0.000253408,0.00019238867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030813715,0.00015184547,0.051728986,0.00010691563,0.00004949066,0.00030090776,0.00010373781,0.8218407,0.01906507,0.0004883342,0.00089267426,0.10496313],"study_design_scores_gemma":[0.0000019458894,0.000025848183,0.0072187134,0.000002385484,0.000004506866,0.00001546815,0.000029110131,0.98949033,0.0029215862,0.0001461839,0.00013916995,0.000004699701],"about_ca_topic_score_codex":0.01887846,"about_ca_topic_score_gemma":0.016541312,"teacher_disagreement_score":0.01887846,"about_ca_system_score_codex":0.0006876977,"about_ca_system_score_gemma":0.00041551617,"threshold_uncertainty_score":0.037537158},"labels":[],"label_agreement":null},{"id":"W4394361452","doi":"10.6084/m9.figshare.19246273","title":"A comparative study of water quality using two quality indices and a risk index in a drinking water distribution network","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Water Systems and 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":"Index (typography); Water quality; Quality (philosophy); Environmental science; Distribution (mathematics); Statistics; Mathematics; Computer science; World Wide Web; Biology; Ecology","score_opus":0.06703949748004201,"score_gpt":0.3190157552632573,"score_spread":0.2519762577832153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394361452","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99606544,0.0001427783,0.0019148716,0.000035174544,0.0000034713942,0.000028911725,0.00016540223,0.000019536774,0.0016243354],"genre_scores_gemma":[0.9983115,0.00008735373,0.001054885,0.0000045944876,0.0000033080737,0.0000135762875,0.0002453328,0.0000034128943,0.00027615094],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99788123,0.0006309245,0.00017784708,0.00030896306,0.0008399528,0.00016101827],"domain_scores_gemma":[0.99598444,0.0011336642,0.0010807317,0.00022168366,0.0013699238,0.00020959844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026064657,0.00026705675,0.0003908534,0.0039141206,0.0003184437,0.0012979772,0.00031197837,0.0003307182,0.000570513],"category_scores_gemma":[0.0067148237,0.000121118675,0.0004208347,0.0042959903,0.000358752,0.0013725125,0.0006748044,0.00021353504,0.000085463944],"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":[0.00038836425,0.00016940686,0.9547568,0.00008243855,0.00016258872,0.00019871794,0.00081273663,0.00549315,0.0021913492,0.00039918875,0.00021258536,0.035132665],"study_design_scores_gemma":[0.000011651181,0.0005641157,0.9747902,0.00001954586,0.000082893224,0.00014227627,0.002293629,0.019307084,0.0013124403,0.00026243788,0.0011888688,0.000024785904],"about_ca_topic_score_codex":0.020489018,"about_ca_topic_score_gemma":0.016634818,"teacher_disagreement_score":0.020489018,"about_ca_system_score_codex":0.0020236347,"about_ca_system_score_gemma":0.00059686473,"threshold_uncertainty_score":0.040739477},"labels":[],"label_agreement":null},{"id":"W4394782994","doi":"10.1145/3639592.3639612","title":"Leak Detection for Large Complex Gas Pipelines by Discrepancy Analysis of Simulated-Measured Pressure Profiles","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Pipeline transport; Pipeline (software); Leak; Computer science; Transient (computer programming); Leak detection; Instrumentation (computer programming); Real-time computing; Natural gas; Virtual instrumentation; Simulation; Data acquisition; Engineering; Mechanical engineering","score_opus":0.016522845404958623,"score_gpt":0.2380531077907045,"score_spread":0.22153026238574586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394782994","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":[0.51795113,0.00010358453,0.4770776,0.00014443138,0.000025058853,0.000054550135,0.00021545409,0.0032215936,0.0012066928],"genre_scores_gemma":[0.957273,0.000035217003,0.042237483,0.000013164679,0.0000030179074,0.00002681088,0.0001349078,0.00007359866,0.00020288689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994584,0.00011290221,0.0000368767,0.0001246445,0.0002257619,0.000041405514],"domain_scores_gemma":[0.9987889,0.000508415,0.00029991748,0.00016821134,0.00019135664,0.00004323718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008868179,0.00047846415,0.00041658187,0.0009695127,0.00020290607,0.00056946586,0.00056605204,0.00048371346,0.0006064314],"category_scores_gemma":[0.003047244,0.00025055773,0.000399121,0.0006547119,0.0003966149,0.0009744634,0.0007857206,0.00060175476,0.0001542545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011144025,0.0004257204,0.08418622,0.0005246238,0.00015077167,0.0008565649,0.0011634987,0.5463886,0.18908176,0.00438326,0.0016755507,0.17004909],"study_design_scores_gemma":[0.000009572315,0.000081335274,0.004346064,0.0000072899325,0.000008474796,0.00006927916,0.000058499918,0.97856516,0.016186547,0.00035497817,0.00029312147,0.000019672254],"about_ca_topic_score_codex":0.002295864,"about_ca_topic_score_gemma":0.0017029608,"teacher_disagreement_score":0.002295864,"about_ca_system_score_codex":0.0004997565,"about_ca_system_score_gemma":0.0006368734,"threshold_uncertainty_score":0.0046899915},"labels":[],"label_agreement":null},{"id":"W4394854276","doi":"10.1038/s41598-024-59598-z","title":"A novel robust evaluation approach to improve systematic behavior of failure safety in water supply system under various ellipsoid uncertainties","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Water Systems and 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":"Institute on Governance","funders":"Ministry of Science and Technology of the People's Republic of China; Chengdu University; Chengdu University of Information Technology","keywords":"Ellipsoid; Computer science; Water supply; Environmental science; Geology; Geodesy","score_opus":0.015520793392591636,"score_gpt":0.2115142410794657,"score_spread":0.19599344768687407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394854276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02132486,0.00014413889,0.97660875,0.000086359905,0.000010187204,0.000034967743,0.000025187761,0.000088394045,0.0016772244],"genre_scores_gemma":[0.91171306,0.00019271497,0.086665876,0.000048503618,0.000023395216,0.00011674626,0.0000750187,0.000049516384,0.0011151775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985696,0.00059219473,0.000068797315,0.00021740692,0.0004064211,0.00014553645],"domain_scores_gemma":[0.998131,0.00093122124,0.00027420826,0.00009301348,0.00051053456,0.00005999952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003253965,0.0011129052,0.0009884344,0.001196493,0.0003740546,0.0011100961,0.0010325724,0.000762437,0.0014878474],"category_scores_gemma":[0.005486969,0.00047337802,0.0009671237,0.0005586551,0.00063899165,0.0014357496,0.0012674809,0.0007691852,0.00010450989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002294948,0.000014504581,0.00036771825,0.000029197048,0.000029605177,0.000025690142,0.000022040938,0.9842858,0.000856339,0.005711532,0.0001224608,0.008512317],"study_design_scores_gemma":[0.0000020519797,0.000017483944,0.000066228386,0.0000028553163,0.000004880184,0.0000037164705,0.000003917111,0.99868196,0.00020873413,0.00094725314,0.000057816742,0.0000030454978],"about_ca_topic_score_codex":0.0071583814,"about_ca_topic_score_gemma":0.0033458602,"teacher_disagreement_score":0.0071583814,"about_ca_system_score_codex":0.0013319313,"about_ca_system_score_gemma":0.0015412237,"threshold_uncertainty_score":0.017208815},"labels":[],"label_agreement":null},{"id":"W4395042031","doi":"10.21203/rs.3.rs-4259384/v1","title":"Evaluating ATP testing for distribution system monitoring: Comparison to HPC, impact of chlorine quenching, and hold time dependency","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Dependency (UML); Chlorine; Quenching (fluorescence); Chemistry; Distribution (mathematics); Computer science; Environmental science; Nuclear engineering; Engineering; Mathematics; Physics; Software engineering; Fluorescence; Organic chemistry","score_opus":0.15041048608436047,"score_gpt":0.4475580097421296,"score_spread":0.29714752365776914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395042031","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":[0.9473829,0.00042798472,0.047553767,0.0001851635,0.000039985192,0.000050084458,0.00045155978,0.0012597423,0.0026488148],"genre_scores_gemma":[0.9944411,0.000039916813,0.004797069,0.000016397582,0.0000053266644,0.000009410226,0.00014532586,0.000040989406,0.00050437666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986539,0.000562095,0.000057739355,0.00015761062,0.00041598748,0.00015268949],"domain_scores_gemma":[0.9849266,0.011858764,0.0005244353,0.0009040771,0.0015206324,0.00026551148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018546422,0.00081420556,0.0005230099,0.0007838155,0.00025679384,0.0005864294,0.0010629973,0.00069178874,0.0031554878],"category_scores_gemma":[0.007942745,0.00019716001,0.00041021354,0.0006326531,0.00047015928,0.0014463457,0.00047819698,0.00047343445,0.0002972273],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008179163,0.0009264127,0.049162965,0.0007306375,0.0002045645,0.00027045954,0.00016139515,0.5870049,0.07537273,0.0022499007,0.0020830426,0.2736538],"study_design_scores_gemma":[0.00014079952,0.002413174,0.013675179,0.000018175262,0.00011939373,0.00012735178,0.00012726351,0.90961915,0.07154334,0.0014048016,0.00078525755,0.000026198473],"about_ca_topic_score_codex":0.009754456,"about_ca_topic_score_gemma":0.007955929,"teacher_disagreement_score":0.009754456,"about_ca_system_score_codex":0.00077389245,"about_ca_system_score_gemma":0.0009458375,"threshold_uncertainty_score":0.019395351},"labels":[],"label_agreement":null},{"id":"W4395450176","doi":"10.2166/aqua.2024.125","title":"Contaminations in water distribution systems: a critical review of detection and response methods","year":2024,"lang":"en","type":"review","venue":"AQUA - Water Infrastructure Ecosystems and Society","topic":"Water Systems and 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":"Polytechnique Montréal","funders":"","keywords":"Distribution (mathematics); Computer science; Mathematics","score_opus":0.014197556238941304,"score_gpt":0.2998589417610453,"score_spread":0.28566138552210396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395450176","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":[0.00015680329,0.9976888,0.00096139766,0.00031995043,0.00020132137,0.0000125667075,0.000017306551,0.000006793242,0.0006350788],"genre_scores_gemma":[0.0019312027,0.9963509,0.0010947031,0.00015040746,0.00021604994,0.000016022284,0.000023613235,0.000004300208,0.00021278662],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979632,0.00052677176,0.0003906296,0.00030997596,0.0007073463,0.000102030965],"domain_scores_gemma":[0.99030113,0.0068365866,0.0006263161,0.00020327525,0.0019161425,0.00011660888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042348257,0.001527341,0.0020625002,0.0060993135,0.0005373178,0.0021018132,0.0018692092,0.001988427,0.003709774],"category_scores_gemma":[0.008390196,0.00086351443,0.0016267891,0.006442823,0.0010783894,0.003208413,0.0009825418,0.0016622862,0.0011279645],"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":[0.00007875885,0.000118070915,0.0006505319,0.06318122,0.00023399122,0.00017992526,0.00021778932,0.0017090455,0.00088119926,0.011132813,0.014879418,0.90673727],"study_design_scores_gemma":[0.000018190934,0.00031593535,0.0026686834,0.05034021,0.00082529965,0.0011946016,0.00044902932,0.0015498672,0.0019781264,0.008259599,0.932274,0.00012644906],"about_ca_topic_score_codex":0.0042330422,"about_ca_topic_score_gemma":0.0034318585,"teacher_disagreement_score":0.0060993135,"about_ca_system_score_codex":0.001625305,"about_ca_system_score_gemma":0.0034183722,"threshold_uncertainty_score":0.022396147},"labels":[],"label_agreement":null},{"id":"W4396681160","doi":"10.14796/jwmm.c515","title":"Mathematical Program with Vanishing Constraints for Optimal Pressure Control in Water Distribution Systems","year":2024,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Distribution (mathematics); Control (management); Mathematical optimization; Computer science; Mathematics; Mathematical analysis; Artificial intelligence","score_opus":0.008599773976467724,"score_gpt":0.20847313214760854,"score_spread":0.19987335817114082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396681160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010705446,0.0007823414,0.9753642,0.00062297063,0.00007938272,0.00009061443,0.0001654369,0.00012832612,0.012061374],"genre_scores_gemma":[0.70674914,0.0025517272,0.26763293,0.00047914044,0.00021470358,0.0011938635,0.0005926782,0.00026953165,0.020316206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935323,0.00026877568,0.000026880323,0.00009811001,0.00017109564,0.000081875],"domain_scores_gemma":[0.9987496,0.00090992125,0.00009380378,0.000028097716,0.00018134374,0.00003716058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001964395,0.0013759467,0.0013354148,0.00081025454,0.00057251,0.001803387,0.00079500343,0.0014614286,0.0042121364],"category_scores_gemma":[0.003924788,0.000645605,0.0012697388,0.0010434109,0.001197935,0.0013784439,0.0011996226,0.0023601355,0.00041634977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001675231,0.000032168628,0.00018914611,0.000112233065,0.000018402166,0.000059841088,0.000052815813,0.9464377,0.0004320227,0.04606615,0.00091410277,0.0056686937],"study_design_scores_gemma":[0.0000052181085,0.000010976303,0.00003561006,0.000008669608,0.000004122852,0.000004356867,0.000009386829,0.9929832,0.000086161104,0.006340538,0.0005082192,0.0000034807267],"about_ca_topic_score_codex":0.011634589,"about_ca_topic_score_gemma":0.006533738,"teacher_disagreement_score":0.011634589,"about_ca_system_score_codex":0.0014304087,"about_ca_system_score_gemma":0.0025496776,"threshold_uncertainty_score":0.023133755},"labels":[],"label_agreement":null},{"id":"W4396763185","doi":"10.1002/cjce.25303","title":"Leakage characteristics of plug flow in the case of pipe leakage","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and 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":"Natural Science Foundation of Henan Province; Henan University; Zhengzhou University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Leakage (economics); Spark plug; Plug flow; Leak; Petroleum engineering; Environmental science; Mechanics; Materials science; Engineering; Mechanical engineering; Physics; Environmental engineering","score_opus":0.005994933362916615,"score_gpt":0.1685014410004568,"score_spread":0.16250650763754018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396763185","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":[0.9923036,0.00014938632,0.0058798874,0.000039904702,0.0000137209045,0.000014961523,0.0001478546,0.00042552064,0.0010250725],"genre_scores_gemma":[0.9995976,0.000014483009,0.00014681295,0.000002865893,0.0000012755986,0.000002916006,0.000055572447,0.0000064789815,0.00017205208],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977785,0.000043854146,0.00001556406,0.000037079757,0.00006485075,0.00006079024],"domain_scores_gemma":[0.99837023,0.0009299919,0.00021569831,0.000092251044,0.00029110597,0.00010078713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043141842,0.00035321445,0.0004531021,0.0009876465,0.00027244122,0.0005585486,0.0003376453,0.0006613573,0.0018675518],"category_scores_gemma":[0.0016362547,0.00021110858,0.00038563836,0.0005351147,0.0005649819,0.00073731726,0.00030871545,0.0004782667,0.00027804635],"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":[0.008589562,0.0007809743,0.13498883,0.0006273381,0.00021406135,0.007994442,0.0018875119,0.320042,0.45015946,0.0020882434,0.002230828,0.07039681],"study_design_scores_gemma":[0.00009751218,0.0023827003,0.12114387,0.00007093404,0.000094323674,0.0014465273,0.0007081019,0.7119593,0.15950628,0.00063152466,0.0017985807,0.00016032178],"about_ca_topic_score_codex":0.0023567542,"about_ca_topic_score_gemma":0.0006242563,"teacher_disagreement_score":0.0023567542,"about_ca_system_score_codex":0.00036650815,"about_ca_system_score_gemma":0.0002230524,"threshold_uncertainty_score":0.0062476397},"labels":[],"label_agreement":null},{"id":"W4396835196","doi":"10.1016/j.scitotenv.2024.173051","title":"Enhancing reclaimed water distribution network resilience with cost-effective meshing","year":2024,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Water Systems and 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":"Ministerio de Industria, Energía y Turismo; Generalitat de Catalunya; China Scholarship Council; Universitat de Girona; Canadian Institute for Advanced Research","keywords":"Resilience (materials science); Network planning and design; Computer science; Baseline (sea); Water supply; Network performance; Risk analysis (engineering); Reliability engineering; Environmental science; Engineering; Environmental engineering; Computer network; Business","score_opus":0.003585639098767399,"score_gpt":0.17160525965195,"score_spread":0.1680196205531826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396835196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13995242,0.00031102166,0.85286295,0.0003092888,0.000075052,0.000071128314,0.000078044104,0.0006270907,0.005712986],"genre_scores_gemma":[0.9016618,0.00018359073,0.09675507,0.000049210794,0.0000132464465,0.000049857874,0.000053809876,0.00005030549,0.0011831052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997272,0.000084019506,0.000014654652,0.000048935894,0.00006820615,0.000057023928],"domain_scores_gemma":[0.9992112,0.00033976688,0.00015160994,0.00012626103,0.00012491841,0.000046207253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070115033,0.00061990926,0.00039126165,0.0006733682,0.0002978051,0.00046192345,0.0008705076,0.00048700595,0.0016604293],"category_scores_gemma":[0.0021008654,0.00020777728,0.00046870747,0.0003291188,0.0004357508,0.0012041121,0.0010129126,0.00039462958,0.00017984904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067875946,0.00003471545,0.0005940297,0.00006898575,0.000015986148,0.000069644935,0.00004029771,0.93003285,0.013913699,0.006163407,0.00041417018,0.048584405],"study_design_scores_gemma":[0.000011261097,0.000120309705,0.00024294356,0.0000114879,0.000020219237,0.000035238867,0.000031488096,0.9888965,0.004332943,0.0048300927,0.001459265,0.000008302778],"about_ca_topic_score_codex":0.0019019897,"about_ca_topic_score_gemma":0.002385854,"teacher_disagreement_score":0.0019019897,"about_ca_system_score_codex":0.0008732895,"about_ca_system_score_gemma":0.00052699546,"threshold_uncertainty_score":0.0063361526},"labels":[],"label_agreement":null},{"id":"W4396976061","doi":"10.1061/9780784485477.119","title":"Framework for Predicting Water Main Breaks in the Face of Climate Change","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Concordia University; National Research Council Canada","funders":"","keywords":"Face (sociological concept); Climate change; Computer science; Environmental science; Climatology; Geology; Oceanography","score_opus":0.019083851958307453,"score_gpt":0.23619292773544556,"score_spread":0.21710907577713812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396976061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21433006,0.0013095053,0.7722278,0.0012802434,0.00021251224,0.0001234937,0.0017698304,0.0020574275,0.00668922],"genre_scores_gemma":[0.95635295,0.00030610088,0.03918141,0.0001454633,0.000068376394,0.00013369574,0.0010620924,0.00004703826,0.0027028292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999838,0.00003211138,0.000010167667,0.00004919898,0.000030481322,0.000040081042],"domain_scores_gemma":[0.99958533,0.00018832774,0.000059840244,0.000016376678,0.00012005676,0.00003006358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066565786,0.00081449974,0.0005941692,0.0005895652,0.00034730026,0.00087848265,0.0011869803,0.001014526,0.0019406213],"category_scores_gemma":[0.0013185561,0.0003927615,0.0006326531,0.00049039134,0.00037588752,0.0006397917,0.000859059,0.0011751155,0.00027718558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025468564,0.000024748126,0.0012960928,0.000014460243,0.000026791304,0.000030220825,0.000008757658,0.98911846,0.00023954624,0.0007034867,0.00043982972,0.008072178],"study_design_scores_gemma":[0.0000018935955,0.0000046705104,0.00015810104,0.000001860345,0.0000027046444,0.000002345756,0.0000025337513,0.9992939,0.000044211556,0.00042704592,0.00005930848,0.0000013740288],"about_ca_topic_score_codex":0.06181707,"about_ca_topic_score_gemma":0.05273934,"teacher_disagreement_score":0.06181707,"about_ca_system_score_codex":0.001105643,"about_ca_system_score_gemma":0.0018517148,"threshold_uncertainty_score":0.12291455},"labels":[],"label_agreement":null},{"id":"W4396976258","doi":"10.1061/9780784485477.122","title":"Robust Booster Disinfection Scheduling Using Incomplete Mixing Water Quality Model (EPANET-IMX)","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Booster (rocketry); Scheduling (production processes); Water quality; Computer science; Mathematical optimization; Mathematics; Engineering; Aerospace engineering","score_opus":0.07294807031645156,"score_gpt":0.256281676633603,"score_spread":0.18333360631715145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396976258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31823763,0.00051626394,0.6539949,0.0005448073,0.000095320436,0.00025703624,0.00079230143,0.00074112945,0.024820495],"genre_scores_gemma":[0.96108395,0.00016109466,0.034763925,0.000053183652,0.000008520997,0.000146208,0.00026096127,0.00003052106,0.0034916336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997863,0.000051142153,0.000008066222,0.000047194044,0.000062681174,0.000044570494],"domain_scores_gemma":[0.9996655,0.00014615108,0.000073325085,0.000021374743,0.00006842449,0.00002530193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000661676,0.0006475726,0.00069444807,0.00028720874,0.00031443406,0.00072268245,0.00073477905,0.00060598383,0.0017097043],"category_scores_gemma":[0.0009406073,0.0003449362,0.0006840683,0.00023777579,0.00039107856,0.0005281541,0.000576201,0.0007822705,0.00014338491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017274155,0.000007753735,0.00016566046,0.00001061743,0.0000032015896,0.000010509358,0.000004794792,0.99759966,0.00034680468,0.00068797806,0.00006628849,0.0010794535],"study_design_scores_gemma":[0.00000376079,0.000016237667,0.00006887783,0.0000012041984,0.000002400091,0.0000016575756,0.0000022146887,0.99932647,0.00024136498,0.00020619816,0.00012812037,0.000001526966],"about_ca_topic_score_codex":0.018466417,"about_ca_topic_score_gemma":0.010163799,"teacher_disagreement_score":0.018466417,"about_ca_system_score_codex":0.001052372,"about_ca_system_score_gemma":0.0017949842,"threshold_uncertainty_score":0.036717832},"labels":[],"label_agreement":null},{"id":"W4398190349","doi":"10.1061/jpsea2.pseng-1597","title":"Economics of Inspection and Condition Assessment of High-Consequence Water Pipeline and Assessing Its Remaining Life","year":2024,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and 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":"Thornhill Medical (Canada); Inro Consultants (Canada)","funders":"","keywords":"Pipeline (software); Forensic engineering; Petroleum engineering; Engineering; Pipeline transport; Environmental science; Civil engineering; Construction engineering; Reliability engineering; Computer science; Environmental engineering; Mechanical engineering","score_opus":0.013442622861661624,"score_gpt":0.2602914752070361,"score_spread":0.2468488523453745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398190349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20553322,0.0008716171,0.7372737,0.003066931,0.00009570117,0.00083421473,0.0014622331,0.00021452967,0.05064785],"genre_scores_gemma":[0.91847783,0.00052491203,0.07586252,0.00009764136,0.000034478864,0.00026543977,0.00026358993,0.000044396,0.004429123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960147,0.0021776846,0.00012606362,0.00035319966,0.0011007012,0.00022765432],"domain_scores_gemma":[0.9918522,0.0060596447,0.00083575095,0.00034252656,0.0006976764,0.00021219182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00705035,0.00086851424,0.00059290824,0.0021335466,0.0004937452,0.002892432,0.0017034253,0.0014406067,0.00475592],"category_scores_gemma":[0.01265449,0.000795022,0.0010051285,0.0015656174,0.0014183694,0.0032410028,0.0013875167,0.0013407953,0.00025742874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021090904,0.000111471396,0.007930491,0.00017536209,0.00008007389,0.00036782527,0.00010494121,0.8444329,0.0023664213,0.11052885,0.0009670243,0.032723825],"study_design_scores_gemma":[0.000020290194,0.0002585939,0.008799101,0.000074117655,0.00006334253,0.0002479757,0.00025655705,0.9304599,0.0017050136,0.05556669,0.0024835959,0.000064857275],"about_ca_topic_score_codex":0.00968185,"about_ca_topic_score_gemma":0.0099080745,"teacher_disagreement_score":0.00968185,"about_ca_system_score_codex":0.0055628726,"about_ca_system_score_gemma":0.0038794335,"threshold_uncertainty_score":0.040361643},"labels":[],"label_agreement":null},{"id":"W4398243568","doi":"10.1007/s11269-024-03892-y","title":"Impact of Pressure on the Deterioration of Drinking Water Distribution Networks","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Hydrogeology; Environmental science; Distribution (mathematics); Water resource management; Water pressure; Hydrology (agriculture); Environmental engineering; Geology; Geotechnical engineering; Mathematics","score_opus":0.006208640020536222,"score_gpt":0.1923820423803597,"score_spread":0.18617340235982346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398243568","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":[0.98490256,0.00019435398,0.0012366739,0.0012460173,0.000075757736,0.000011883878,0.00038552654,0.000044398275,0.011902878],"genre_scores_gemma":[0.99934846,0.00008240744,0.00006577818,0.000017675127,0.000011396638,0.0000021694848,0.000053722026,0.000005144829,0.0004132587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994067,0.00018839972,0.000014564282,0.000041414114,0.0001079549,0.00024101541],"domain_scores_gemma":[0.996865,0.0018790113,0.0002472842,0.000083715226,0.0005825537,0.00034245633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007801485,0.0002677617,0.0003580558,0.0004550061,0.000592527,0.0012862699,0.00042751592,0.00082490686,0.004898681],"category_scores_gemma":[0.0065413225,0.00013339864,0.00034186803,0.0006187647,0.0006549297,0.0011028538,0.0006965804,0.000828093,0.00030555832],"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":[0.0024968307,0.0005789556,0.10469696,0.0002972849,0.0001771142,0.0036821212,0.00057802827,0.7572839,0.016664192,0.022257045,0.016194688,0.07509282],"study_design_scores_gemma":[0.00011286012,0.001462375,0.20848563,0.00008181031,0.00017832856,0.00071444234,0.0039617424,0.74546146,0.013388151,0.016728248,0.00932726,0.00009767928],"about_ca_topic_score_codex":0.013311049,"about_ca_topic_score_gemma":0.006743939,"teacher_disagreement_score":0.013311049,"about_ca_system_score_codex":0.0012275828,"about_ca_system_score_gemma":0.0006777666,"threshold_uncertainty_score":0.026467144},"labels":[],"label_agreement":null},{"id":"W4399019493","doi":"10.1080/10406026.2024.2356096","title":"Energy-Efficient Joint Scheduling of Pumps and Valves in Water Distribution Network Using Hybrid Optimization Algorithm","year":2023,"lang":"en","type":"article","venue":"Environmental Claims Journal","topic":"Water Systems and 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":"The Interdisciplinary Centre for the Development of Ocean Mapping","funders":"","keywords":"Computer science; Mathematical optimization; Scheduling (production processes); Algorithm; Optimization algorithm; Energy (signal processing); Mathematics","score_opus":0.008172364803530046,"score_gpt":0.17320651724745845,"score_spread":0.1650341524439284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399019493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055431094,0.0015353645,0.9308281,0.0006137166,0.00013513457,0.00015610392,0.00021509021,0.0004515509,0.010633882],"genre_scores_gemma":[0.80318844,0.00075810566,0.18798275,0.00023522181,0.000070874215,0.00030384917,0.0003821316,0.00015576716,0.0069228527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957293,0.00013963,0.000018477738,0.000091159454,0.00006158371,0.000116255185],"domain_scores_gemma":[0.9992393,0.0005191804,0.00006964991,0.000022161148,0.00008561776,0.000064031425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008197,0.0011116995,0.0014869424,0.0006357863,0.00046392396,0.0015066592,0.0013173987,0.0010717609,0.00400751],"category_scores_gemma":[0.0015629848,0.0006111402,0.0008214153,0.0008623871,0.00057442253,0.0009551584,0.00087811693,0.00094189285,0.0003212613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032490592,0.000020778356,0.00022008682,0.000037907903,0.000019926894,0.000023130906,0.000014571683,0.9900198,0.00018386908,0.0017157233,0.0004785231,0.0072331983],"study_design_scores_gemma":[0.0000074805694,0.00001361446,0.000047503094,0.0000039699157,0.000005071966,0.000003815996,0.000008228595,0.99863154,0.000050529707,0.001023491,0.00020298209,0.0000017625587],"about_ca_topic_score_codex":0.011206316,"about_ca_topic_score_gemma":0.009274297,"teacher_disagreement_score":0.011206316,"about_ca_system_score_codex":0.0012773145,"about_ca_system_score_gemma":0.0018505766,"threshold_uncertainty_score":0.022282183},"labels":[],"label_agreement":null},{"id":"W4399047867","doi":"10.1007/978-3-031-46590-1_3","title":"CO2 Transportation Facilities: Economic Optimization Using Genetic Algorithm","year":2024,"lang":"en","type":"book-chapter","venue":"Green energy and technology","topic":"Water Systems and 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; University of Alberta","funders":"","keywords":"Genetic algorithm; Computer science; Mathematical optimization; Mathematics; Machine learning","score_opus":0.006293365716977285,"score_gpt":0.16188524709738542,"score_spread":0.15559188138040814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399047867","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":[0.023037862,0.0052132844,0.84685606,0.0007898761,0.00039082707,0.00012189423,0.00037893213,0.00051213923,0.12269913],"genre_scores_gemma":[0.54683226,0.007277837,0.34332532,0.0002647958,0.0002580739,0.0003763044,0.0005485373,0.0006123923,0.10050443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984944,0.00004973906,0.000003227537,0.000020542933,0.00005648852,0.00002056432],"domain_scores_gemma":[0.99990785,0.000055883957,0.0000074151612,0.000007307416,0.000017435714,0.000004144142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000309302,0.00097363,0.0007851001,0.0005783596,0.0003929094,0.0013146914,0.0010670171,0.0017347647,0.005690546],"category_scores_gemma":[0.0006210663,0.00045986514,0.0007387256,0.0031304297,0.00045576185,0.00093150145,0.0005329588,0.0010296244,0.0008522836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016043647,0.000016738117,0.000052836527,0.000033653134,0.000015610212,0.000017276803,0.0000065436543,0.96484417,0.00032150763,0.010901558,0.0025660293,0.021208111],"study_design_scores_gemma":[0.000004650117,0.000009430376,0.0000764804,0.000009922509,0.0000049475125,0.0000091196325,0.0000075317485,0.9899624,0.0002413871,0.0068755345,0.0027934008,0.000005151649],"about_ca_topic_score_codex":0.018527536,"about_ca_topic_score_gemma":0.018170316,"teacher_disagreement_score":0.018527536,"about_ca_system_score_codex":0.0014929255,"about_ca_system_score_gemma":0.00089761254,"threshold_uncertainty_score":0.036839366},"labels":[],"label_agreement":null},{"id":"W4399141350","doi":"10.21203/rs.3.rs-4335811/v1","title":"Qanat Discharge Prediction Using a Comparative Analysis of Machine Learning Methods","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Water Systems and 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":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.12136579504715281,"score_gpt":0.447332698320648,"score_spread":0.3259669032734952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399141350","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":[0.53286785,0.024116531,0.42257562,0.001730206,0.00045431976,0.00022253954,0.000978696,0.0016256697,0.015428622],"genre_scores_gemma":[0.93361354,0.0024402607,0.06138777,0.000118670425,0.0001481862,0.00008235809,0.00051689765,0.00011296657,0.0015795452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974726,0.0013833861,0.00017326407,0.000247818,0.00062948023,0.00009340034],"domain_scores_gemma":[0.9771329,0.019770415,0.0003961651,0.00064753543,0.0019586876,0.00009433358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076078996,0.0007196513,0.0014468657,0.0028774245,0.00048991316,0.0016450438,0.00083030737,0.0010480593,0.0019124773],"category_scores_gemma":[0.01655247,0.00024971276,0.0011549284,0.0017859897,0.00036141634,0.0020873703,0.00062941504,0.00084439374,0.00034212557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016011965,0.00038764442,0.01872692,0.00068722235,0.0007651396,0.00006221137,0.000123222,0.5880828,0.0018769194,0.0073601888,0.0024113583,0.3779152],"study_design_scores_gemma":[0.000022804961,0.00015237935,0.0039106235,0.000030568433,0.00008206737,0.000014965375,0.00003801206,0.99296814,0.0009895171,0.001258466,0.00051629805,0.000016155402],"about_ca_topic_score_codex":0.00930046,"about_ca_topic_score_gemma":0.005851017,"teacher_disagreement_score":0.00930046,"about_ca_system_score_codex":0.0010114975,"about_ca_system_score_gemma":0.00096242566,"threshold_uncertainty_score":0.040234864},"labels":[],"label_agreement":null},{"id":"W4399677512","doi":"10.2166/hydro.2024.135","title":"Data augmentation using conditional generative adversarial network (cGAN): applications for sewer condition classification and testing using different machine learning techniques","year":2024,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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 Waterloo; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Support vector machine; Artificial intelligence; Computer science; Naive Bayes classifier; Decision tree; Linear discriminant analysis; Artificial neural network; Asset management; Data mining; Pattern recognition (psychology)","score_opus":0.07101107055262296,"score_gpt":0.30061745641295357,"score_spread":0.2296063858603306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399677512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.189194,0.00061204913,0.80438423,0.0007795484,0.00015246429,0.00016935411,0.00033868774,0.0020707762,0.0022988764],"genre_scores_gemma":[0.9280573,0.00012192346,0.07025699,0.0001404569,0.00003448619,0.00009575789,0.00038491283,0.000042699197,0.0008654309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991314,0.00044190523,0.000038955306,0.00014651807,0.00018334627,0.00005797359],"domain_scores_gemma":[0.9954047,0.0033626782,0.00028673623,0.00038874778,0.00046002815,0.00009715256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027675114,0.0008805911,0.00054595794,0.00066035404,0.00026855327,0.000478619,0.0010262536,0.00072155526,0.0010119053],"category_scores_gemma":[0.005823495,0.0002647548,0.00062893773,0.0005193215,0.0008309762,0.00072192145,0.0010492987,0.0013043763,0.00020174203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019229349,0.000119910794,0.004375643,0.00004623484,0.00004738239,0.000092841736,0.000050886512,0.9205813,0.002403899,0.0016437377,0.0010301975,0.06941578],"study_design_scores_gemma":[0.0000014716079,0.000016215954,0.000177323,0.0000018406101,0.0000019278873,0.000005951606,0.0000028370177,0.998451,0.00076981506,0.0005014633,0.00006793523,0.0000022624308],"about_ca_topic_score_codex":0.004060072,"about_ca_topic_score_gemma":0.003109057,"teacher_disagreement_score":0.004060072,"about_ca_system_score_codex":0.0006261954,"about_ca_system_score_gemma":0.0005478261,"threshold_uncertainty_score":0.014636219},"labels":[],"label_agreement":null},{"id":"W4399724054","doi":"10.1615/tfec2024.mpf.050352","title":"Numerical and experimental modeling of two leaks behavior for water-air multiphase flow through a pipeline","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Memorial University of Newfoundland; Concordia University","funders":"","keywords":"Multiphase flow; Pipeline (software); Computer science; Petroleum engineering; Numerical models; Flow (mathematics); Atmospheric model; Mechanics; Environmental science; Computer simulation; Geology; Simulation; Meteorology; Physics","score_opus":0.021277854450895382,"score_gpt":0.27403874801847544,"score_spread":0.2527608935675801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399724054","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":[0.9757934,0.0001351959,0.020912807,0.000121880796,0.000020170843,0.00004891487,0.00019447613,0.00022852735,0.0025447113],"genre_scores_gemma":[0.9949184,0.000060346447,0.0043378235,0.0000063431808,0.0000018567285,0.00003255571,0.000051688385,0.000008821143,0.00058213517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998091,0.000029217246,0.000015081043,0.000039804483,0.00006701743,0.000039846378],"domain_scores_gemma":[0.99949133,0.00023732129,0.00009747563,0.000043911416,0.00009486629,0.000035118275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003755682,0.00044236644,0.00042771501,0.00044763467,0.00048610524,0.0004805835,0.0004620974,0.0009861252,0.0010359822],"category_scores_gemma":[0.00086460455,0.00022994104,0.00040332694,0.00037384193,0.00077976094,0.0005080865,0.0004138381,0.00042747392,0.00009815611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002450179,0.00036522673,0.0077501438,0.0001642776,0.000013782341,0.0004042703,0.00033305594,0.8749358,0.10688115,0.0015002247,0.00029674463,0.0071103172],"study_design_scores_gemma":[0.000010734321,0.00016021825,0.0013712068,0.000005165352,0.000004012642,0.000021118543,0.000051014187,0.98690575,0.011206808,0.00010166575,0.00015109873,0.000011357799],"about_ca_topic_score_codex":0.0052602882,"about_ca_topic_score_gemma":0.0025151395,"teacher_disagreement_score":0.0052602882,"about_ca_system_score_codex":0.00053865055,"about_ca_system_score_gemma":0.0006131022,"threshold_uncertainty_score":0.010459363},"labels":[],"label_agreement":null},{"id":"W4399731053","doi":"10.1061/jhend8.hyeng-14019","title":"Exploring Hydraulic Impacts of Downstream Water Levels on the Drainage Capacity of a Stormwater Pipe","year":2024,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and 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":"Stormwater; Downstream (manufacturing); Drainage; Environmental science; Stormwater management; Hydrology (agriculture); Surface runoff; Geotechnical engineering; Geology; Engineering","score_opus":0.042079313038325544,"score_gpt":0.20180882103408718,"score_spread":0.15972950799576163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399731053","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":[0.996834,0.000011091722,0.0025655262,0.000011933207,0.0000023162827,0.000010706879,0.00009584482,0.00003362253,0.00043508672],"genre_scores_gemma":[0.999201,0.000016742617,0.0005620073,0.0000024832102,5.8301043e-7,0.000008909058,0.000057339996,0.0000036066392,0.0001473725],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985206,0.000019746769,0.0000067435217,0.000044247518,0.00003865116,0.000038567578],"domain_scores_gemma":[0.9996488,0.00018891464,0.000052089294,0.000030033705,0.00005409992,0.000026062498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022101375,0.000222616,0.00023684764,0.0002835166,0.00023453667,0.00037180167,0.00024945624,0.0003245624,0.00080677867],"category_scores_gemma":[0.00064741034,0.00017952951,0.00024849412,0.0002123139,0.00039909093,0.0006117839,0.00040039135,0.0003164336,0.00008959788],"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":[0.0008094673,0.00065722206,0.07301973,0.00017763118,0.00004530509,0.0003500724,0.00024959398,0.23721747,0.65857077,0.001658907,0.00043303578,0.026810747],"study_design_scores_gemma":[0.000050711995,0.0019387094,0.09702699,0.00000959097,0.000059781436,0.00005939144,0.00027813678,0.5795996,0.31929407,0.00076861127,0.00085076055,0.00006361892],"about_ca_topic_score_codex":0.0028629412,"about_ca_topic_score_gemma":0.0033258544,"teacher_disagreement_score":0.0028629412,"about_ca_system_score_codex":0.00042133464,"about_ca_system_score_gemma":0.00040761544,"threshold_uncertainty_score":0.0056925416},"labels":[],"label_agreement":null},{"id":"W4399799617","doi":"10.1007/978-3-031-56740-7_8","title":"Infinite Impulse Response Filters","year":2024,"lang":"en","type":"book-chapter","venue":"Digital Signal Processing","topic":"Water Systems and 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":"Concordia University","funders":"","keywords":"Finite impulse response; Impulse response; Infinite impulse response; Impulse (physics); Computer science; Digital filter; Physics; Mathematics; Filter (signal processing); Algorithm; Mathematical analysis; Computer vision; Classical mechanics","score_opus":0.010179899844881195,"score_gpt":0.19117156688667244,"score_spread":0.18099166704179126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399799617","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":[0.0012808949,0.008046928,0.583842,0.00051166385,0.0012318428,0.00003463248,0.00017929618,0.0017061171,0.40316662],"genre_scores_gemma":[0.020679947,0.0082437135,0.08629624,0.0005849252,0.00068225054,0.000057632897,0.00040881542,0.00057025644,0.8824762],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997547,0.000027189766,0.000007840367,0.000047335892,0.0001449187,0.000017975864],"domain_scores_gemma":[0.9998312,0.000064019216,0.000007837146,0.000035576508,0.000053371223,0.000007944447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019622025,0.0009860763,0.0005395619,0.0007562916,0.0004056117,0.0016088332,0.0005940014,0.0011052022,0.040357783],"category_scores_gemma":[0.0006148018,0.00036193753,0.0003404287,0.0007386984,0.0006547796,0.0013550132,0.00064844,0.0014131899,0.03177359],"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":[0.000042147814,0.000041517567,0.000045401044,0.00024802517,0.00002070146,0.00009242084,0.00012141008,0.005656516,0.015112193,0.23749448,0.08236003,0.6587652],"study_design_scores_gemma":[0.000008827652,0.00004523822,0.0002035422,0.00012251161,0.000022579334,0.0003931868,0.00004292813,0.015373875,0.012205918,0.12203632,0.84951776,0.000027338692],"about_ca_topic_score_codex":0.0004855701,"about_ca_topic_score_gemma":0.0007692589,"teacher_disagreement_score":0.040357783,"about_ca_system_score_codex":0.00041906664,"about_ca_system_score_gemma":0.00036842734,"threshold_uncertainty_score":0.1350103},"labels":[],"label_agreement":null},{"id":"W4400000452","doi":"10.1016/j.compchemeng.2024.108777","title":"Moving horizon estimation for pipeline leak detection, localization, and constrained size estimation","year":2024,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Water Systems and 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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leak; Pipeline (software); Discretization; Estimator; Pipeline transport; Control theory (sociology); Observer (physics); Horizon; Mathematical optimization; Computer science; Engineering; Mathematics; Statistics; Artificial intelligence; Environmental engineering","score_opus":0.0039028719797920743,"score_gpt":0.1817872447411305,"score_spread":0.17788437276133842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400000452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038997002,0.00015031378,0.99515224,0.00003881345,0.000020745922,0.000009484037,0.000015532882,0.00012056933,0.0005925166],"genre_scores_gemma":[0.9024341,0.00054385205,0.09397853,0.00007856784,0.000058862664,0.000116525975,0.00012308195,0.000048527745,0.0026178744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994467,0.00011897392,0.000035746914,0.00016094961,0.00018049187,0.000057156103],"domain_scores_gemma":[0.99926955,0.0002951848,0.00017439686,0.000050837836,0.00018259631,0.000027383034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006416222,0.00087538344,0.0007210267,0.00041341953,0.00030839158,0.0005941888,0.00079462887,0.0006612026,0.0014712904],"category_scores_gemma":[0.0023341042,0.0003812074,0.00060596253,0.00042670706,0.00057112164,0.0010277501,0.0007349121,0.0009772964,0.00020034269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002101295,0.000043817203,0.0008298056,0.0003074289,0.00005954981,0.00012871705,0.0001505479,0.849982,0.018212425,0.021223033,0.0012563936,0.10759611],"study_design_scores_gemma":[0.0000056801887,0.000049000035,0.00017810159,0.000006638736,0.000007799281,0.000011211274,0.0000056305103,0.99552155,0.0020933957,0.0015864624,0.00052498776,0.0000096283],"about_ca_topic_score_codex":0.007453077,"about_ca_topic_score_gemma":0.0039951215,"teacher_disagreement_score":0.007453077,"about_ca_system_score_codex":0.00067503407,"about_ca_system_score_gemma":0.0009870501,"threshold_uncertainty_score":0.014819384},"labels":[],"label_agreement":null},{"id":"W4400121950","doi":"10.2166/9781789063059_0299","title":"Data management and quality control","year":2024,"lang":"en","type":"book-chapter","venue":"IWA Publishing eBooks","topic":"Water Systems and 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":"Lake Simcoe Region Conservation Authority","funders":"","keywords":"Data quality; Data science; Computer science; Big data; Data management; Data collection; Quality (philosophy); Data governance; Anticipation (artificial intelligence); Control (management); Risk analysis (engineering); Data mining; Engineering; Operations management; Business; Artificial intelligence","score_opus":0.04969668545935088,"score_gpt":0.23075256980275768,"score_spread":0.1810558843434068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400121950","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":[0.0039255773,0.046427142,0.5718473,0.07358988,0.005624224,0.0013419789,0.0027074385,0.0024684558,0.29206797],"genre_scores_gemma":[0.10638096,0.053220104,0.5579891,0.02147256,0.0071294173,0.0021107264,0.006764187,0.0024656106,0.24246736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.97124755,0.010316379,0.002543158,0.0029983248,0.012359064,0.00053541193],"domain_scores_gemma":[0.9420799,0.026965143,0.0026237613,0.011094687,0.016449414,0.0007871855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030471666,0.00083455653,0.0011930923,0.0047746943,0.0020597607,0.014379172,0.0039570327,0.0027653715,0.01607699],"category_scores_gemma":[0.05170564,0.00076200336,0.00084726495,0.008590459,0.00636008,0.009289606,0.004955932,0.0050168256,0.0069962456],"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":[0.00002863192,0.000033322558,0.0005004048,0.0007219159,0.000029506724,0.0000983915,0.001535218,0.0020263535,0.0005990838,0.55924135,0.08103472,0.354151],"study_design_scores_gemma":[0.000010539443,0.000024060942,0.00045111068,0.0010789311,0.000011486417,0.00013155479,0.0003682065,0.0020955778,0.00084897625,0.14671774,0.848229,0.00003284848],"about_ca_topic_score_codex":0.0047968216,"about_ca_topic_score_gemma":0.002241155,"teacher_disagreement_score":0.030471666,"about_ca_system_score_codex":0.0050782757,"about_ca_system_score_gemma":0.010366983,"threshold_uncertainty_score":0.16115153},"labels":[],"label_agreement":null},{"id":"W4400122067","doi":"10.2166/9781789063059_0231","title":"From condition-based to service-based strategies","year":2024,"lang":"en","type":"book-chapter","venue":"IWA Publishing eBooks","topic":"Water Systems and 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":"Provincial Laboratory of Public Health; University of Calgary; University of Alberta; Lake Simcoe Region Conservation Authority","funders":"","keywords":"Context (archaeology); Prioritization; Risk analysis (engineering); Service (business); Asset (computer security); Computer science; Asset management; Engineering; Process management; Business; Computer security; Marketing; Geography","score_opus":0.015266042897519068,"score_gpt":0.20203681911129473,"score_spread":0.18677077621377566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400122067","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":[0.025488975,0.0015664719,0.81524366,0.0053201104,0.00013854461,0.00029037133,0.00010230145,0.0003122023,0.15153734],"genre_scores_gemma":[0.84889823,0.0020774119,0.13169271,0.00044062917,0.0000988941,0.0002921877,0.000105402316,0.00014078022,0.016253829],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99745935,0.0010405694,0.000108353735,0.00030494024,0.0007973758,0.0002894317],"domain_scores_gemma":[0.9989471,0.00040262035,0.00010735958,0.00013147073,0.00024735328,0.00016411368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024770552,0.0015164078,0.0005975187,0.0012286905,0.000729714,0.0054224283,0.0018124112,0.0018717189,0.0085039465],"category_scores_gemma":[0.003101013,0.00032485463,0.00060423237,0.0010291041,0.0041506407,0.003399642,0.0048270887,0.0019411019,0.0009960652],"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":[0.00002712623,0.00006113912,0.00043921036,0.00017950141,0.000031016792,0.00012947991,0.00049134844,0.13150018,0.0014562269,0.7992524,0.0019971693,0.064435236],"study_design_scores_gemma":[0.000020889933,0.00022550719,0.0005729981,0.00023651887,0.000030321367,0.0001414895,0.0011167562,0.24401967,0.001633673,0.71673596,0.035221223,0.000045046196],"about_ca_topic_score_codex":0.0029379255,"about_ca_topic_score_gemma":0.0019241788,"teacher_disagreement_score":0.0085039465,"about_ca_system_score_codex":0.0036784878,"about_ca_system_score_gemma":0.0028358325,"threshold_uncertainty_score":0.028448522},"labels":[],"label_agreement":null},{"id":"W4400122078","doi":"10.2166/9781789063059_0343","title":"Operation, maintenance and rehabilitation techniques","year":2024,"lang":"en","type":"book-chapter","venue":"IWA Publishing eBooks","topic":"Water Systems and 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 Calgary; University of Alberta","funders":"","keywords":"Terminology; Standardization; Rehabilitation; Engineering; Sanitary sewer; Trenchless technology; Construction engineering; Civil engineering; Risk analysis (engineering); Computer science; Business; Environmental engineering; Pipeline transport","score_opus":0.00674161889145553,"score_gpt":0.18089806838316702,"score_spread":0.1741564494917115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400122078","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":[0.025152309,0.040325437,0.34313816,0.0017686705,0.0010662533,0.00031705134,0.0006818286,0.00231303,0.5852374],"genre_scores_gemma":[0.17118156,0.03602329,0.1553116,0.00039179917,0.0004809648,0.00030275978,0.0012314683,0.0007630284,0.6343136],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968386,0.000024934456,0.000016925693,0.000078649246,0.00016857623,0.000027060983],"domain_scores_gemma":[0.99991107,0.000018557945,0.0000093247945,0.000021083726,0.000033007003,0.000006992372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021451761,0.00063794863,0.00036929044,0.0011012848,0.0004958459,0.0018394418,0.0013057236,0.00069034734,0.016687417],"category_scores_gemma":[0.00033632107,0.00029140533,0.00043405642,0.0012293292,0.00062697055,0.0013517876,0.00078800647,0.0007300315,0.0076854834],"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":[0.000039563492,0.00008461698,0.00034576858,0.0009232233,0.0000130966055,0.0001480487,0.0004887248,0.00961813,0.016808013,0.07894625,0.036748778,0.8558358],"study_design_scores_gemma":[0.000012310024,0.0001266487,0.0020793017,0.00054841116,0.000025076311,0.00079640344,0.00023658814,0.013615745,0.009572034,0.030533051,0.94242185,0.00003259756],"about_ca_topic_score_codex":0.0012148069,"about_ca_topic_score_gemma":0.0015553515,"teacher_disagreement_score":0.016687417,"about_ca_system_score_codex":0.0005802748,"about_ca_system_score_gemma":0.0005906997,"threshold_uncertainty_score":0.055824995},"labels":[],"label_agreement":null},{"id":"W4400140601","doi":"10.1016/j.ifacol.2024.10.236","title":"Optimization of the Paris wastewater treatment plants and sewer network: preliminary results","year":2024,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Water Systems and 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":"Wastewater; Sewage treatment; Sanitary sewer; Environmental science; Waste management; Environmental engineering; Engineering","score_opus":0.00865650298594841,"score_gpt":0.19500878955741444,"score_spread":0.18635228657146602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400140601","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":[0.8382434,0.003305774,0.12627965,0.00078964466,0.000112699454,0.00018814682,0.001220939,0.0006705917,0.029189153],"genre_scores_gemma":[0.9822711,0.00062183704,0.01151829,0.000060853763,0.000026326607,0.00008982759,0.00041439795,0.000054930726,0.0049425093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994816,0.0002375042,0.000017081346,0.00006703421,0.00008626477,0.00011061383],"domain_scores_gemma":[0.99841356,0.0011127638,0.00014073991,0.00006271617,0.00019104828,0.00007918282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096323015,0.0019579877,0.0016577352,0.0005862368,0.00042950152,0.0013862092,0.00071721285,0.0015303595,0.0028462303],"category_scores_gemma":[0.002214857,0.00047904896,0.0012575436,0.0007444435,0.0008385864,0.0005418548,0.00068360043,0.00092664704,0.0001932058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001891265,0.000017606772,0.00014152614,0.000026537582,0.000010869532,0.000023285575,0.0000059397103,0.9984987,0.0001946779,0.00034607077,0.000075664524,0.0006403235],"study_design_scores_gemma":[0.000014603798,0.000061305705,0.00051270804,0.000003965877,0.000012136359,0.0000048071993,0.000021734812,0.99811906,0.0005745051,0.00037315657,0.0002947606,0.000007177038],"about_ca_topic_score_codex":0.07354344,"about_ca_topic_score_gemma":0.03472284,"teacher_disagreement_score":0.07354344,"about_ca_system_score_codex":0.0025434145,"about_ca_system_score_gemma":0.001659583,"threshold_uncertainty_score":0.1462307},"labels":[],"label_agreement":null},{"id":"W4400235767","doi":"10.11159/ffhmt24.036","title":"Numerical Modeling Of Water-Air Multiphase Flow Within A Pipeline In The Presence Of Double Leaks","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer","topic":"Water Systems and Optimization","field":"Engineering","cited_by":5,"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":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Multiphase flow; Pipeline (software); Petroleum engineering; Flow (mathematics); Numerical models; Computer science; Mechanics; Environmental science; Marine engineering; Computer simulation; Geology; Engineering; Simulation; Physics","score_opus":0.023909724460290927,"score_gpt":0.23128459780831728,"score_spread":0.20737487334802635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400235767","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":[0.8849409,0.0005621806,0.09850752,0.0008887492,0.00016894971,0.000078695346,0.00021262238,0.0003799803,0.014260227],"genre_scores_gemma":[0.9941679,0.000085162515,0.0033611772,0.000021772525,0.000013937419,0.000022381779,0.00003871808,0.000023654484,0.0022651085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979776,0.00006115757,0.000011814353,0.000039546136,0.000041715262,0.000048009442],"domain_scores_gemma":[0.9994498,0.00027513516,0.00008333839,0.000032088195,0.00008038105,0.00007931763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046966746,0.00065150444,0.0010275283,0.0006470636,0.00088227127,0.0014777383,0.0009014038,0.0019739293,0.0013954791],"category_scores_gemma":[0.0015552739,0.0005514869,0.0005940874,0.00048621366,0.0014860019,0.0011613232,0.0010809544,0.0007998347,0.00019663884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001167924,0.00005863602,0.0010241704,0.000027230833,0.000011198333,0.00017780304,0.000052166182,0.990524,0.0047771735,0.0013292066,0.0001307923,0.0017708901],"study_design_scores_gemma":[0.000006362621,0.00001824507,0.00013690864,0.0000017769107,0.0000019557187,0.000008089401,0.000011445032,0.9993105,0.00032089493,0.00011649432,0.000063974396,0.0000033174686],"about_ca_topic_score_codex":0.0114103,"about_ca_topic_score_gemma":0.004928877,"teacher_disagreement_score":0.0114103,"about_ca_system_score_codex":0.0009114124,"about_ca_system_score_gemma":0.0011810586,"threshold_uncertainty_score":0.022687733},"labels":[],"label_agreement":null},{"id":"W4400287297","doi":"10.1121/10.0027713","title":"Leak detection in an operational underground water distribution network using active acoustics","year":2024,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Water Systems and 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":"Northern Digital (Canada)","funders":"","keywords":"Leak detection; Acoustics; Leak; Computer science; Environmental science; Engineering; Physics; Environmental engineering","score_opus":0.011414463328440939,"score_gpt":0.2285711412698838,"score_spread":0.21715667794144286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400287297","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":[0.9585484,0.000040448067,0.04075492,0.000038028873,0.0000048854195,0.000013601589,0.000034615485,0.0001333525,0.00043174077],"genre_scores_gemma":[0.99444515,0.00002467271,0.005327585,0.0000068939275,0.0000015224607,0.0000060052366,0.000014111951,0.000002768991,0.00017130372],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99980694,0.0000424838,0.000009097161,0.000051893832,0.000072724586,0.000016948323],"domain_scores_gemma":[0.999642,0.00016761714,0.000067878755,0.000018564313,0.00008588449,0.00001799363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023176442,0.00031193226,0.00022439753,0.00031366877,0.00012361114,0.00025338892,0.00033774335,0.00024391423,0.0003315712],"category_scores_gemma":[0.0006358097,0.00016423086,0.00008602977,0.0002224594,0.00035919843,0.00061101,0.00039198762,0.00017513934,0.000059728118],"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":[0.00094131875,0.00033590512,0.057623155,0.0002524424,0.00004411624,0.00046028037,0.0006211158,0.05786158,0.7534122,0.00047883778,0.0003228326,0.12764625],"study_design_scores_gemma":[0.00006138566,0.0010769913,0.03427488,0.000024881934,0.000048383557,0.00022821441,0.00046908399,0.72842944,0.23403785,0.00050519203,0.00079808565,0.000045587374],"about_ca_topic_score_codex":0.0014126389,"about_ca_topic_score_gemma":0.001974055,"teacher_disagreement_score":0.0014126389,"about_ca_system_score_codex":0.00022597863,"about_ca_system_score_gemma":0.00016789255,"threshold_uncertainty_score":0.0028088093},"labels":[],"label_agreement":null},{"id":"W4400532948","doi":"10.1016/j.tust.2024.105958","title":"A hybrid machine learning-based model for predicting failure of water mains under climatic variations: A Hong Kong case study","year":2024,"lang":"en","type":"article","venue":"Tunnelling and Underground Space Technology","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Mains electricity; Engineering; Forensic engineering; Machine learning; Mechanical engineering; Environmental science; Computer science; Electrical engineering","score_opus":0.012375437945787441,"score_gpt":0.22028464265094408,"score_spread":0.20790920470515664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400532948","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":[0.98728323,0.00015985392,0.010392161,0.00012279111,0.000015946664,0.00003208352,0.00043526336,0.000119676544,0.0014390894],"genre_scores_gemma":[0.9973003,0.00005935697,0.0013740055,0.0000073963747,0.0000041112808,0.000014948299,0.00018356561,0.0000065487516,0.0010497482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997603,0.0000665094,0.000019195886,0.000059475416,0.000027422153,0.00006710466],"domain_scores_gemma":[0.99919075,0.00043450488,0.00008408108,0.000046416757,0.00019388333,0.000050356957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088533066,0.0010488139,0.0009564729,0.00087837083,0.00050458696,0.0010615577,0.0011490799,0.0010241991,0.0013407316],"category_scores_gemma":[0.0009557256,0.0004851209,0.0007628273,0.0007889099,0.000501537,0.00064939365,0.00043280804,0.0004503398,0.00018195913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006314423,0.00006279856,0.009338398,0.000016936016,0.000029928173,0.00013929706,0.000020543182,0.9867662,0.00030319637,0.00015080877,0.00018389913,0.002924878],"study_design_scores_gemma":[0.0000025961926,0.000015666037,0.0015872592,0.0000013133284,0.0000057483153,0.000005251991,0.000016438076,0.99820495,0.000096931435,0.000031733663,0.000028882565,0.0000033216168],"about_ca_topic_score_codex":0.2586757,"about_ca_topic_score_gemma":0.15782066,"teacher_disagreement_score":0.2586757,"about_ca_system_score_codex":0.0025847983,"about_ca_system_score_gemma":0.0014462598,"threshold_uncertainty_score":0.51434004},"labels":[],"label_agreement":null},{"id":"W4400546375","doi":"10.2166/aqua.2024.083","title":"Optimized phased planning for dynamic rehabilitation of integrated municipal infrastructure","year":2024,"lang":"en","type":"article","venue":"AQUA - Water Infrastructure Ecosystems and Society","topic":"Water Systems and 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":true,"ca_institutions":"Concordia University","funders":"H2020 European Research Council; Österreichische Agentur für Internationale Mobilität und Kooperation in Bildung, Wissenschaft und Forschung; Austrian Science Fund; Österreichischen Akademie der Wissenschaften","keywords":"Interdependence; Plan (archaeology); Intervention (counseling); Multi-objective optimization; Metaheuristic; Phase (matter); Evolutionary algorithm; Variable (mathematics); Computer science; Operations research; Task (project management); Operations management; Transport engineering; Engineering; Systems engineering; Artificial intelligence","score_opus":0.0044252052763874014,"score_gpt":0.2205553670306429,"score_spread":0.2161301617542555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400546375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38188174,0.00029556776,0.6046654,0.00019163499,0.000029386158,0.00031060667,0.00030788718,0.0003873325,0.011930425],"genre_scores_gemma":[0.95678645,0.000049621798,0.041386537,0.00001670237,0.0000025263337,0.00012966816,0.000089199806,0.000023324837,0.0015158957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995938,0.00015206165,0.000011946575,0.00006563984,0.00007567776,0.00010091172],"domain_scores_gemma":[0.9995222,0.00023455516,0.000077576646,0.000029982886,0.000087650034,0.000048013968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008268802,0.0007077108,0.00071048754,0.00080209546,0.00048949284,0.0010488564,0.0006786648,0.0007199212,0.0024203232],"category_scores_gemma":[0.0011647574,0.00053974555,0.00052030745,0.0007803192,0.0006400946,0.0005105255,0.0006984836,0.0005817533,0.00014486638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017868273,0.000017803519,0.00017071447,0.00000877856,0.000005095295,0.000012195684,0.0000056759727,0.99626946,0.00037810783,0.00039336382,0.000039792987,0.002681154],"study_design_scores_gemma":[0.000008167829,0.000055808996,0.00023068783,0.0000031115144,0.000005875643,0.0000041724147,0.0000141674245,0.998679,0.00031264612,0.00050250976,0.00018071133,0.000003127484],"about_ca_topic_score_codex":0.019966422,"about_ca_topic_score_gemma":0.020850131,"teacher_disagreement_score":0.019966422,"about_ca_system_score_codex":0.0021953345,"about_ca_system_score_gemma":0.0023119617,"threshold_uncertainty_score":0.03970039},"labels":[],"label_agreement":null},{"id":"W4400900249","doi":"10.1016/j.compchemeng.2024.108811","title":"Fast Bayesian filtering for wastewater treatment plants with inaccurate process noise statistics","year":2024,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Water Systems and 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 Alberta","funders":"National Key Research and Development Program of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Bayesian probability; Noise (video); Process (computing); Statistics; Computer science; Bayesian statistics; Artificial intelligence; Machine learning; Environmental science; Mathematics; Data mining; Bayesian inference","score_opus":0.006881796793363438,"score_gpt":0.19609895775882882,"score_spread":0.1892171609654654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400900249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017028425,0.00017452451,0.98145914,0.00020402155,0.000034620094,0.000016708835,0.00005752272,0.0003226146,0.00070232037],"genre_scores_gemma":[0.67718846,0.0005189602,0.3153117,0.00018215616,0.00012003158,0.00013794735,0.0005183992,0.00020962647,0.0058126934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989925,0.0002948149,0.000051239942,0.00014293674,0.00039684735,0.000121672405],"domain_scores_gemma":[0.997239,0.0020031766,0.00022408279,0.00015183455,0.00032201913,0.00005989733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019231435,0.00094168336,0.0012497945,0.0006106936,0.00059788214,0.0011165268,0.00090027764,0.0015569385,0.0012164088],"category_scores_gemma":[0.007389722,0.0011670792,0.0007571323,0.00073857495,0.00062235387,0.001537986,0.0010511902,0.0014973283,0.00039836398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036672136,0.00007751768,0.0006598284,0.0001334777,0.00006256087,0.00006274856,0.000083572275,0.89729786,0.0076971087,0.009561323,0.0017160376,0.08228127],"study_design_scores_gemma":[0.000008467962,0.000010375837,0.00017265759,0.0000027974859,0.000004781165,0.000007927621,0.0000032116893,0.9967206,0.00092286244,0.001953019,0.00018723831,0.0000061228766],"about_ca_topic_score_codex":0.01814382,"about_ca_topic_score_gemma":0.017594736,"teacher_disagreement_score":0.01814382,"about_ca_system_score_codex":0.0012505418,"about_ca_system_score_gemma":0.0025948267,"threshold_uncertainty_score":0.036076427},"labels":[],"label_agreement":null},{"id":"W4401136384","doi":"10.18280/mmep.110702","title":"Optimal Operation of the Multi-reservoir Rainwater Harvesting System for Hydropower Generation","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Water Systems and 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":"Rainwater harvesting; Hydropower; Environmental science; Water resource management; Hydrology (agriculture); Geology; Engineering; Geotechnical engineering; Electrical engineering; Ecology","score_opus":0.03130006757476222,"score_gpt":0.20602705340469296,"score_spread":0.17472698582993074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401136384","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":[0.9318792,0.00021961746,0.06366676,0.000102632875,0.00002005823,0.00017193446,0.00015674559,0.00030311572,0.0034799052],"genre_scores_gemma":[0.99451584,0.000036268975,0.0048753233,0.000004547494,0.0000016286778,0.000030748513,0.000034587938,0.0000041683734,0.0004968298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998011,0.00004327819,0.0000135605715,0.000041392464,0.000037343016,0.00006341088],"domain_scores_gemma":[0.99977905,0.0000737652,0.000055635883,0.000015341173,0.00005051261,0.0000256305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005764719,0.0005497157,0.00053360715,0.00035521606,0.00036603777,0.00071744254,0.0005663181,0.00039615805,0.0013222704],"category_scores_gemma":[0.00046996682,0.00035211787,0.00036987904,0.0003235543,0.00026963747,0.0006568603,0.0004115092,0.00031143957,0.00015008207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024010993,0.0002002831,0.0037695905,0.00009746443,0.00003581801,0.00015925073,0.000051319894,0.95507056,0.01564823,0.00050685304,0.00027137352,0.023949131],"study_design_scores_gemma":[0.000039392955,0.00040935134,0.002332597,0.000006266008,0.000034661734,0.00002234492,0.000060475704,0.99029654,0.006289699,0.000253788,0.00024232928,0.000012520602],"about_ca_topic_score_codex":0.005297305,"about_ca_topic_score_gemma":0.006264453,"teacher_disagreement_score":0.005297305,"about_ca_system_score_codex":0.0006280153,"about_ca_system_score_gemma":0.0010147266,"threshold_uncertainty_score":0.010532916},"labels":[],"label_agreement":null},{"id":"W4401341977","doi":"10.1101/2024.08.02.24311418","title":"Optimization and performance analytics of global aircraft-based wastewater surveillance networks","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Water Systems and 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":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Centers for Disease Control and Prevention; National Institutes of Health; Bill and Melinda Gates Foundation; U.S. Department of Health and Human Services","keywords":"Analytics; Wastewater; Computer science; Environmental science; Business; Data science; Environmental engineering","score_opus":0.00804219038009448,"score_gpt":0.1960946968997299,"score_spread":0.1880525065196354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401341977","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":[0.49394625,0.0006048588,0.49255678,0.00183423,0.000051028903,0.00009952573,0.0010668907,0.0003587888,0.00948161],"genre_scores_gemma":[0.98575866,0.00020319349,0.01242261,0.000048975373,0.000017355389,0.00005633806,0.00027627934,0.000034156557,0.0011823666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959785,0.00017171117,0.000013660345,0.000082567574,0.000053762087,0.00008049645],"domain_scores_gemma":[0.99833775,0.0011941706,0.00022084544,0.000053190055,0.00011961362,0.00007438157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011205807,0.0011401507,0.0007424746,0.0005168124,0.00026859395,0.00093303237,0.00061343156,0.0007679965,0.0013061223],"category_scores_gemma":[0.004091455,0.00034287464,0.0005390078,0.00042072218,0.0008907328,0.00075127056,0.00088994135,0.00073075853,0.00010013053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008404277,0.0000047674334,0.00034475545,0.000005309304,0.0000042601973,0.0000076544875,0.0000037912992,0.9980714,0.00008171681,0.0008987135,0.000064636966,0.00050468196],"study_design_scores_gemma":[0.0000012400103,0.000005200062,0.00013507366,9.92099e-7,0.0000011206685,0.0000012740323,0.000005315581,0.9988493,0.000047571666,0.0009197043,0.00003228686,9.322567e-7],"about_ca_topic_score_codex":0.010029551,"about_ca_topic_score_gemma":0.003399028,"teacher_disagreement_score":0.010029551,"about_ca_system_score_codex":0.001070022,"about_ca_system_score_gemma":0.00094741315,"threshold_uncertainty_score":0.019942343},"labels":[],"label_agreement":null},{"id":"W4401460028","doi":"10.1115/omae2024-127882","title":"Investigation of Multiphase Flow Leak Detection in Pipeline Using Time Series Analysis Technique","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Computer science; Pipeline (software); Time series; Series (stratigraphy); Leak detection; Leak; Multiphase flow; Flow (mathematics); Pipeline transport; Petroleum engineering; Engineering; Mechanics; Geology; Mechanical engineering; Machine learning","score_opus":0.010077593954675683,"score_gpt":0.20403919873706658,"score_spread":0.19396160478239088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401460028","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":[0.6743971,0.0004901266,0.3224054,0.0001298747,0.00007103867,0.0000647943,0.00025952954,0.0008897792,0.0012922232],"genre_scores_gemma":[0.9620536,0.0002350314,0.03706649,0.000019846846,0.000019035266,0.000033567867,0.00011599788,0.000028624328,0.00042775608],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966514,0.00003779464,0.000023888304,0.00008179283,0.00016397888,0.00002750083],"domain_scores_gemma":[0.9995402,0.00014835803,0.0001305522,0.00002792125,0.00013295114,0.000020005025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005033262,0.0005118881,0.00036797172,0.0016605272,0.00017115619,0.00041542944,0.0002962973,0.0004839005,0.0007306273],"category_scores_gemma":[0.00103038,0.00015739363,0.00035735182,0.0013014773,0.00024630706,0.0008262765,0.00021920595,0.0003174988,0.00017723218],"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":[0.0005019449,0.00041108325,0.03382598,0.0006064727,0.00014843633,0.001057735,0.00063075725,0.03921372,0.6315185,0.0017626621,0.0011423816,0.28918037],"study_design_scores_gemma":[0.0000149544885,0.00072484807,0.057065412,0.000034965724,0.00008159437,0.00064287527,0.0003303183,0.79968154,0.13851018,0.0011208747,0.0017249143,0.0000676218],"about_ca_topic_score_codex":0.00087860145,"about_ca_topic_score_gemma":0.0005872801,"teacher_disagreement_score":0.0016605272,"about_ca_system_score_codex":0.00018325401,"about_ca_system_score_gemma":0.00019769011,"threshold_uncertainty_score":0.0026618838},"labels":[],"label_agreement":null},{"id":"W4401520464","doi":"10.1039/d4ew00329b","title":"Improving chlorine residual predictions in water distribution systems using recurrent neural networks","year":2024,"lang":"en","type":"article","venue":"Environmental Science Water Research & Technology","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Water quality; Distribution (mathematics); Artificial neural network; Computer science; Chlorine; Environmental science; Artificial intelligence; Chemistry; Mathematics; Algorithm; Ecology; Biology","score_opus":0.01859282089393839,"score_gpt":0.2569409289984921,"score_spread":0.2383481081045537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401520464","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":[0.7272807,0.0010086881,0.2639557,0.001007559,0.000107742286,0.000044892036,0.00053133327,0.0025158413,0.003547575],"genre_scores_gemma":[0.9949228,0.00006744584,0.004133597,0.000034726723,0.0000108292115,0.0000071695526,0.00018525096,0.000026942405,0.00061130174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998292,0.000040079543,0.000010290589,0.000050591418,0.00003780647,0.000032132662],"domain_scores_gemma":[0.9992575,0.00041324124,0.000088393295,0.000039846385,0.00016886953,0.000032060707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005505599,0.0007203285,0.00059752614,0.00036454402,0.00028003205,0.00089920696,0.00069811195,0.0007683808,0.0009582489],"category_scores_gemma":[0.0021510802,0.0003798702,0.00039062175,0.00025599642,0.00023039333,0.00092634105,0.0004279091,0.00095578836,0.00022335736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009909016,0.00006163566,0.0021058896,0.000031908865,0.00003534634,0.000026489402,0.000012113167,0.9757263,0.0018306053,0.00020732776,0.00051290577,0.019350352],"study_design_scores_gemma":[0.0000016893845,0.0000059035815,0.000114280476,8.485326e-7,0.0000019482154,5.677833e-7,0.0000012967854,0.99944514,0.0003095925,0.000099070305,0.00001852769,0.000001116303],"about_ca_topic_score_codex":0.04013199,"about_ca_topic_score_gemma":0.037056793,"teacher_disagreement_score":0.04013199,"about_ca_system_score_codex":0.00087535725,"about_ca_system_score_gemma":0.0008191242,"threshold_uncertainty_score":0.07979679},"labels":[],"label_agreement":null},{"id":"W4401549698","doi":"10.2166/ws.2024.187","title":"Investigating incomplete mixing models in cross junctions under real-world conditions of water distribution networks","year":2024,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixing (physics); Distribution (mathematics); Environmental science; Physics; Mathematics; Mathematical analysis","score_opus":0.013507736598001915,"score_gpt":0.23928212332046003,"score_spread":0.22577438672245811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401549698","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":[0.8257362,0.00022831289,0.17264345,0.00013809079,0.000017418086,0.00005174603,0.000115754105,0.000067618785,0.0010013357],"genre_scores_gemma":[0.99425465,0.000069585585,0.0052902848,0.000010908793,0.0000049485816,0.000024972856,0.00008604465,0.0000056615986,0.00025276738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983557,0.0007304748,0.0000790009,0.00037837058,0.00020519648,0.0002512277],"domain_scores_gemma":[0.9914622,0.005936264,0.001395892,0.00035117893,0.000635205,0.0002193657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004742885,0.00080871524,0.0011360731,0.0008940809,0.0005500155,0.0010912368,0.0011047402,0.001189097,0.0005519268],"category_scores_gemma":[0.0097640455,0.0005858643,0.0011410879,0.0005161557,0.0010655558,0.0021361813,0.0010373242,0.00082558865,0.00006261268],"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":[0.000036676334,0.000023726612,0.0020645084,0.000015713713,0.000024660294,0.000042773154,0.000026071411,0.9942272,0.0006939688,0.0019703952,0.000031209467,0.00084305234],"study_design_scores_gemma":[0.0000024437552,0.000020872596,0.00042404205,0.0000012970495,0.000005955614,0.0000046739647,0.000013881544,0.99860626,0.0002369694,0.000657476,0.000023110866,0.000002968036],"about_ca_topic_score_codex":0.009636587,"about_ca_topic_score_gemma":0.0039562434,"teacher_disagreement_score":0.009636587,"about_ca_system_score_codex":0.0015354545,"about_ca_system_score_gemma":0.0007070704,"threshold_uncertainty_score":0.025083125},"labels":[],"label_agreement":null},{"id":"W4401686017","doi":"10.3390/en17164107","title":"A Binary Expansion Approach for the Water Pump Scheduling Problem in Large and High-Altitude Water Supply Systems","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Water Systems and 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":"Group for Research in Decision Analysis; HEC Montréal","funders":"Agencia Nacional de Investigación y Desarrollo","keywords":"Desalination; Water supply; Mathematical optimization; Computer science; Scheduling (production processes); Time horizon; Process engineering; Environmental science; Engineering; Environmental engineering; Mathematics","score_opus":0.007579757625632265,"score_gpt":0.19031669037343632,"score_spread":0.18273693274780406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401686017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013474501,0.00036009145,0.97932565,0.00034621847,0.000058123802,0.00006186792,0.00007853252,0.00009962666,0.006195407],"genre_scores_gemma":[0.50443685,0.00087031105,0.48247534,0.0004131051,0.00016317508,0.0004741125,0.0003303062,0.00017366518,0.010663107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947983,0.00022844938,0.000016189391,0.0000708204,0.0001341799,0.00007051606],"domain_scores_gemma":[0.9987545,0.0009634848,0.00009702051,0.000027498007,0.00010517265,0.000052348925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011051607,0.00082589005,0.0009430121,0.0004763746,0.0004547531,0.00083218096,0.001045995,0.0011815773,0.0051176124],"category_scores_gemma":[0.0028307713,0.00053778157,0.000651272,0.0008427102,0.00071617065,0.0013476074,0.0009360219,0.0018819566,0.00048477165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003452061,0.000031032527,0.00018057777,0.000075432596,0.000009062385,0.000058403868,0.00003792628,0.9695388,0.00069670705,0.016010752,0.0008141261,0.012512611],"study_design_scores_gemma":[0.0000081633625,0.00000806598,0.00004305655,0.0000057243783,0.0000020513583,0.000006474124,0.000007940482,0.9963574,0.000096726384,0.003071012,0.00039116436,0.0000022332276],"about_ca_topic_score_codex":0.0073245433,"about_ca_topic_score_gemma":0.0067276144,"teacher_disagreement_score":0.0073245433,"about_ca_system_score_codex":0.0009945713,"about_ca_system_score_gemma":0.0011852313,"threshold_uncertainty_score":0.017120183},"labels":[],"label_agreement":null},{"id":"W4401918800","doi":"10.1007/s13349-024-00845-2","title":"Time-Transformer for acoustic leak detection in water distribution network","year":2024,"lang":"en","type":"article","venue":"Journal of Civil Structural Health Monitoring","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Leak; Leak detection; Transformer; Distribution transformer; Acoustics; Computer science; Environmental science; Engineering; Electrical engineering; Physics; Voltage","score_opus":0.00884267781244237,"score_gpt":0.25023637800989157,"score_spread":0.2413937001974492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401918800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17939872,0.0004625648,0.81690705,0.00012711207,0.00006872016,0.000045831315,0.0001442147,0.0010532149,0.00179257],"genre_scores_gemma":[0.94979364,0.00021083123,0.048191078,0.000037154026,0.000017013826,0.000018319666,0.00009842683,0.000031003558,0.0016025244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982566,0.000054894805,0.000008010317,0.00003221361,0.000057779213,0.000021494201],"domain_scores_gemma":[0.99969506,0.00014498747,0.00003543305,0.00001647331,0.00008778224,0.000020314876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029180845,0.00039996483,0.00033246953,0.00039560423,0.00014174049,0.00031225986,0.0003699395,0.00034558927,0.0017512394],"category_scores_gemma":[0.0008464915,0.00012778903,0.00020663417,0.000309973,0.00015365613,0.0004650337,0.00026101034,0.00029695057,0.00040285045],"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":[0.0024388486,0.00028941073,0.010317974,0.00045283546,0.00009357312,0.0003587327,0.00012667278,0.15784559,0.30045357,0.005506833,0.0034263732,0.5186895],"study_design_scores_gemma":[0.00002171357,0.00015874485,0.001264632,0.000006315665,0.000017369099,0.00015024185,0.000032507574,0.9717234,0.025289146,0.0006566423,0.00067190395,0.000007461591],"about_ca_topic_score_codex":0.0017763282,"about_ca_topic_score_gemma":0.0019262423,"teacher_disagreement_score":0.0017763282,"about_ca_system_score_codex":0.00021383901,"about_ca_system_score_gemma":0.0003254588,"threshold_uncertainty_score":0.005858481},"labels":[],"label_agreement":null},{"id":"W4401960252","doi":"10.3390/engproc2024069003","title":"AI-Assisted Pump Operation for Energy-Efficient Water Distribution Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"","keywords":"Computer science; Energy (signal processing); Physics","score_opus":0.007675877459975622,"score_gpt":0.19598379208206856,"score_spread":0.18830791462209295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401960252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048047367,0.00020382073,0.9412015,0.00031696275,0.00005372683,0.00010083951,0.00018055207,0.0021085246,0.0077867135],"genre_scores_gemma":[0.91366315,0.00007476911,0.08302151,0.000065734916,0.000013534023,0.00010223156,0.00013045261,0.000085767686,0.0028429579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997192,0.0001056234,0.000011411004,0.000041661293,0.00007679489,0.000045245557],"domain_scores_gemma":[0.9997148,0.000147776,0.000033405417,0.000022723623,0.000060050614,0.000021262955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060990104,0.00066031027,0.000501745,0.00040487322,0.00041612005,0.0008625499,0.0009134806,0.0005994627,0.0030265455],"category_scores_gemma":[0.0010326126,0.0003122459,0.00050700986,0.00031705265,0.0003654968,0.0009311816,0.00078566215,0.00081112434,0.00037498807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004670232,0.000032745378,0.00022526755,0.000035262645,0.0000100632415,0.000034206947,0.000028167615,0.9784697,0.0018616734,0.0022351886,0.00050486246,0.016516054],"study_design_scores_gemma":[0.0000032578039,0.000009311088,0.000023556348,0.0000012504687,0.0000016480408,0.0000028856489,0.0000059173367,0.99892235,0.000284038,0.00049011834,0.00025415805,0.0000016497107],"about_ca_topic_score_codex":0.013067709,"about_ca_topic_score_gemma":0.013982369,"teacher_disagreement_score":0.013067709,"about_ca_system_score_codex":0.00083844387,"about_ca_system_score_gemma":0.0014245143,"threshold_uncertainty_score":0.025983334},"labels":[],"label_agreement":null},{"id":"W4402057508","doi":"10.1061/9780784485583.009","title":"Age Is a Misunderstood Predictor of Pipe Failure Risk","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"American Water (Canada); University of Waterloo","funders":"","keywords":"Computer science; Reliability engineering; Forensic engineering; Engineering","score_opus":0.006333394237523466,"score_gpt":0.17662847055248135,"score_spread":0.17029507631495788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402057508","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":[0.9703363,0.00550375,0.016344173,0.002617459,0.00020038757,0.000012301183,0.0017026032,0.00013914742,0.0031439327],"genre_scores_gemma":[0.99659497,0.0008923417,0.0012087375,0.00010379899,0.000116821815,0.000003579834,0.00034903054,0.000020827338,0.0007099837],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99916685,0.00029153473,0.00009993897,0.00018597803,0.00017323424,0.000082501756],"domain_scores_gemma":[0.9849095,0.008597076,0.0040678885,0.0010566972,0.000916658,0.00045215117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002117759,0.00038089207,0.00042605476,0.0009498341,0.0002527736,0.0007897346,0.00036490304,0.00051270827,0.0018451157],"category_scores_gemma":[0.012485631,0.00021402953,0.0005554617,0.0010401049,0.00050619687,0.0017533922,0.00061084406,0.0007400524,0.00052834966],"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":[0.00003698673,0.000008623583,0.9863657,0.000028526614,0.000056086184,0.000056333738,0.00010423035,0.0016937573,0.0001747807,0.000278504,0.00065939466,0.010537142],"study_design_scores_gemma":[0.000002823237,0.00009155567,0.98431027,0.000062298146,0.000093648574,0.00047215892,0.00033977744,0.007637528,0.00052558474,0.0018775186,0.004562322,0.000024505714],"about_ca_topic_score_codex":0.0029108038,"about_ca_topic_score_gemma":0.004393663,"teacher_disagreement_score":0.0029108038,"about_ca_system_score_codex":0.00023702296,"about_ca_system_score_gemma":0.00038900579,"threshold_uncertainty_score":0.011199892},"labels":[],"label_agreement":null},{"id":"W4402059331","doi":"10.1061/9780784485569.053","title":"Pipe Diameter Is a Dominant Factor in Pipe Failure Risk","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"American Water (Canada); University of Waterloo","funders":"","keywords":"Factor (programming language); Computer science; Forensic engineering; Reliability engineering; Engineering; Programming language","score_opus":0.006155326483224228,"score_gpt":0.19020080555852786,"score_spread":0.18404547907530364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402059331","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":[0.9605048,0.0030508256,0.02659796,0.00093154,0.00007502965,0.00007109873,0.0012130475,0.00030448247,0.0072512617],"genre_scores_gemma":[0.9973022,0.00028918637,0.0012273706,0.000025998466,0.000027876808,0.0000100041825,0.00023853817,0.00003441755,0.0008444315],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963606,0.00068242784,0.000482833,0.0008840571,0.0013055962,0.00028451908],"domain_scores_gemma":[0.9349192,0.039627418,0.01632149,0.004011038,0.0034902336,0.0016307851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035432223,0.00065424317,0.0006946526,0.0021288574,0.0004866528,0.0013797672,0.00066412444,0.0009477003,0.0039524776],"category_scores_gemma":[0.024410661,0.0006519703,0.0012243488,0.0019871404,0.0010479954,0.002008313,0.0012779679,0.0010197214,0.0011990936],"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":[0.0001777356,0.000038690272,0.9767711,0.000086424276,0.000121819554,0.00037828434,0.00031906058,0.0058820713,0.0017819988,0.00063481036,0.00058217766,0.013225791],"study_design_scores_gemma":[0.000005378569,0.00018763753,0.9892106,0.000041586685,0.000081684186,0.00066258624,0.00032857002,0.00607354,0.0006187387,0.0012550435,0.0014804304,0.000054286098],"about_ca_topic_score_codex":0.0036913375,"about_ca_topic_score_gemma":0.0034964206,"teacher_disagreement_score":0.0039524776,"about_ca_system_score_codex":0.0005633533,"about_ca_system_score_gemma":0.0005935715,"threshold_uncertainty_score":0.018738627},"labels":[],"label_agreement":null},{"id":"W4402059448","doi":"10.1061/9780784485569.056","title":"Leak Detection and Localization in Water Networks Using Convolutional Neural Networks with a Modified Loss Function","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Convolutional neural network; Computer science; Function (biology); Leak detection; Leak; Artificial neural network; Artificial intelligence; Engineering","score_opus":0.007872889591366897,"score_gpt":0.17156026605282199,"score_spread":0.1636873764614551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402059448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35340872,0.00096174725,0.63917315,0.00069493323,0.00007947069,0.00006508212,0.00029264047,0.0018667236,0.0034575635],"genre_scores_gemma":[0.9586115,0.00016958077,0.03876135,0.000099385165,0.000013510807,0.000037217153,0.00023875303,0.000040201907,0.0020285423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997907,0.00004001751,0.000012305382,0.00007481504,0.000038867165,0.000043345397],"domain_scores_gemma":[0.99946004,0.0002516224,0.0000886482,0.000036099023,0.00013741027,0.000026220257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074745005,0.0010741364,0.00052616355,0.00053626846,0.00028474128,0.0006464521,0.00081170193,0.00082481495,0.0008694693],"category_scores_gemma":[0.00230551,0.00044716962,0.0004879955,0.0004670833,0.0005814393,0.0012968589,0.00072996196,0.0008524176,0.00017708859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007176578,0.000033882334,0.0020020301,0.000027655033,0.00002243391,0.000034497953,0.000015971595,0.965125,0.0018558276,0.00079564354,0.00040112322,0.029614089],"study_design_scores_gemma":[0.0000012589448,0.000007541742,0.00015161102,0.0000023302812,0.000002063212,0.000002751218,0.0000017756355,0.9988734,0.00062860054,0.00028606967,0.00004100849,0.0000015708846],"about_ca_topic_score_codex":0.025508134,"about_ca_topic_score_gemma":0.02446976,"teacher_disagreement_score":0.025508134,"about_ca_system_score_codex":0.0018461128,"about_ca_system_score_gemma":0.00096596894,"threshold_uncertainty_score":0.05071932},"labels":[],"label_agreement":null},{"id":"W4402059665","doi":"10.1061/9780784485590.001","title":"Advancing Water Network Sustainability through Smart Utility Approaches: Case Studies and Lessons Learned (WRF 4917)","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Stantec (Canada)","funders":"","keywords":"Weather Research and Forecasting Model; Sustainability; Computer science; Environmental science; Meteorology; Geography","score_opus":0.05877920949283526,"score_gpt":0.2894366058634673,"score_spread":0.23065739637063204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402059665","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":[0.8957165,0.0014328385,0.026620872,0.005933556,0.00009672412,0.0006366823,0.00028023735,0.000116188,0.06916637],"genre_scores_gemma":[0.9730987,0.0018511709,0.01805361,0.0002390976,0.000018016983,0.00014505045,0.000102522805,0.000024513916,0.006467377],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9970566,0.001983395,0.00005588053,0.00011885251,0.0004748853,0.0003105183],"domain_scores_gemma":[0.997068,0.0017179772,0.00019038185,0.00030007394,0.00047039054,0.00025314608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042725736,0.00035609692,0.00024770736,0.00063692586,0.0014325686,0.0019248631,0.0010860095,0.0013295132,0.002411722],"category_scores_gemma":[0.0044147237,0.00023393633,0.00028791168,0.0014076674,0.0017781359,0.0026142013,0.001814286,0.0013605704,0.00022459726],"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":[0.00056838285,0.005234516,0.04440706,0.0019029721,0.00016014166,0.01602872,0.028739523,0.22782736,0.0067848195,0.21140887,0.043255325,0.41368237],"study_design_scores_gemma":[0.00054433744,0.004329584,0.027386542,0.0015507033,0.00015993162,0.003062373,0.16274528,0.20673165,0.021734536,0.10132826,0.47019953,0.00022730997],"about_ca_topic_score_codex":0.017081866,"about_ca_topic_score_gemma":0.04669529,"teacher_disagreement_score":0.017081866,"about_ca_system_score_codex":0.003254398,"about_ca_system_score_gemma":0.0021138056,"threshold_uncertainty_score":0.033964872},"labels":[],"label_agreement":null},{"id":"W4402060114","doi":"10.1061/9780784485590.014","title":"Detecting Leaks in Purged Pipelines","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Pure North","funders":"","keywords":"Pipeline transport; Computer science; Petroleum engineering; Geology; Engineering; Mechanical engineering","score_opus":0.006268181930190432,"score_gpt":0.18656896017529476,"score_spread":0.18030077824510432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402060114","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":[0.8694912,0.0012698482,0.11945505,0.00066250266,0.00012668503,0.00033295367,0.00046490307,0.0037394315,0.0044574672],"genre_scores_gemma":[0.9445952,0.0006817774,0.049739014,0.00031751342,0.000030099074,0.0000821521,0.00041623684,0.0001191207,0.0040188264],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99779177,0.00032351824,0.000092753195,0.00035898545,0.0011548672,0.00027811038],"domain_scores_gemma":[0.9977064,0.0004855048,0.0004939359,0.00013488437,0.001065474,0.00011376117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001251573,0.0006924483,0.00063911953,0.0015424561,0.0007143909,0.0014487974,0.0012356427,0.0010344456,0.001665017],"category_scores_gemma":[0.0031459671,0.00044810175,0.0003583608,0.00079597713,0.00053135515,0.001988604,0.0015246151,0.0006160622,0.00072406256],"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":[0.00075844495,0.0003040463,0.16117702,0.0009147096,0.00008690121,0.0028576064,0.0034037384,0.00643857,0.5747596,0.0008311748,0.0049131443,0.24355507],"study_design_scores_gemma":[0.000087490975,0.003828259,0.18277188,0.0005951732,0.00026322884,0.004921478,0.009940545,0.11243867,0.6392535,0.0021389755,0.043386146,0.00037464596],"about_ca_topic_score_codex":0.002799441,"about_ca_topic_score_gemma":0.007238422,"teacher_disagreement_score":0.002799441,"about_ca_system_score_codex":0.00087831065,"about_ca_system_score_gemma":0.00070151396,"threshold_uncertainty_score":0.006619036},"labels":[],"label_agreement":null},{"id":"W4402060182","doi":"10.1061/9780784485583.018","title":"Statistical Analysis and Insight from the Water Research Foundation PCCP Failure Database 1942−2022","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"","keywords":"Foundation (evidence); Statistical analysis; Computer science; Database; History; Statistics; Mathematics; Archaeology","score_opus":0.024764532101428583,"score_gpt":0.2739662306335333,"score_spread":0.24920169853210472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402060182","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2826369,0.0004556971,0.03634365,0.0007220803,0.000071957504,0.0008391742,0.6679755,0.0019820782,0.008972862],"genre_scores_gemma":[0.4445413,0.0003194466,0.03184598,0.00014954267,0.000090750844,0.0020743776,0.5162762,0.00034700843,0.0043554525],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99366575,0.0018170909,0.00067037507,0.00078192627,0.0026635614,0.00040134107],"domain_scores_gemma":[0.9677096,0.020143615,0.0035033578,0.0036007045,0.0045747906,0.00046784885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006869348,0.0005202658,0.00055904634,0.0077082124,0.00034071296,0.00093184854,0.00062832393,0.00033809865,0.007885834],"category_scores_gemma":[0.030774226,0.0002090945,0.000578878,0.009049973,0.00033055994,0.0006135432,0.0008654922,0.00073483685,0.0019433736],"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":[0.0010318048,0.00060121086,0.58279616,0.00048045703,0.00057753024,0.000639991,0.0006257097,0.022388129,0.0018144487,0.00426943,0.20837443,0.17640077],"study_design_scores_gemma":[0.00016211909,0.0008982046,0.8265718,0.00016289632,0.00018177512,0.00062695163,0.0014523361,0.04992206,0.0027840093,0.0035701136,0.11356289,0.00010483],"about_ca_topic_score_codex":0.021296231,"about_ca_topic_score_gemma":0.019192263,"teacher_disagreement_score":0.021296231,"about_ca_system_score_codex":0.0007996548,"about_ca_system_score_gemma":0.0017807641,"threshold_uncertainty_score":0.04234457},"labels":[],"label_agreement":null},{"id":"W4402077470","doi":"10.3390/engproc2024069013","title":"Developing an Open Repository of Water Main Break Prediction Models in Kitchener","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Computer science","score_opus":0.019285768665378664,"score_gpt":0.21551753048753147,"score_spread":0.1962317618221528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402077470","genre_codex":"dataset","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":[0.03615664,0.0020746568,0.20707446,0.0013985808,0.0009797472,0.0005187416,0.48093772,0.25582546,0.015033994],"genre_scores_gemma":[0.09775784,0.0011262934,0.1097568,0.00040510716,0.0001145329,0.0007390179,0.7720172,0.0108824,0.007200871],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986493,0.00022792605,0.000153606,0.00047194914,0.00038990856,0.00010738829],"domain_scores_gemma":[0.9964592,0.0010483412,0.00022905915,0.001190414,0.00082689716,0.000246168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001851165,0.0019985074,0.0013661401,0.002811931,0.0005801387,0.0023102686,0.0055111554,0.0018951739,0.018669384],"category_scores_gemma":[0.009364396,0.001191554,0.0021437327,0.0031867402,0.00054777134,0.0034052182,0.0025252611,0.0025240425,0.019713815],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068202405,0.00086241745,0.018701812,0.0028742615,0.00068801007,0.0008068222,0.0003465978,0.3041614,0.0038164777,0.008312486,0.49855503,0.16019265],"study_design_scores_gemma":[0.00031830947,0.0002372481,0.010082934,0.00051696744,0.00017345099,0.00033579755,0.00024334238,0.6731127,0.009747383,0.020635081,0.28430188,0.00029503147],"about_ca_topic_score_codex":0.01062804,"about_ca_topic_score_gemma":0.019135686,"teacher_disagreement_score":0.98937196,"about_ca_system_score_codex":0.001017374,"about_ca_system_score_gemma":0.0022799997,"threshold_uncertainty_score":0.062455356},"labels":[],"label_agreement":null},{"id":"W4402128569","doi":"10.1080/1573062x.2024.2397783","title":"Adaptative water quality management in water distribution systems by optimizing control valves and chlorination booster stations","year":2024,"lang":"en","type":"article","venue":"Urban Water Journal","topic":"Water Systems and 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":"Natural Sciences and Engineering Research Council of Canada; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Booster (rocketry); Chlorine; Environmental science; Environmental engineering; Engineering; Chemistry","score_opus":0.009170681749712875,"score_gpt":0.2157052558020089,"score_spread":0.20653457405229603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402128569","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":[0.86865604,0.0002825401,0.1278335,0.00011262951,0.0000146847315,0.00014688775,0.000052452513,0.00019562528,0.0027056227],"genre_scores_gemma":[0.9939733,0.000045918634,0.005649226,0.0000063070092,0.0000015657806,0.000013393897,0.000010464397,0.0000035796972,0.00029622784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998037,0.00005453835,0.000010030462,0.00004674585,0.000045771372,0.000039264858],"domain_scores_gemma":[0.9998605,0.000053333002,0.000040715982,0.00001292568,0.00002269367,0.000009776291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039096188,0.00045065547,0.00026818147,0.0002895264,0.00015286845,0.000665343,0.0005615456,0.00031396377,0.00041152647],"category_scores_gemma":[0.000554501,0.00019302388,0.000269684,0.00025912514,0.00028389663,0.00041315478,0.00029959806,0.00025931865,0.000043057018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016710654,0.0002611329,0.0050915503,0.000106153966,0.000045035522,0.000109518536,0.000048172788,0.87324053,0.061697673,0.0009335355,0.00018654887,0.058113046],"study_design_scores_gemma":[0.000054152817,0.00047312974,0.0043006316,0.0000069570306,0.000046531746,0.000035249035,0.00007769034,0.96400684,0.029308528,0.0007504363,0.0009188941,0.000020928504],"about_ca_topic_score_codex":0.0066902456,"about_ca_topic_score_gemma":0.0081914775,"teacher_disagreement_score":0.0066902456,"about_ca_system_score_codex":0.0007652303,"about_ca_system_score_gemma":0.00054217514,"threshold_uncertainty_score":0.013302565},"labels":[],"label_agreement":null},{"id":"W4402263313","doi":"10.23919/acc60939.2024.10644804","title":"Average Cost Optimality of Partially Observed MDPs: Contraction of Non-Linear Filters and Existence of Optimal Solutions and Approximations","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Queen's University","funders":"","keywords":"Contraction (grammar); Mathematical optimization; Mathematics; Computer science; Applied mathematics","score_opus":0.0407724179510444,"score_gpt":0.2414977932169485,"score_spread":0.20072537526590412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402263313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020429363,0.00017525213,0.976747,0.00062635285,0.000026829484,0.000029892739,0.000065400236,0.000064140724,0.0018357247],"genre_scores_gemma":[0.8642483,0.0005976794,0.12988436,0.0003252571,0.00008734471,0.00028844035,0.00020434514,0.00015696864,0.004207302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977894,0.0009081964,0.00014198468,0.00047369747,0.00043679753,0.00024994678],"domain_scores_gemma":[0.97535783,0.020552225,0.0016035881,0.0008522194,0.0011362449,0.00049783295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056989184,0.0012552943,0.0023953004,0.00085774745,0.00061242504,0.0020598813,0.0016951406,0.0020935454,0.0025896488],"category_scores_gemma":[0.031727582,0.00083625404,0.0012974537,0.0006298856,0.003275438,0.003932995,0.0027255646,0.0030276508,0.00018904766],"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":[0.00008844541,0.000052681924,0.0008356353,0.0001644486,0.0000626861,0.0000959469,0.00018216329,0.7223402,0.0013792047,0.26461482,0.00063156453,0.009552142],"study_design_scores_gemma":[0.0000079069705,0.000030721876,0.00009834657,0.000022686812,0.0000063228863,0.000017687978,0.000020465688,0.9451173,0.0003649378,0.05408025,0.00022461788,0.000008706174],"about_ca_topic_score_codex":0.0027762107,"about_ca_topic_score_gemma":0.00133381,"teacher_disagreement_score":0.0056989184,"about_ca_system_score_codex":0.0021248965,"about_ca_system_score_gemma":0.002680408,"threshold_uncertainty_score":0.030139148},"labels":[],"label_agreement":null},{"id":"W4402420334","doi":"10.3390/engproc2024069093","title":"Investigating the Efficacy of Topological Methods for Optimal Sensor Placement in Water Distribution Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Computer science; Topology (electrical circuits); Distribution (mathematics); Mathematics; Mathematical analysis","score_opus":0.02845561741353976,"score_gpt":0.2998455449872002,"score_spread":0.2713899275736604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402420334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39988372,0.0014058643,0.5895829,0.00065726595,0.00016361564,0.00012172479,0.00013150334,0.0002882428,0.0077652195],"genre_scores_gemma":[0.95932835,0.00034800026,0.039672293,0.000033607634,0.000023362662,0.000037084148,0.00005966718,0.00003849241,0.00045912756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990545,0.0005402704,0.000041604504,0.000096245385,0.00016567326,0.00010175995],"domain_scores_gemma":[0.98926497,0.008690698,0.0007335295,0.0003436792,0.00075394457,0.00021313553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025061164,0.0010998998,0.00059369113,0.0014442296,0.0004969984,0.0008438995,0.00084833015,0.0009570455,0.0014427113],"category_scores_gemma":[0.015565933,0.00036867958,0.00048814903,0.00095966365,0.00068948243,0.0019242184,0.0011259371,0.0005066512,0.00014511442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006152404,0.000035798745,0.0007292789,0.00006424194,0.000019051668,0.000017453187,0.000020307218,0.9854159,0.0005797293,0.0036223219,0.0001263913,0.009308035],"study_design_scores_gemma":[0.0000059485897,0.000067035726,0.00019460685,0.0000068757063,0.0000066310195,0.0000074568993,0.00002936802,0.9976338,0.0003239999,0.0016273819,0.00009304183,0.000003831834],"about_ca_topic_score_codex":0.0046835425,"about_ca_topic_score_gemma":0.0030745454,"teacher_disagreement_score":0.0046835425,"about_ca_system_score_codex":0.000913787,"about_ca_system_score_gemma":0.0009460033,"threshold_uncertainty_score":0.013253808},"labels":[],"label_agreement":null},{"id":"W4402438613","doi":"10.11159/htff24.238","title":"Experimental Investigation of the Transition from the Segregated Flow Regime to the Intermittent Flow Regime in a Horizontal Pipe","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Water Systems and 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":"Campus France","keywords":"Mechanics; Flow (mathematics); Pipe flow; Geology; Materials science; Physics; Turbulence","score_opus":0.006048439796777316,"score_gpt":0.18002328685146976,"score_spread":0.17397484705469243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402438613","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":[0.996102,0.00009032519,0.0024100884,0.000037656588,0.000013231381,0.000017626933,0.00010965075,0.00003984804,0.0011795713],"genre_scores_gemma":[0.9983359,0.000057346548,0.00088575104,0.000009123043,0.0000070469246,0.000012482772,0.00005934583,0.000008172471,0.0006249741],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998591,0.000017385235,0.0000066639936,0.0000431338,0.000037499216,0.000036202993],"domain_scores_gemma":[0.99946135,0.00024530335,0.00008256338,0.00007591667,0.00009249298,0.000042449356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023840391,0.00013778963,0.00017722069,0.00018915764,0.00039797928,0.00028505773,0.0003717588,0.00040804507,0.0032756312],"category_scores_gemma":[0.00060162833,0.00019022604,0.00013757996,0.00021021957,0.0006957607,0.00037608805,0.00029302062,0.00048284003,0.0002345778],"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":[0.00047313463,0.000286352,0.0016263135,0.000074769865,0.000006587223,0.00012354326,0.00015945932,0.000904794,0.9924482,0.00046233894,0.00014199536,0.0032924623],"study_design_scores_gemma":[0.000093560426,0.0032317715,0.024244685,0.00001691254,0.000023215543,0.00015424771,0.00035620277,0.017912574,0.95211434,0.00040019804,0.0014249748,0.000027282325],"about_ca_topic_score_codex":0.0005528403,"about_ca_topic_score_gemma":0.00049941224,"teacher_disagreement_score":0.0032756312,"about_ca_system_score_codex":0.00016972184,"about_ca_system_score_gemma":0.00018147311,"threshold_uncertainty_score":0.0109580755},"labels":[],"label_agreement":null},{"id":"W4402546194","doi":"10.2166/hydro.2024.108","title":"Investigating water quality dynamics in distribution networks with dynamically adaptive connectivity","year":2024,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Bristol Water; Imperial College London; Royal Academy of Engineering","keywords":"Controllability; Flexibility (engineering); Computer science; Water quality; Quality (philosophy); Observability; Control (management); Environmental science; Mathematics; Ecology","score_opus":0.0072272629605955345,"score_gpt":0.2043366592848137,"score_spread":0.19710939632421817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402546194","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":[0.9865492,0.000028332408,0.012506713,0.00010240999,0.0000042754987,0.000011685904,0.00008909515,0.00003481365,0.00067351584],"genre_scores_gemma":[0.9992879,0.000007613248,0.0005983302,0.000003770499,6.100797e-7,0.0000037750756,0.000018431485,0.0000013406038,0.00007827978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997924,0.000077255005,0.000006298239,0.000045956043,0.00002972118,0.000048418213],"domain_scores_gemma":[0.9986395,0.000873728,0.0002476441,0.00007819028,0.00010439891,0.000056587105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039632537,0.00027919497,0.00021949332,0.00040439263,0.0002222118,0.0004976613,0.0004384966,0.00048554284,0.00057253294],"category_scores_gemma":[0.0019698706,0.00013944169,0.00022961287,0.00035604095,0.00064563233,0.0007828675,0.0003759676,0.00042438894,0.000029460738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004961409,0.00002491054,0.006019696,0.000007735125,0.000013527193,0.000057964215,0.000020102803,0.98970056,0.0020069913,0.0007019864,0.000058295565,0.0013387256],"study_design_scores_gemma":[0.0000061931473,0.000045372453,0.0025530553,0.0000013052326,0.000003523728,0.000010408034,0.000034993773,0.99627835,0.00051845855,0.0004886433,0.0000556989,0.000003992503],"about_ca_topic_score_codex":0.012870397,"about_ca_topic_score_gemma":0.007068131,"teacher_disagreement_score":0.012870397,"about_ca_system_score_codex":0.0010007394,"about_ca_system_score_gemma":0.00028501914,"threshold_uncertainty_score":0.025590956},"labels":[],"label_agreement":null},{"id":"W4402571999","doi":"10.1109/phm61473.2024.00063","title":"Can graph neural networks outperform in supervised classification of noisy acoustic signals? An industrial case study of hydroelectric generator","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 de Montréal; Mila - Quebec Artificial Intelligence Institute; Hydro-Québec","funders":"","keywords":"Hydroelectricity; Generator (circuit theory); Computer science; Graph; Artificial neural network; Artificial intelligence; Signal generator; Speech recognition; Pattern recognition (psychology); Machine learning; Electrical engineering; Theoretical computer science; Engineering; Telecommunications","score_opus":0.03302522523803908,"score_gpt":0.22775679471379945,"score_spread":0.19473156947576037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402571999","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":[0.8131024,0.0034434206,0.16923855,0.002666089,0.00045496845,0.00014091979,0.0005319724,0.0014735342,0.008948076],"genre_scores_gemma":[0.97673166,0.00028344555,0.021018052,0.0001584246,0.000057540554,0.000020185504,0.0004068333,0.000050346534,0.0012736229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904734,0.0004318986,0.000038398746,0.00022266418,0.00015524405,0.00010446655],"domain_scores_gemma":[0.99694675,0.0019176619,0.0002398382,0.00027130276,0.0004979558,0.00012652176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026201247,0.0011402722,0.00070892856,0.0009215758,0.00036187854,0.00091995153,0.00073257036,0.0014353012,0.0011268745],"category_scores_gemma":[0.007953593,0.00017152395,0.0004756056,0.0005663057,0.0006695074,0.0014778867,0.000528179,0.00092212873,0.00046821136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011744965,0.00054224255,0.01997725,0.0005099847,0.00030305472,0.000573383,0.00015454867,0.66249454,0.0086333165,0.0047055483,0.0070233927,0.29390818],"study_design_scores_gemma":[0.000015273241,0.00018316488,0.0024856688,0.000023453358,0.000027337086,0.000048054688,0.00007130492,0.98933685,0.0026676476,0.004419139,0.00071096676,0.000011239445],"about_ca_topic_score_codex":0.004700832,"about_ca_topic_score_gemma":0.007431084,"teacher_disagreement_score":0.004700832,"about_ca_system_score_codex":0.00059698505,"about_ca_system_score_gemma":0.00053613994,"threshold_uncertainty_score":0.013856769},"labels":[],"label_agreement":null},{"id":"W4402751381","doi":"10.1007/s11269-024-03985-8","title":"Distributed Nonconvex Optimization for Control of Water Networks with Time-coupling Constraints","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and 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":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Bristol Water; Imperial College London; Royal Academy of Engineering","keywords":"Convergence (economics); Mathematical optimization; Computer science; Optimization problem; Nonlinear system; Set (abstract data type); Benchmarking; Scale (ratio); Coupling (piping); Hydrogeology; Mathematics; Engineering","score_opus":0.00352064870132687,"score_gpt":0.16487091908563084,"score_spread":0.16135027038430397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402751381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00656302,0.00022115868,0.9906322,0.0002617096,0.000043131236,0.00002960846,0.000041138454,0.00008924931,0.00211888],"genre_scores_gemma":[0.82816637,0.00057841325,0.16164048,0.00029620164,0.00010539083,0.00040022912,0.00022857898,0.00015399446,0.00843029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992586,0.00026097498,0.000022809198,0.00018633546,0.00019100985,0.00008039273],"domain_scores_gemma":[0.9989104,0.0006744744,0.00015955235,0.00006844007,0.00013504252,0.00005209795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014883397,0.0013551125,0.0012206818,0.0004057099,0.00043889764,0.0014628508,0.0011077863,0.0015701364,0.0024349517],"category_scores_gemma":[0.0025185992,0.00049795484,0.00075776794,0.0006375162,0.00161149,0.0013192677,0.0016046942,0.0018623591,0.00021893503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000954034,0.000009231035,0.000049771184,0.000023060438,0.00000662031,0.000014873043,0.000009300854,0.9919701,0.00030817385,0.0052284063,0.00019883219,0.0021721057],"study_design_scores_gemma":[0.0000036460337,0.00000569689,0.000015136753,0.0000012376598,8.9235283e-7,0.0000015659996,0.0000022107356,0.9983183,0.0000635883,0.0014211354,0.00016565179,0.0000010374946],"about_ca_topic_score_codex":0.007910696,"about_ca_topic_score_gemma":0.0060306005,"teacher_disagreement_score":0.007910696,"about_ca_system_score_codex":0.0014368664,"about_ca_system_score_gemma":0.0018471376,"threshold_uncertainty_score":0.015729308},"labels":[],"label_agreement":null},{"id":"W4402808469","doi":"10.1007/978-3-031-62867-2_9","title":"Experiences on Use of State Estimator at Hydro One","year":2024,"lang":"en","type":"book-chapter","venue":"Power electronics and power systems","topic":"Water Systems and 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":"Hydro One (Canada); Barrie Urology Group","funders":"","keywords":"State (computer science); Estimator; State estimator; Environmental science; Computer science; Statistics; Mathematics; Algorithm","score_opus":0.011692196465932857,"score_gpt":0.19007548228162924,"score_spread":0.17838328581569637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402808469","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":[0.32754654,0.004886487,0.24897297,0.003745944,0.00064830726,0.00025444652,0.0006747147,0.0022005492,0.4110701],"genre_scores_gemma":[0.7090237,0.004966185,0.1131362,0.001191886,0.00027819263,0.00007728248,0.0009978416,0.001317778,0.16901088],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987166,0.00052531203,0.000053805128,0.00016261895,0.00040879828,0.00013278928],"domain_scores_gemma":[0.9976398,0.0011010144,0.000044766715,0.00039657523,0.0005948166,0.00022319134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030544372,0.00047577813,0.0003419942,0.0007386858,0.0009327569,0.0016592123,0.0013208846,0.0011332845,0.012206632],"category_scores_gemma":[0.003755121,0.00030745947,0.0003838337,0.0011079261,0.00092656317,0.0030117903,0.0017278913,0.0014910189,0.0035961696],"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":[0.00050527154,0.001187031,0.014889173,0.00035421323,0.00005839201,0.0041248584,0.013708645,0.008918692,0.024664044,0.023495996,0.020748796,0.8873449],"study_design_scores_gemma":[0.00008836845,0.0024085273,0.019002542,0.00037025704,0.00013946489,0.0047978475,0.010420673,0.024093868,0.088254035,0.0157756,0.8344318,0.00021702352],"about_ca_topic_score_codex":0.003970244,"about_ca_topic_score_gemma":0.0070809936,"teacher_disagreement_score":0.012206632,"about_ca_system_score_codex":0.000630585,"about_ca_system_score_gemma":0.0006150447,"threshold_uncertainty_score":0.04083532},"labels":[],"label_agreement":null},{"id":"W4402901213","doi":"10.3390/modelling5040069","title":"Novel Adaptive Hidden Markov Model Utilizing Expectation–Maximization Algorithm for Advanced Pipeline Leak Detection","year":2024,"lang":"en","type":"article","venue":"Modelling—International Open Access Journal of Modelling in Engineering Science","topic":"Water Systems and 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 Waterloo","funders":"","keywords":"Computer science; Leak detection; Pipeline (software); Expectation–maximization algorithm; Leak; Maximization; Markov chain; Hidden Markov model; Algorithm; Forward algorithm; Markov model; Machine learning; Artificial intelligence; Maximum likelihood; Variable-order Markov model; Mathematical optimization; Engineering; Mathematics; Statistics; Programming language","score_opus":0.05123299499623737,"score_gpt":0.3072032868765379,"score_spread":0.25597029188030057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402901213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008874884,0.00032367624,0.98947084,0.00014577757,0.000040944164,0.00001817245,0.000045604786,0.0004306521,0.0006494357],"genre_scores_gemma":[0.63377315,0.00076961174,0.35902247,0.0002954393,0.00012397046,0.00018070533,0.00050891086,0.0001575713,0.0051681856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994844,0.0001537934,0.000033794815,0.00012806464,0.0001221702,0.00007773479],"domain_scores_gemma":[0.9990301,0.00066548696,0.00008036002,0.000029933963,0.00015835444,0.000035716483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010692491,0.00070266775,0.0011740023,0.0005297993,0.00036358816,0.00078177755,0.0013794341,0.0010193038,0.001933718],"category_scores_gemma":[0.002305382,0.00053017994,0.0011170466,0.00058309257,0.00038984758,0.0011517269,0.0007676314,0.0014703617,0.00059022894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025179025,0.00015130534,0.0033988766,0.00016283474,0.00014767032,0.00025479836,0.00012102719,0.8134625,0.0060807476,0.011198546,0.0027301626,0.16203979],"study_design_scores_gemma":[0.0000030149413,0.0000078260255,0.00006427015,0.00000202502,0.0000036667082,0.000012363828,0.0000022472054,0.99872774,0.00021310007,0.0008458608,0.00011455131,0.0000032959092],"about_ca_topic_score_codex":0.009290615,"about_ca_topic_score_gemma":0.007184514,"teacher_disagreement_score":0.009290615,"about_ca_system_score_codex":0.0005204451,"about_ca_system_score_gemma":0.0012426387,"threshold_uncertainty_score":0.01847303},"labels":[],"label_agreement":null},{"id":"W4403265355","doi":"10.1007/978-3-031-61519-1_3","title":"Multi-pump Station Basin-Level Wastewater System Model Calibration","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Water Systems and 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 Lethbridge; Stantec (Canada)","funders":"","keywords":"Calibration; Environmental science; Structural basin; Wastewater; Hydrology (agriculture); Water resource management; Geology; Environmental engineering; Geomorphology; Geotechnical engineering; Mathematics; Statistics","score_opus":0.016376346965556125,"score_gpt":0.1905519490404858,"score_spread":0.1741756020749297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403265355","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":[0.79485536,0.00025024434,0.15915653,0.00046681965,0.00010015136,0.00026392835,0.018751105,0.0047341683,0.021421634],"genre_scores_gemma":[0.9653652,0.00009480564,0.025939753,0.00006090891,0.000006501642,0.000204898,0.003719534,0.00022423285,0.0043843854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980277,0.00004300581,0.000013943926,0.000060797844,0.00005673747,0.0000227605],"domain_scores_gemma":[0.99965656,0.0001064356,0.000022268663,0.00008256943,0.00012125247,0.000010984057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000546017,0.0004684897,0.00063600054,0.0002800171,0.0004501767,0.00060856756,0.001254233,0.00089955877,0.0050915796],"category_scores_gemma":[0.0010767151,0.0005911906,0.0007177708,0.0007434659,0.00020764854,0.0011919701,0.00050856697,0.0008729218,0.0012888525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010169355,0.000069417925,0.0021065646,0.000063699525,0.000043093693,0.00001621101,0.000016709007,0.97874635,0.0036456138,0.0005085322,0.0015039972,0.013178209],"study_design_scores_gemma":[0.0000453344,0.00003811773,0.0034008408,0.0000046864798,0.000016658409,0.0000056740596,0.000010831308,0.99111664,0.0039574965,0.0004757747,0.0009116954,0.000016207883],"about_ca_topic_score_codex":0.0273686,"about_ca_topic_score_gemma":0.028661007,"teacher_disagreement_score":0.0273686,"about_ca_system_score_codex":0.0011554357,"about_ca_system_score_gemma":0.0014677857,"threshold_uncertainty_score":0.054418623},"labels":[],"label_agreement":null},{"id":"W4403318357","doi":"10.3846/jcem.2024.21744","title":"Planning municipal drainage infrastructure maintenance operations with finite available crews: pragmatic optimization approach","year":2024,"lang":"en","type":"article","venue":"Journal of Civil Engineering and Management","topic":"Water Systems and 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":"Alberta Environment and Protected Areas; University of Alberta","funders":"","keywords":"Computer science; Operations research; Engineering","score_opus":0.004947627403737697,"score_gpt":0.17658714142609433,"score_spread":0.17163951402235664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403318357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034995865,0.00019945302,0.9527981,0.00092398515,0.000022211761,0.00024298175,0.000085467414,0.00009635963,0.010635536],"genre_scores_gemma":[0.61987907,0.00042946707,0.37549353,0.00014350268,0.000040006864,0.0005554225,0.00010851972,0.000080521124,0.0032700219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862194,0.00078800187,0.000044883367,0.00014766783,0.0002742929,0.00012324573],"domain_scores_gemma":[0.9978662,0.0016802623,0.00015022604,0.00008518238,0.00012475978,0.0000933703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021633347,0.0008791559,0.001157842,0.0008506054,0.00073296594,0.0023509269,0.0017800141,0.0016128193,0.0034778174],"category_scores_gemma":[0.005884454,0.0011748099,0.00084534293,0.00088985066,0.001345064,0.0018758435,0.001725796,0.0014941698,0.00023765114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052095078,0.000081100174,0.0004493394,0.00012882349,0.00003690774,0.00015147461,0.00017239113,0.957043,0.00074545946,0.024131745,0.00049002504,0.01651769],"study_design_scores_gemma":[0.000034239198,0.00006165901,0.00021063813,0.000026066073,0.000015702826,0.000035573874,0.00018361604,0.9808251,0.00027881953,0.016918756,0.0013961574,0.000013670226],"about_ca_topic_score_codex":0.010201184,"about_ca_topic_score_gemma":0.011729518,"teacher_disagreement_score":0.010201184,"about_ca_system_score_codex":0.0019619912,"about_ca_system_score_gemma":0.0038684793,"threshold_uncertainty_score":0.02028364},"labels":[],"label_agreement":null},{"id":"W4403357045","doi":"10.21608/ajcit.2024.309281.1002","title":"Intelligent Waterproofing System","year":2024,"lang":"en","type":"article","venue":"Al-Ryada Journal for Computational Intelligence & Technology","topic":"Water Systems and 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":"Sciencetech (Canada)","funders":"","keywords":"Waterproofing; Computer science; Engineering; Civil engineering","score_opus":0.014236923349602366,"score_gpt":0.2596140316423741,"score_spread":0.24537710829277176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403357045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061498594,0.0013859288,0.88552016,0.00067134545,0.00038164438,0.00036011892,0.00047311466,0.014233258,0.03547578],"genre_scores_gemma":[0.8641668,0.00074668834,0.111408204,0.00037843897,0.00011962143,0.00023853693,0.0007304383,0.00018142573,0.022029873],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960095,0.00003302891,0.000026132306,0.0001535976,0.00013145496,0.00005489335],"domain_scores_gemma":[0.99973816,0.000041497286,0.000036956208,0.000052420375,0.00010750732,0.00002353019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002815529,0.0007586509,0.0008004679,0.0008458432,0.0008212872,0.0014379033,0.0016655242,0.0011758758,0.007818485],"category_scores_gemma":[0.00077651197,0.00029813434,0.000523158,0.00045537626,0.0003857156,0.0018818581,0.0010403318,0.00050593086,0.0032497516],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064054946,0.00035219875,0.0053942017,0.00057178544,0.00011276251,0.0007457993,0.00035936385,0.1331213,0.12926835,0.017208593,0.021260245,0.69096476],"study_design_scores_gemma":[0.00005927752,0.00032348646,0.0022724676,0.00005070188,0.00010785572,0.0004651355,0.00011710798,0.89355874,0.045864217,0.009625166,0.047484294,0.00007154709],"about_ca_topic_score_codex":0.002181674,"about_ca_topic_score_gemma":0.0014875571,"teacher_disagreement_score":0.007818485,"about_ca_system_score_codex":0.00060783955,"about_ca_system_score_gemma":0.0006764966,"threshold_uncertainty_score":0.026155472},"labels":[],"label_agreement":null},{"id":"W4403647939","doi":"10.3390/engproc2024069204","title":"Examining the Effect of Small-Amplitude Transients on the Shear Strength of Biofilms in Water Distribution Pipes","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Queen's University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Amplitude; Distribution (mathematics); Materials science; Shear (geology); Mechanics; Shear strength (soil); Geotechnical engineering; Geology; Composite material; Physics; Optics; Soil science; Mathematics","score_opus":0.012188800919268158,"score_gpt":0.19376844256449827,"score_spread":0.1815796416452301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403647939","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":[0.9989568,0.000096631426,0.0007587973,0.00001994882,0.000005344386,0.000007188688,0.00002424813,0.0000074084196,0.00012343847],"genre_scores_gemma":[0.9992785,0.000090770816,0.0004803157,0.00000922549,0.0000025581714,0.0000064263254,0.000019669656,0.000002498111,0.000110224195],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998616,0.000021352447,0.000010889966,0.000029531671,0.000047414596,0.000029240413],"domain_scores_gemma":[0.99952865,0.0002700735,0.00007038129,0.000021483555,0.00006519767,0.000044162392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033441724,0.00019239215,0.00027788096,0.00015817658,0.00023005025,0.0003205879,0.00014757439,0.0002837183,0.000784053],"category_scores_gemma":[0.0008550362,0.00013053023,0.00016712524,0.00009817727,0.0003158525,0.00029794395,0.00026760728,0.0003308705,0.00007184937],"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":[0.00018780073,0.000037003087,0.0023225108,0.000044506553,0.0000051110815,0.000040708444,0.00005946192,0.00080852595,0.99351007,0.000024501156,0.000017431146,0.0029422974],"study_design_scores_gemma":[0.00001675485,0.0018109205,0.059105072,0.000011760325,0.000023381373,0.00006758963,0.00021022838,0.011497632,0.9268192,0.00006836816,0.00035210687,0.00001684886],"about_ca_topic_score_codex":0.00088599056,"about_ca_topic_score_gemma":0.000958563,"teacher_disagreement_score":0.00088599056,"about_ca_system_score_codex":0.00024278868,"about_ca_system_score_gemma":0.00018718727,"threshold_uncertainty_score":0.0026229024},"labels":[],"label_agreement":null},{"id":"W4403777234","doi":"10.1016/j.watres.2024.122701","title":"Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques","year":2024,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and 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 British Columbia, Okanagan Campus; Okanagan University College","funders":"","keywords":"Term (time); Water quality; Quality (philosophy); Machine learning; Environmental science; Base (topology); Computer science; Artificial intelligence; Mathematics; Ecology","score_opus":0.06157083379015968,"score_gpt":0.33587102905433996,"score_spread":0.27430019526418026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403777234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1187085,0.00006158477,0.8790188,0.00014391323,0.000015003655,0.000026791275,0.00011235734,0.0006016573,0.0013115051],"genre_scores_gemma":[0.9382089,0.00004014083,0.061052855,0.000030021978,0.000011113589,0.000027740325,0.00014811505,0.000034543384,0.00044665343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997203,0.00008877273,0.000017546361,0.000066309134,0.00007756398,0.000029572728],"domain_scores_gemma":[0.9991897,0.0003727666,0.00012236233,0.00011235293,0.00017720272,0.000025588824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082603603,0.0004472242,0.000405152,0.00039156404,0.0002560682,0.0005494297,0.00088948273,0.00046666965,0.00038004664],"category_scores_gemma":[0.0021198753,0.0002939112,0.00059333397,0.00032613683,0.0003816552,0.0010760367,0.0006787789,0.0006171801,0.00008967164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009306339,0.000021003083,0.0013070134,0.000006440205,0.000015964519,0.000011117313,0.000018500823,0.98825294,0.0008983045,0.00074135256,0.00006682199,0.008651393],"study_design_scores_gemma":[5.6183e-7,0.0000034317245,0.00016530593,6.2136274e-7,0.000001164252,0.0000017540897,0.0000018211123,0.99926645,0.00020038274,0.00031088555,0.00004628545,0.0000014411667],"about_ca_topic_score_codex":0.018682234,"about_ca_topic_score_gemma":0.01567262,"teacher_disagreement_score":0.018682234,"about_ca_system_score_codex":0.0008117676,"about_ca_system_score_gemma":0.000993723,"threshold_uncertainty_score":0.037146986},"labels":[],"label_agreement":null},{"id":"W4403861335","doi":"10.14796/jwmm.h527","title":"Analysis of Optimal Solutions of a Benchmark Water Distribution Network for Exploring the Global Optimality of the Current-best Solution","year":2024,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Benchmark (surveying); Current (fluid); Computer science; Mathematical optimization; Distribution (mathematics); Mathematics; Engineering; Geography; Electrical engineering; Cartography","score_opus":0.03892386188104321,"score_gpt":0.24390250068037453,"score_spread":0.20497863879933131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403861335","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":[0.8524981,0.0014758902,0.12235929,0.0005312784,0.00007179464,0.0001732489,0.00094784884,0.0002664032,0.021676118],"genre_scores_gemma":[0.97057253,0.0002487783,0.027370118,0.000025779487,0.000007016642,0.00008870987,0.0005017572,0.000027848659,0.0011574711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996865,0.000101227124,0.000012401843,0.000043232103,0.000090922265,0.00006572097],"domain_scores_gemma":[0.9987464,0.00079343637,0.000107292166,0.0000555337,0.0002585032,0.000038911123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009982816,0.0008786153,0.00067464745,0.0016980497,0.00041213105,0.00082207297,0.0005751964,0.0009883747,0.0019019599],"category_scores_gemma":[0.0039791786,0.00019846747,0.0006161941,0.0010448113,0.0004385415,0.00063436665,0.0003703741,0.0004604402,0.00011083385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006383525,0.000049745337,0.0011429377,0.00013800069,0.0000339039,0.00010176522,0.000033209923,0.98670006,0.0011757262,0.002884723,0.0005230183,0.0071531576],"study_design_scores_gemma":[0.000011221689,0.000097099844,0.001174586,0.0000226192,0.00002013974,0.00002839839,0.00009173164,0.99450594,0.0018094843,0.0017317514,0.0004996239,0.0000073344063],"about_ca_topic_score_codex":0.008594248,"about_ca_topic_score_gemma":0.005763634,"teacher_disagreement_score":0.008594248,"about_ca_system_score_codex":0.0011009334,"about_ca_system_score_gemma":0.0007329803,"threshold_uncertainty_score":0.017088413},"labels":[],"label_agreement":null},{"id":"W4404039352","doi":"10.3390/en17215517","title":"Gas Pipeline Leak Detection by Integrating Dynamic Modeling and Machine Learning Under the Transient State","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"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":"Korea Institute of Energy Technology Evaluation and Planning; National Research Foundation of Korea; Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy; National Research Foundation","keywords":"Transient (computer programming); Pipeline (software); Leak detection; Leak; Computer science; State (computer science); Transient state; Transient analysis; Gas pipeline; Engineering; Transient response; Petroleum engineering; Electrical engineering; Operating system; Algorithm","score_opus":0.005648823596694782,"score_gpt":0.18308683067368783,"score_spread":0.17743800707699306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404039352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39337903,0.00016537191,0.6005762,0.00023871912,0.00002670039,0.00007462816,0.00010977834,0.0017447794,0.0036847843],"genre_scores_gemma":[0.98399884,0.0000454565,0.015136331,0.000016654109,0.000004580417,0.000025860276,0.00006614708,0.000021115964,0.0006849962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998505,0.000033178036,0.000009200443,0.00004521186,0.000037410773,0.000024519777],"domain_scores_gemma":[0.9995122,0.00024773454,0.00008101848,0.000037766815,0.000106486215,0.000014767145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048581412,0.0007088515,0.00046321735,0.00043983685,0.00024793018,0.00076991227,0.0004017707,0.0005291833,0.0005389358],"category_scores_gemma":[0.0015205295,0.0003612655,0.000476453,0.00025022397,0.00032814004,0.0009439614,0.00044153022,0.000572692,0.00013496303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033694938,0.000037653925,0.0028115904,0.000016594111,0.000014924996,0.000022843835,0.000021855441,0.9797005,0.0018581607,0.0003913315,0.00009659861,0.014994226],"study_design_scores_gemma":[4.4452284e-7,0.000006969288,0.00017836741,7.648592e-7,0.0000010024091,0.0000016387249,0.0000019520824,0.9993863,0.00030784082,0.00008639065,0.000026985805,0.0000013418334],"about_ca_topic_score_codex":0.019928034,"about_ca_topic_score_gemma":0.012365899,"teacher_disagreement_score":0.019928034,"about_ca_system_score_codex":0.0008251966,"about_ca_system_score_gemma":0.0010628408,"threshold_uncertainty_score":0.039624095},"labels":[],"label_agreement":null},{"id":"W4404169668","doi":"10.2139/ssrn.5014925","title":"Randomness and Stochastic Modeling of Combined Sewer Overflows","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Water Systems and 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","funders":"","keywords":"Combined sewer; Randomness; Computer science; Mathematics; Stormwater; Statistics; Surface runoff; Ecology","score_opus":0.005729754449615679,"score_gpt":0.19264562879104444,"score_spread":0.18691587434142876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404169668","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":[0.5480045,0.0014745746,0.43652564,0.002247867,0.00027833224,0.000070231275,0.0008305394,0.00040964075,0.01015865],"genre_scores_gemma":[0.9925649,0.00030530314,0.0027720444,0.000046400848,0.000083151965,0.00002977285,0.00015920088,0.000047398855,0.0039917533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987161,0.00046992843,0.00007141347,0.00028321636,0.00021278708,0.0002465496],"domain_scores_gemma":[0.99296594,0.004703267,0.001196344,0.00031061817,0.00043984246,0.00038391943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002196653,0.0008065842,0.0018827557,0.0015438735,0.0006629868,0.002547778,0.0020716528,0.0025928149,0.0024587908],"category_scores_gemma":[0.011953623,0.0011809629,0.0015206648,0.0013633962,0.0022622223,0.003273961,0.0015046349,0.0015032156,0.00021388715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055082117,0.000020294445,0.0010850311,0.000022571168,0.0000410189,0.00007966196,0.000027509808,0.94979346,0.0004822997,0.046667177,0.00044557155,0.0012802922],"study_design_scores_gemma":[0.0000068846216,0.000009482529,0.00038191662,0.0000033629851,0.000007440801,0.000011378243,0.000008889875,0.98486334,0.000069401176,0.014528347,0.00009691062,0.000012672544],"about_ca_topic_score_codex":0.008875985,"about_ca_topic_score_gemma":0.0063502816,"teacher_disagreement_score":0.008875985,"about_ca_system_score_codex":0.0016863099,"about_ca_system_score_gemma":0.0009837843,"threshold_uncertainty_score":0.017648637},"labels":[],"label_agreement":null},{"id":"W4404733055","doi":"10.1016/j.ymssp.2024.112171","title":"A hybrid prognostic framework: Stochastic degradation process with adaptive trajectory learning to transfer historical health knowledge","year":2024,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Water Systems and 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":"","keywords":"Trajectory; Degradation (telecommunications); Process (computing); Knowledge transfer; Transfer of learning; Computer science; Stochastic process; Artificial intelligence; Control theory (sociology); Machine learning; Mathematics; Knowledge management; Physics; Statistics","score_opus":0.018141884810950942,"score_gpt":0.2331501855473804,"score_spread":0.21500830073642946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404733055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033944005,0.00014274435,0.9956748,0.00007820067,0.000030980227,0.000014050552,0.000041273834,0.00015045353,0.00047312854],"genre_scores_gemma":[0.78174204,0.00059506873,0.21184844,0.00017817161,0.00020982457,0.00019735534,0.00032965714,0.000096784075,0.0048026303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952984,0.00010808945,0.000036785936,0.00013588819,0.00013864845,0.00005069429],"domain_scores_gemma":[0.9992969,0.00028426974,0.00010098128,0.00007569693,0.00019976625,0.000042352127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012697314,0.0007589351,0.0011784048,0.000613362,0.00029818295,0.0010438871,0.001758402,0.0014671058,0.0020943037],"category_scores_gemma":[0.0027322685,0.00046661694,0.00067424576,0.00081274414,0.00065768434,0.0014750959,0.0014749574,0.0011819499,0.00044366275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006742961,0.000051568004,0.00034196343,0.000076664604,0.000054931665,0.00004965001,0.00004069499,0.9285616,0.0017332043,0.01080665,0.00071732077,0.057498448],"study_design_scores_gemma":[0.0000027985036,0.000011927723,0.0000380629,0.0000022207407,0.0000038277326,0.000006520811,0.0000010429407,0.99836296,0.00013493451,0.0013066701,0.00012572772,0.000003398717],"about_ca_topic_score_codex":0.004888875,"about_ca_topic_score_gemma":0.0035522408,"teacher_disagreement_score":0.004888875,"about_ca_system_score_codex":0.00049437536,"about_ca_system_score_gemma":0.0011089239,"threshold_uncertainty_score":0.009720802},"labels":[],"label_agreement":null},{"id":"W4404752188","doi":"10.1038/s41545-024-00406-6","title":"Contrastive learning method for leak detection in water distribution networks","year":2024,"lang":"en","type":"article","venue":"npj Clean Water","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Leak; Leak detection; Computer science; Distribution (mathematics); Artificial intelligence; Environmental science; Mathematics; Environmental engineering","score_opus":0.005612379125307662,"score_gpt":0.2122003555461294,"score_spread":0.20658797642082175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404752188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02944361,0.0002414454,0.96867126,0.00013523197,0.000026487447,0.00003886966,0.00004478404,0.00046051032,0.0009377376],"genre_scores_gemma":[0.7556557,0.00032760476,0.24015008,0.00022323687,0.00006366086,0.0001262363,0.00023910406,0.00009808518,0.0031162368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965465,0.00008779217,0.000017879267,0.00011002504,0.0000888477,0.000040790837],"domain_scores_gemma":[0.99922776,0.00040670505,0.000089843,0.000058537884,0.00018686302,0.000030228279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009536285,0.0007998145,0.00062191155,0.0007982019,0.00027062156,0.00058280915,0.0010232418,0.0008413158,0.0010954024],"category_scores_gemma":[0.0031359743,0.00029465414,0.00061768765,0.00041440185,0.0006406417,0.0011348644,0.0009295625,0.0012384935,0.00030409507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031858656,0.00019905927,0.003859864,0.00014828634,0.00008591728,0.00016671121,0.000124755,0.5915065,0.025064498,0.010009501,0.002125857,0.36639047],"study_design_scores_gemma":[0.000003850953,0.000026735119,0.00019268,0.000003532519,0.000005590288,0.000015801528,0.000004170721,0.9958192,0.002594777,0.001097564,0.00023238409,0.0000037648858],"about_ca_topic_score_codex":0.002480924,"about_ca_topic_score_gemma":0.002285046,"teacher_disagreement_score":0.002480924,"about_ca_system_score_codex":0.00066390703,"about_ca_system_score_gemma":0.000717485,"threshold_uncertainty_score":0.005043328},"labels":[],"label_agreement":null},{"id":"W4404769381","doi":"10.1016/j.dche.2024.100202","title":"Exploring spatial and temporal importance of input features and the explainability of machine learning-based modelling of water distribution systems","year":2024,"lang":"en","type":"article","venue":"Digital Chemical Engineering","topic":"Water Systems and 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":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Distribution (mathematics); Artificial intelligence; Machine learning; Mathematics","score_opus":0.013014549380097901,"score_gpt":0.16656877591230218,"score_spread":0.15355422653220427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404769381","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":[0.8742533,0.00015332503,0.12280895,0.0006657843,0.00001351079,0.000026737818,0.00017426538,0.00023201095,0.0016721254],"genre_scores_gemma":[0.9960514,0.000026713135,0.0036351555,0.0000102422655,0.0000023051791,0.000004806913,0.000041994368,0.0000074158547,0.00022018862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997874,0.000078679666,0.00001186873,0.000054284337,0.0000337144,0.00003398051],"domain_scores_gemma":[0.9979937,0.0015551198,0.0001919638,0.000083192856,0.00014817441,0.000027809012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007931885,0.0005140381,0.00021784996,0.00030752807,0.00026386828,0.0008494532,0.00048355927,0.0004668385,0.00042987504],"category_scores_gemma":[0.004553515,0.00028334904,0.00033379675,0.00020815562,0.00050497503,0.0008211581,0.0004414822,0.0008961837,0.000040066785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027983317,0.000011756493,0.0067288955,0.000014786504,0.000018796607,0.000045287106,0.00007615139,0.9861522,0.0010020887,0.0007643847,0.000048800375,0.00510889],"study_design_scores_gemma":[7.884126e-7,0.0000048392476,0.0011639628,0.0000015015773,0.0000022405509,0.000002929688,0.000011406432,0.9981452,0.00022333616,0.00040327225,0.000038863596,0.0000017610437],"about_ca_topic_score_codex":0.11007352,"about_ca_topic_score_gemma":0.070654325,"teacher_disagreement_score":0.11007352,"about_ca_system_score_codex":0.0017468195,"about_ca_system_score_gemma":0.0011052728,"threshold_uncertainty_score":0.21886563},"labels":[],"label_agreement":null},{"id":"W4404828031","doi":"10.1007/978-3-031-73486-1_4","title":"Source-Linked Water Distribution Emergency Hydraulic Response Measures","year":2024,"lang":"en","type":"book-chapter","venue":"Springer proceedings in energy","topic":"Water Systems and 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 Windsor","funders":"","keywords":"Emergency response; Environmental science; Distribution (mathematics); Medicine; Mathematics; Medical emergency","score_opus":0.009633659146105871,"score_gpt":0.18329055289978768,"score_spread":0.17365689375368182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404828031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02942278,0.001380612,0.6552504,0.0027619787,0.0010591479,0.00025438427,0.0014512199,0.0025741048,0.3058453],"genre_scores_gemma":[0.7858989,0.0021263987,0.07554339,0.00088303856,0.0004506119,0.0002178298,0.0016604357,0.00043677347,0.1327826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992843,0.00015247497,0.000023625189,0.000086927095,0.00037542675,0.000077186734],"domain_scores_gemma":[0.9994368,0.00018903098,0.00007602892,0.000063756874,0.00019677043,0.00003751374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083580194,0.0007619849,0.00037727447,0.0009090586,0.00032486173,0.001558717,0.0010613509,0.0007837658,0.013172285],"category_scores_gemma":[0.0021463803,0.00022560211,0.00035403686,0.0008531172,0.0003955309,0.0015097887,0.0010936044,0.0010430724,0.0020315477],"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":[0.00024541133,0.00026522542,0.0010048007,0.00030290644,0.000082438644,0.00013498697,0.000116019655,0.29818782,0.0074247415,0.18284439,0.04868261,0.4607086],"study_design_scores_gemma":[0.00008010693,0.0003831896,0.0050807507,0.00034741923,0.00010721435,0.0001685506,0.00037077052,0.4489194,0.018797422,0.3215126,0.20412125,0.00011126361],"about_ca_topic_score_codex":0.0012545,"about_ca_topic_score_gemma":0.0022817096,"teacher_disagreement_score":0.013172285,"about_ca_system_score_codex":0.0011938887,"about_ca_system_score_gemma":0.0011091331,"threshold_uncertainty_score":0.044065714},"labels":[],"label_agreement":null},{"id":"W4405004151","doi":"10.1680/jwama.23.00049","title":"Applying neural networks combined with Monte Carlo simulation in dam operations to obtain operational, economic and environmental gains","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Water Management","topic":"Water Systems and 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":"Impact","funders":"","keywords":"Metropolitan area; Water supply; Artificial neural network; Monte Carlo method; Water resources; Work (physics); Resource (disambiguation); Computer science; Environmental science; Environmental economics; Engineering; Environmental engineering; Artificial intelligence; Geography; Mathematics","score_opus":0.006275404183701188,"score_gpt":0.1827866920466151,"score_spread":0.17651128786291392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405004151","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":[0.59617895,0.0007483601,0.39055157,0.0005557673,0.00013518897,0.00015683465,0.00011638655,0.00050872564,0.011048197],"genre_scores_gemma":[0.97973746,0.00012566178,0.01923502,0.000029085531,0.000010691452,0.000056410612,0.00003498695,0.000015328373,0.00075528515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957377,0.00022363318,0.000024026993,0.0000464789,0.00007461919,0.000057397283],"domain_scores_gemma":[0.99726295,0.002244918,0.00019429979,0.0000662998,0.00018798948,0.000043585052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012897815,0.00071951724,0.00082816946,0.0008441102,0.00037696693,0.0008964792,0.0006001908,0.0011128973,0.001184386],"category_scores_gemma":[0.0040651965,0.0005166395,0.00049258996,0.00083418563,0.00047894602,0.0007516286,0.00048453448,0.000777619,0.00009668379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002546999,0.000020248779,0.0003042215,0.000010044237,0.000009622881,0.000007816293,0.000004608724,0.9968598,0.000099598146,0.00022147218,0.000026968535,0.0024101424],"study_design_scores_gemma":[0.0000014750543,0.0000069447133,0.000042727057,9.532745e-7,0.0000014066976,6.517739e-7,0.0000012100406,0.9997849,0.00006840121,0.00007762347,0.000012700463,0.0000010440586],"about_ca_topic_score_codex":0.021803021,"about_ca_topic_score_gemma":0.017343502,"teacher_disagreement_score":0.021803021,"about_ca_system_score_codex":0.0013267827,"about_ca_system_score_gemma":0.0010579258,"threshold_uncertainty_score":0.043352246},"labels":[],"label_agreement":null},{"id":"W4405327070","doi":"10.1007/978-3-031-61535-1_25","title":"Operational Modal Analysis of a Hydroelectric Dam Under Mixed Excitation Conditions","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Water Systems and 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":"Energie NB Power (Canada); University of New Brunswick","funders":"","keywords":"Hydroelectricity; Modal; Modal analysis; Environmental science; Engineering; Electrical engineering; Structural engineering; Materials science; Finite element method","score_opus":0.007240580935029696,"score_gpt":0.1962529064713423,"score_spread":0.18901232553631261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405327070","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":[0.5970083,0.00032763352,0.32505924,0.00035197308,0.000048843027,0.00005139126,0.0002695654,0.00029596314,0.07658706],"genre_scores_gemma":[0.9927128,0.000052891024,0.0026063572,0.000011361697,0.000006158754,0.000008140727,0.000023853065,0.00002002696,0.004558309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99993896,0.000016650743,0.0000018721712,0.00000768653,0.000022278608,0.000012439828],"domain_scores_gemma":[0.9998914,0.000061103354,0.000012052935,0.00000795608,0.000019097311,0.0000083625455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016457966,0.00026710413,0.00023226968,0.00022979859,0.0002679484,0.00047890723,0.0003859076,0.00039414875,0.003861667],"category_scores_gemma":[0.0002750304,0.00013933096,0.00022040578,0.0001411177,0.0005829289,0.00049904105,0.0004728222,0.00031433965,0.00022909262],"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":[0.0003146199,0.00007401751,0.0010036727,0.000118922886,0.000029821987,0.0005127026,0.0002699564,0.83356684,0.08995333,0.047263496,0.0012258054,0.025666788],"study_design_scores_gemma":[0.0000023348284,0.000025022277,0.000537217,0.000003495359,0.0000038521957,0.000035391946,0.000051753745,0.9948531,0.0015404029,0.0026780088,0.00026364962,0.000005873723],"about_ca_topic_score_codex":0.0013653453,"about_ca_topic_score_gemma":0.0017422348,"teacher_disagreement_score":0.003861667,"about_ca_system_score_codex":0.00022923028,"about_ca_system_score_gemma":0.00021967506,"threshold_uncertainty_score":0.012918532},"labels":[],"label_agreement":null},{"id":"W4405352293","doi":"","title":"Proceedings of the Toronto TEAM/ACES Workshop","year":2025,"lang":"en","type":"article","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Water Systems and 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":"University of Chicago; U.S. Department of Energy","keywords":"Library science; Psychology; Aeronautics; Engineering; Computer science","score_opus":0.006189978509528316,"score_gpt":0.19833775855037794,"score_spread":0.19214778004084962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405352293","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":[0.014310328,0.029419648,0.0057350867,0.06965995,0.036631294,0.000475069,0.008827497,0.0009179115,0.8340231],"genre_scores_gemma":[0.06085624,0.016507447,0.0047214366,0.0032500548,0.005371907,0.00029724598,0.011082992,0.00054161093,0.8973712],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966582,0.00042115638,0.00010663618,0.00032383003,0.0018585074,0.00063151383],"domain_scores_gemma":[0.994764,0.00028968372,0.0001221025,0.00028372253,0.0022453154,0.0022951476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040495214,0.0013226452,0.0007241812,0.0015404607,0.0051991157,0.008041232,0.002067552,0.0019199436,0.10375366],"category_scores_gemma":[0.0028857521,0.00050256064,0.00092343526,0.0022449498,0.001507567,0.001303338,0.0034507986,0.0024116428,0.02261356],"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":[0.000077004624,0.0000414885,0.00019508525,0.000070966504,0.0000068965924,0.0000898128,0.00024590254,0.00043844586,0.0001799741,0.0032059464,0.9732405,0.022208132],"study_design_scores_gemma":[0.000013564709,0.000016459602,0.001276244,0.00009462042,0.000005284796,0.000025969399,0.0004969196,0.00023057815,0.00014394287,0.0007058882,0.9969798,0.000010748603],"about_ca_topic_score_codex":0.4990022,"about_ca_topic_score_gemma":0.71196735,"teacher_disagreement_score":0.4990022,"about_ca_system_score_codex":0.024567407,"about_ca_system_score_gemma":0.032855913,"threshold_uncertainty_score":0.99219537},"labels":[],"label_agreement":null},{"id":"W4405360806","doi":"10.1115/ipc2024-130959","title":"Design and Testing of a Flow Facility for Pipeline Leak Prediction, Detection, and Investigation","year":2024,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Pipeline (software); Leak detection; Computer science; Leak; Pipeline transport; Flow (mathematics); Reliability engineering; Petroleum engineering; Engineering; Operating system; Mechanics; Mechanical engineering","score_opus":0.021859748325694962,"score_gpt":0.2078094261902479,"score_spread":0.18594967786455294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405360806","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":[0.7211723,0.00013593881,0.26937836,0.00016826992,0.00007608224,0.0014270672,0.0009083254,0.0055677453,0.001165877],"genre_scores_gemma":[0.93964654,0.000037533933,0.0586724,0.000043619668,0.000011100681,0.00047118388,0.00026953444,0.000048988688,0.0007990871],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992466,0.00012229999,0.000050621577,0.0001809529,0.00026686979,0.00013275791],"domain_scores_gemma":[0.9985442,0.00030927215,0.0003144038,0.00020689583,0.0004361016,0.00018907589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011323317,0.00063941657,0.00056700135,0.0011784027,0.00061422837,0.00041844053,0.0015221535,0.0009306175,0.0023753142],"category_scores_gemma":[0.0014224795,0.00034920746,0.00035868277,0.0003316632,0.00050800736,0.0007139569,0.0005902132,0.00036639266,0.00038021515],"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":[0.0017710992,0.0015662229,0.02607274,0.0006725296,0.00006188845,0.00061856525,0.00022204316,0.0706539,0.78968877,0.0016480356,0.002847663,0.10417657],"study_design_scores_gemma":[0.00046078567,0.009326213,0.028503524,0.00007614355,0.00010974296,0.0005146056,0.00012700526,0.2922647,0.66031265,0.00044409634,0.007706037,0.00015445598],"about_ca_topic_score_codex":0.0017071626,"about_ca_topic_score_gemma":0.00117957,"teacher_disagreement_score":0.0023753142,"about_ca_system_score_codex":0.0006805216,"about_ca_system_score_gemma":0.0015198122,"threshold_uncertainty_score":0.007946193},"labels":[],"label_agreement":null},{"id":"W4405360952","doi":"10.1115/ipc2024-134107","title":"Leak Orientation Effects on Flow-Induced Vibration in Horizontal Multiphase Pipelines","year":2024,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and 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":"Canadian Nuclear Laboratories; University of Calgary","funders":"","keywords":"Pipeline transport; Leak; Multiphase flow; Flow (mathematics); Vibration; Orientation (vector space); Mechanics; Vortex-induced vibration; Petroleum engineering; Materials science; Geology; Environmental science; Engineering; Acoustics; Mechanical engineering; Physics; Geometry","score_opus":0.008588544226464163,"score_gpt":0.22390946962253008,"score_spread":0.21532092539606593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405360952","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":[0.9937448,0.000049133254,0.005871971,0.000010946948,0.000006908501,0.000010741914,0.00004207531,0.00007998375,0.00018355668],"genre_scores_gemma":[0.99899524,0.000023258373,0.0008389148,0.000005684483,0.0000014907484,0.0000064493784,0.000032029224,0.000007401106,0.00008957539],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980515,0.00002373083,0.000010172627,0.00003895322,0.00008050946,0.00004159417],"domain_scores_gemma":[0.9996253,0.00014023721,0.00010812731,0.000025220546,0.0000715222,0.000029664061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027955562,0.00032194503,0.00023420971,0.0002517171,0.00011924067,0.00015808415,0.00015931472,0.00025619712,0.0009991195],"category_scores_gemma":[0.0006220764,0.00018133888,0.00017145347,0.00013760713,0.0003564133,0.0002479226,0.0002703415,0.00018827792,0.00011838856],"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":[0.00030208705,0.00006956295,0.0037869723,0.00008654864,0.00000780477,0.00011760404,0.00008258702,0.0031069238,0.98402727,0.00003381258,0.00004737493,0.008331395],"study_design_scores_gemma":[0.000050514802,0.0026923532,0.14865217,0.00003316894,0.000038311442,0.00021641846,0.00027810977,0.04037084,0.8068427,0.0001553522,0.000626276,0.0000438871],"about_ca_topic_score_codex":0.00023152037,"about_ca_topic_score_gemma":0.00024359443,"teacher_disagreement_score":0.0009991195,"about_ca_system_score_codex":0.00011551107,"about_ca_system_score_gemma":0.00009867132,"threshold_uncertainty_score":0.00334239},"labels":[],"label_agreement":null},{"id":"W4405534765","doi":"10.3390/w16243647","title":"Key Factors Influencing Drinking Water Advisories on Indigenous Reserves in Canada: An XGBoost Analysis","year":2024,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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 Guelph","funders":"","keywords":"Indigenous; Duration (music); Certification; Business; Water supply; Key (lock); Environmental planning; Environmental resource management; Geography; Environmental science; Computer science; Environmental engineering; Computer security; Economics; Ecology","score_opus":0.008528873760811378,"score_gpt":0.18568880629783244,"score_spread":0.17715993253702106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405534765","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":[0.98936975,0.00041368583,0.0016978653,0.00043697472,0.000014133806,0.000031757994,0.006602802,0.00008952336,0.0013435314],"genre_scores_gemma":[0.9823669,0.00029213433,0.0028451693,0.00006707457,0.0000065574263,0.000016601029,0.01186732,0.000020377845,0.002517837],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996259,0.000042540607,0.00001689741,0.0000803231,0.000090748275,0.00014348832],"domain_scores_gemma":[0.9990582,0.0002188314,0.000092048766,0.000040366707,0.00046860223,0.0001218611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007470894,0.0005503217,0.00046132016,0.0012631358,0.0011264639,0.0010514546,0.00085506635,0.00039901314,0.0012103699],"category_scores_gemma":[0.0018374255,0.00018104605,0.00074567535,0.0023117813,0.0005630691,0.00030553614,0.00052935537,0.0007227345,0.0002018569],"study_design_candidate":"simulation_or_modeling","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":[0.00039054477,0.00021444072,0.87560356,0.0001298231,0.00024483944,0.00025749655,0.0003753503,0.065190144,0.0010546459,0.0008180831,0.008538499,0.047182586],"study_design_scores_gemma":[0.000027586228,0.00005823053,0.7140879,0.000061703155,0.00011690154,0.00006223017,0.0021322658,0.2752481,0.0009982907,0.000456169,0.006699574,0.000050933097],"about_ca_topic_score_codex":0.9862694,"about_ca_topic_score_gemma":0.98370194,"teacher_disagreement_score":0.013730586,"about_ca_system_score_codex":0.01102999,"about_ca_system_score_gemma":0.017002586,"threshold_uncertainty_score":0.080028534},"labels":[],"label_agreement":null},{"id":"W4405835833","doi":"10.1007/698_2024_1195","title":"Strategic Decision-Making in Water Utilities: Historical Insights and Emerging Analytics for Water Mains Repair Versus Replacement Decision","year":2024,"lang":"en","type":"book-chapter","venue":"˜The œhandbook of environmental chemistry","topic":"Water Systems and 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 British Columbia","funders":"","keywords":"Analytics; Mains electricity; Water utility; Electricity; Business; Computer science; Environmental economics; Environmental science; Water supply; Engineering; Data science; Economics; Environmental engineering; Electrical engineering","score_opus":0.012501757692498217,"score_gpt":0.20282306599842326,"score_spread":0.19032130830592503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405835833","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":[0.017584087,0.3185121,0.22084357,0.0295425,0.0020130407,0.00011386921,0.0007740208,0.00018302847,0.41043386],"genre_scores_gemma":[0.46755886,0.3330465,0.080799975,0.0040464383,0.0021918314,0.00014644825,0.00072794285,0.00017704674,0.111304946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992162,0.00029606864,0.00003822371,0.000114929026,0.00024207159,0.000092469316],"domain_scores_gemma":[0.9985018,0.0011366347,0.00008297534,0.000053771604,0.000163017,0.00006181394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016593316,0.001135683,0.000586849,0.001168793,0.0007854635,0.006717381,0.0011756741,0.0017659401,0.0059377137],"category_scores_gemma":[0.002846287,0.0004977256,0.00041277116,0.0046813446,0.004405974,0.007680114,0.0011821628,0.0029058466,0.0012258238],"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":[0.000014948766,0.00004019013,0.00027769778,0.00020443134,0.000012383546,0.000039741983,0.0003836809,0.007234776,0.00018693488,0.9053188,0.017361078,0.06892541],"study_design_scores_gemma":[0.0000025025004,0.000014915058,0.00034517286,0.00029561252,0.0000054078605,0.0000449577,0.00055849226,0.0060397442,0.00020064483,0.91314,0.079334795,0.000017787432],"about_ca_topic_score_codex":0.006239958,"about_ca_topic_score_gemma":0.014214693,"teacher_disagreement_score":0.006717381,"about_ca_system_score_codex":0.0039955596,"about_ca_system_score_gemma":0.0039390163,"threshold_uncertainty_score":0.028989911},"labels":[],"label_agreement":null},{"id":"W4405843806","doi":"10.1016/j.ecolind.2024.112984","title":"A fuzzy logic approach within the DPSIR framework to address the inherent uncertainty and complexity of water security assessments","year":2024,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Water Systems and 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":"Ministry of Education; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"DPSIR; Fuzzy logic; Computer science; Risk analysis (engineering); Environmental science; Environmental resource management; Business; Artificial intelligence","score_opus":0.04237378235462633,"score_gpt":0.2800415429201359,"score_spread":0.23766776056550956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405843806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021532297,0.00021153012,0.990254,0.00024805398,0.00003806694,0.0000741467,0.000047809564,0.0000545108,0.006918616],"genre_scores_gemma":[0.30509982,0.0010337627,0.68633294,0.000321034,0.00020731919,0.0005358269,0.00018152922,0.000041114614,0.0062466976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783295,0.0009567718,0.00016524157,0.00031231457,0.0006182817,0.00011445633],"domain_scores_gemma":[0.99847513,0.0007440688,0.00021319026,0.00010096465,0.00041500173,0.00005160169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035012553,0.0010834608,0.0011225044,0.0027617326,0.0009197215,0.0028162568,0.0018834808,0.0011463921,0.0030827571],"category_scores_gemma":[0.0036654216,0.00046203152,0.001977199,0.0022169612,0.0012586303,0.00248556,0.0017527621,0.0020129737,0.00052507507],"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":[0.00004101352,0.00012417334,0.0018771038,0.00052825426,0.0002394241,0.00065513677,0.0006394923,0.3806827,0.0043814415,0.48201564,0.002134557,0.12668112],"study_design_scores_gemma":[0.0000140474685,0.00011819271,0.00044410763,0.00012029776,0.00007482462,0.00022963312,0.0002050522,0.7949484,0.0009829865,0.19477892,0.0080310395,0.00005245265],"about_ca_topic_score_codex":0.0068100374,"about_ca_topic_score_gemma":0.0065541454,"teacher_disagreement_score":0.0068100374,"about_ca_system_score_codex":0.002258478,"about_ca_system_score_gemma":0.0027749848,"threshold_uncertainty_score":0.0185166},"labels":[],"label_agreement":null},{"id":"W4405860087","doi":"10.2166/wst.2024.389","title":"Modelling and integrated assessment of urban water systems","year":2024,"lang":"en","type":"editorial","venue":"Water Science & Technology","topic":"Water Systems and 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 Laval","funders":"","keywords":"Environmental science; Computer science; Environmental planning; Systems engineering; Water resource management; Engineering","score_opus":0.006093039304391346,"score_gpt":0.21511462165830936,"score_spread":0.20902158235391802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405860087","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":[0.000163582,0.016441924,0.0055051805,0.034011226,0.9375576,0.000029665329,0.0003025289,0.00019827922,0.005790066],"genre_scores_gemma":[0.0052073323,0.022480482,0.0031548063,0.012363091,0.912344,0.000070100876,0.00028620518,0.0003184297,0.043775465],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99399465,0.0017759971,0.0005369216,0.0005215114,0.0029686843,0.00020220056],"domain_scores_gemma":[0.9848408,0.007275155,0.0005526763,0.0006530718,0.0057409015,0.0009374317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075766174,0.0035889135,0.0037225788,0.0028525165,0.0017724439,0.0074899863,0.0046422514,0.013827437,0.007437896],"category_scores_gemma":[0.014952554,0.0013472077,0.0032038747,0.0022546644,0.0026528714,0.005715565,0.0018672708,0.013005976,0.005269056],"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":[0.00003161034,0.000022698328,0.000027411597,0.00024266614,0.000055756762,0.000053655025,0.000011056344,0.0018345906,0.00009343424,0.0017198591,0.98408717,0.011820057],"study_design_scores_gemma":[0.0000659098,0.00003935072,0.00034816307,0.000440247,0.000110049885,0.00011851595,0.000044068278,0.010210544,0.00030000228,0.020909702,0.9673657,0.00004786254],"about_ca_topic_score_codex":0.0077457894,"about_ca_topic_score_gemma":0.017987309,"teacher_disagreement_score":0.013827437,"about_ca_system_score_codex":0.0027966737,"about_ca_system_score_gemma":0.0033917427,"threshold_uncertainty_score":0.04006946},"labels":[],"label_agreement":null},{"id":"W4405937687","doi":"10.1109/la-cci62337.2024.10814808","title":"Semi and Self-supervised Learning for Multi-label Image Classification: An Underwater Inspection Imagery Application","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"McMaster University","funders":"","keywords":"Underwater; Computer science; Artificial intelligence; Multi-label classification; Contextual image classification; Image (mathematics); Pattern recognition (psychology); Computer vision; Machine learning; Geology","score_opus":0.02532896457757237,"score_gpt":0.251647095174596,"score_spread":0.22631813059702363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405937687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2148733,0.0008903398,0.77207565,0.0011779163,0.0002669508,0.00022699963,0.000772123,0.006791379,0.0029253527],"genre_scores_gemma":[0.66087806,0.00015529677,0.33243445,0.00029823932,0.00011111066,0.000112860405,0.0019358993,0.00022091706,0.0038532128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892443,0.0003323878,0.000044089884,0.00034399822,0.0002274082,0.00012762382],"domain_scores_gemma":[0.9980737,0.0008707648,0.00016085884,0.00034708617,0.00042995703,0.00011761559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017677695,0.0012799419,0.00090658694,0.0008652585,0.00048392263,0.0006552338,0.0014088369,0.001521163,0.0012853354],"category_scores_gemma":[0.0031448072,0.00030602212,0.00090697,0.0008074351,0.0005813905,0.0009341607,0.0008541812,0.0016041641,0.0005765582],"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":[0.000938827,0.0011325873,0.006965498,0.00032670473,0.0002246859,0.00043733206,0.0002612586,0.32734388,0.023358865,0.0018183467,0.012832583,0.6243595],"study_design_scores_gemma":[0.000012255055,0.000056467845,0.0007514112,0.0000046847454,0.000007644461,0.00003231355,0.000029047342,0.9940566,0.0035849598,0.0008982542,0.0005582927,0.00000808356],"about_ca_topic_score_codex":0.008302671,"about_ca_topic_score_gemma":0.010955996,"teacher_disagreement_score":0.008302671,"about_ca_system_score_codex":0.0006716698,"about_ca_system_score_gemma":0.00081587426,"threshold_uncertainty_score":0.016508639},"labels":[],"label_agreement":null},{"id":"W4405945834","doi":"10.18280/mmep.111211","title":"Numerical Modeling of Hydraulic Transients in a Failure Water Supply Pumping Pipeline System Due to Power Outage: Nasiriyah Water Supply Project, Iraq","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Water Systems and 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":"Pipeline (software); Water supply; Power (physics); Environmental science; Hydraulic machinery; Marine engineering; Engineering; Petroleum engineering; Environmental engineering; Mechanical engineering; Physics","score_opus":0.012200217696326135,"score_gpt":0.1911183991221536,"score_spread":0.17891818142582744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405945834","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":[0.94701964,0.00025022126,0.043266572,0.0004639026,0.00006255705,0.00007723424,0.00032716306,0.00027125824,0.008261445],"genre_scores_gemma":[0.9966271,0.00009218065,0.0017593801,0.00001912385,0.0000087677445,0.000036002883,0.00008710742,0.00001532329,0.0013549873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998221,0.00005084179,0.000011203236,0.000028593166,0.00003282906,0.000054574986],"domain_scores_gemma":[0.9995677,0.00020794863,0.000071381386,0.000020385603,0.00009041921,0.00004210614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038694678,0.0005079071,0.0006184063,0.0005490266,0.0010246924,0.0010416524,0.0007626917,0.0015607739,0.001465646],"category_scores_gemma":[0.0010544313,0.0003566757,0.00075038295,0.00047789028,0.0009118636,0.0005629247,0.00078742957,0.00073436217,0.00012018987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003552761,0.000029955476,0.003323893,0.00002259384,0.0000105415775,0.00028346767,0.000069937436,0.99329424,0.0012444532,0.0004609233,0.0001827268,0.0010416311],"study_design_scores_gemma":[0.000004876563,0.000019724386,0.00066736387,0.0000022517268,0.0000037179939,0.000013588254,0.000053334577,0.99891126,0.00014638706,0.00008823584,0.00008513954,0.000004078883],"about_ca_topic_score_codex":0.037588436,"about_ca_topic_score_gemma":0.012575127,"teacher_disagreement_score":0.037588436,"about_ca_system_score_codex":0.0010183122,"about_ca_system_score_gemma":0.0011686144,"threshold_uncertainty_score":0.07473934},"labels":[],"label_agreement":null},{"id":"W4406062931","doi":"10.1016/j.tust.2024.106357","title":"A comprehensive review of influential factors and predictive techniques of time to failure for sewer pipes","year":2025,"lang":"en","type":"review","venue":"Tunnelling and Underground Space Technology","topic":"Water Systems and 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":"McGill University","funders":"","keywords":"Engineering; Forensic engineering; Mathematics","score_opus":0.010853386793359452,"score_gpt":0.2539020435778087,"score_spread":0.24304865678444926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406062931","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":[0.00029362945,0.99573874,0.0030145536,0.00008435047,0.00010531468,0.000009469638,0.000081993974,0.00001999666,0.0006519323],"genre_scores_gemma":[0.00264101,0.993913,0.0026398504,0.00006183104,0.00019404697,0.000013911021,0.00010493567,0.00000770301,0.00042367267],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9994443,0.000096768796,0.000090557594,0.00013169582,0.00020940509,0.000027224492],"domain_scores_gemma":[0.9979365,0.0014260458,0.00021585767,0.000056074256,0.0003374156,0.000028209142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015204381,0.0019984734,0.0027284732,0.0043581054,0.00032344542,0.0015740066,0.0017528865,0.0012162959,0.0036781435],"category_scores_gemma":[0.0034251497,0.00085840194,0.0018004777,0.006547665,0.00047627723,0.0020640583,0.0007409288,0.0012277511,0.0013002346],"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":[0.00005906681,0.000121831006,0.0006415529,0.038704135,0.00030441477,0.00006920048,0.000044373788,0.010634228,0.00097129506,0.00663158,0.012403533,0.9294147],"study_design_scores_gemma":[0.000054031378,0.0007074973,0.0075140228,0.02920991,0.0024905438,0.0010111508,0.00018666219,0.025978252,0.0042327414,0.01847087,0.90982497,0.00031940427],"about_ca_topic_score_codex":0.003947958,"about_ca_topic_score_gemma":0.0045728507,"teacher_disagreement_score":0.0043581054,"about_ca_system_score_codex":0.0006627,"about_ca_system_score_gemma":0.0015928403,"threshold_uncertainty_score":0.012304604},"labels":[],"label_agreement":null},{"id":"W4406102737","doi":"10.1063/5.0245556","title":"Air–water interaction in a partially filled circular pipe","year":2025,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Water Systems and 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":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Physics; Air water; Mechanics; Classical mechanics; Aerospace engineering","score_opus":0.008071525215209817,"score_gpt":0.2128095298655701,"score_spread":0.2047380046503603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406102737","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":[0.98901844,0.00015536754,0.010236998,0.000028803654,0.000012138471,0.0000097285365,0.000016648033,0.00005287839,0.00046895441],"genre_scores_gemma":[0.9979437,0.00006741176,0.0014480588,0.000008058807,0.0000042765973,0.000006983255,0.000014706813,0.0000053027547,0.0005013799],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998331,0.000020304975,0.0000057838606,0.000050078084,0.000043595068,0.00004698155],"domain_scores_gemma":[0.99985516,0.00006061449,0.000032859276,0.000012600904,0.00001924236,0.000019562147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013520791,0.0002604113,0.00033534758,0.00020983806,0.00034425338,0.00043388226,0.000284636,0.0004992385,0.00044613736],"category_scores_gemma":[0.0004000726,0.00019806255,0.00021505888,0.0001343922,0.0006570385,0.000480951,0.0006262509,0.00017848969,0.00010463294],"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":[0.00022044199,0.000042740336,0.0018536072,0.000089616165,0.000021103146,0.00055581867,0.0001674078,0.030271837,0.9592854,0.00095228304,0.000092887065,0.0064468132],"study_design_scores_gemma":[0.00008163255,0.0015864866,0.015770286,0.00001698009,0.000056555407,0.0005282946,0.00031022762,0.53580076,0.44192517,0.0011175598,0.0027310099,0.00007498408],"about_ca_topic_score_codex":0.00081093,"about_ca_topic_score_gemma":0.00050593715,"teacher_disagreement_score":0.00081093,"about_ca_system_score_codex":0.00018296874,"about_ca_system_score_gemma":0.0002839708,"threshold_uncertainty_score":0.0016124249},"labels":[],"label_agreement":null},{"id":"W4406108206","doi":"10.1111/itor.13607","title":"An optimization model for the energy management of the network of tanks in a drinking water distribution system","year":2025,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Water Systems and 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; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia y Tecnología; Ministerio de Ciencia e Innovación","keywords":"Inflow; Outflow; Population; Environmental science; Water supply; Water quality; Water resources; Water storage; Computer science; Water resource management; Environmental engineering; Engineering; Ecology","score_opus":0.025781471092105313,"score_gpt":0.3034227159329594,"score_spread":0.27764124484085406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406108206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13158628,0.002067956,0.78413814,0.0025041723,0.0003788354,0.00048045884,0.0021280472,0.00070140953,0.07601465],"genre_scores_gemma":[0.9519506,0.0004979579,0.02335831,0.00017485351,0.000046398425,0.00044760198,0.00054702366,0.00008959665,0.022887653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993876,0.00021913917,0.000020870233,0.00011168489,0.000099350895,0.0001613298],"domain_scores_gemma":[0.99895525,0.0006318831,0.000083413535,0.000021562482,0.00023329271,0.000074600684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013234352,0.0014048564,0.0016524201,0.00094934547,0.00087462395,0.0027195313,0.0015104099,0.003223788,0.011628943],"category_scores_gemma":[0.0023018091,0.0009987763,0.0013916317,0.0010102061,0.0009024522,0.0011518965,0.0013015226,0.0019956925,0.00080281304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002853309,0.000013219494,0.00015851544,0.000031211217,0.000012968616,0.000052165175,0.000012093579,0.9964226,0.0001438815,0.0017645662,0.00033275396,0.0010274721],"study_design_scores_gemma":[0.0000092506925,0.000012371052,0.000072827315,0.000007319052,0.000006399279,0.0000033136994,0.000014240868,0.9989674,0.000041263007,0.00058838876,0.00027409566,0.0000030514684],"about_ca_topic_score_codex":0.03782789,"about_ca_topic_score_gemma":0.017590953,"teacher_disagreement_score":0.03782789,"about_ca_system_score_codex":0.0026336785,"about_ca_system_score_gemma":0.0022343271,"threshold_uncertainty_score":0.0752154},"labels":[],"label_agreement":null},{"id":"W4406370402","doi":"10.1002/cjce.25591","title":"Modelling of the gas purification system for flue gas acid production","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Water Systems and 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":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Extended Kalman filter; Flue gas; Process engineering; Overhead (engineering); Production (economics); Filter (signal processing); Kalman filter; Nonlinear system; Computer science; Engineering; Waste management","score_opus":0.009178238505622098,"score_gpt":0.15850008393217419,"score_spread":0.14932184542655208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406370402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27451405,0.0006637842,0.70231426,0.00058981124,0.000094114905,0.00023188758,0.0006283666,0.0015488651,0.019414881],"genre_scores_gemma":[0.99018294,0.00015448738,0.0058079283,0.0000141030605,0.000007427815,0.000101336074,0.0001057193,0.000020255853,0.0036058465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979264,0.000038296785,0.000010864693,0.00005986876,0.00006361362,0.00003476131],"domain_scores_gemma":[0.9998634,0.000046996283,0.000024100196,0.000009090892,0.00004788236,0.000008497944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002785566,0.00077856885,0.00057759497,0.0003810377,0.0006380301,0.0009544552,0.0007205269,0.0010403418,0.001681137],"category_scores_gemma":[0.00041801055,0.0004007106,0.0006465393,0.00025060668,0.00047390466,0.00075792824,0.00051759405,0.0006400827,0.0003133478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024537656,0.000014587118,0.00075304194,0.000033544817,0.000010765985,0.000078527984,0.00003613202,0.99168396,0.0035085008,0.0008483927,0.00014483964,0.0028631466],"study_design_scores_gemma":[0.0000035193546,0.000014372125,0.0003221178,0.0000015000697,0.0000035656142,0.000007785313,0.0000052397936,0.99880767,0.0005136143,0.00012408069,0.00019291554,0.0000036299905],"about_ca_topic_score_codex":0.039409447,"about_ca_topic_score_gemma":0.014347404,"teacher_disagreement_score":0.039409447,"about_ca_system_score_codex":0.00083687785,"about_ca_system_score_gemma":0.0015507746,"threshold_uncertainty_score":0.07836014},"labels":[],"label_agreement":null},{"id":"W4406391886","doi":"10.1016/s0967-0653(98)83929-x","title":"10.1016/s0967-0653(98)83929-x","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Water Systems and 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":"Environmental science","score_opus":0.004032475832532523,"score_gpt":0.1403301736201119,"score_spread":0.13629769778757939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406391886","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":[0.00050910073,0.00043045267,0.0012268076,0.00039369604,0.0003283223,0.00011082522,0.0008289081,0.0010985774,0.99507326],"genre_scores_gemma":[0.0006993318,0.00019351284,0.00050122093,0.00015502331,0.000049960145,0.00005008346,0.00036951213,0.00019196079,0.9977895],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994708,0.000040158517,0.000042974065,0.0001819922,0.00015159699,0.00011247835],"domain_scores_gemma":[0.99830234,0.00043709236,0.00012465776,0.00025387146,0.00036952738,0.0005125621],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00092800497,0.0023436127,0.0014219073,0.0025594996,0.0018514091,0.0033571848,0.0028943894,0.004165071,0.98651695],"category_scores_gemma":[0.0015471147,0.0008259345,0.0011600787,0.0026952187,0.0017329891,0.0047387346,0.0025810306,0.0023178018,0.9905194],"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":[0.0003373616,0.00018608147,0.0006833185,0.000549853,0.000034898312,0.00020930503,0.000110940455,0.00057903974,0.0020843702,0.006663916,0.37598637,0.6125745],"study_design_scores_gemma":[0.000041350042,0.00008942766,0.00048865535,0.00023573141,0.000011913424,0.00024222385,0.000098165845,0.0003123887,0.00036837795,0.0007330851,0.99736005,0.000018570645],"about_ca_topic_score_codex":0.004331646,"about_ca_topic_score_gemma":0.0033689959,"teacher_disagreement_score":0.0134830475,"about_ca_system_score_codex":0.000992571,"about_ca_system_score_gemma":0.00096303574,"threshold_uncertainty_score":0.019231856},"labels":[],"label_agreement":null},{"id":"W4406415893","doi":"10.1016/s0967-0653(97)84464-x","title":"10.1016/s0967-0653(97)84464-x","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Water Systems and 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":"Horizontal and vertical; Geology; Environmental science; Geodesy","score_opus":0.00457830551516411,"score_gpt":0.1397839271821298,"score_spread":0.1352056216669657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406415893","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":[0.000494286,0.00045639343,0.0012110212,0.00038852272,0.00034638794,0.00010407824,0.0008527508,0.001088086,0.99505854],"genre_scores_gemma":[0.00067757897,0.00020292892,0.0004643601,0.00014182334,0.0000497543,0.000046243833,0.00039033423,0.00019120434,0.99783593],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994604,0.00004084437,0.000043139964,0.00018137644,0.0001592807,0.00011494263],"domain_scores_gemma":[0.99828404,0.00043365872,0.0001244424,0.00025766797,0.00037778387,0.0005222753],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00089905615,0.0023664976,0.0014508147,0.0025201614,0.0017331006,0.0034153615,0.0028482366,0.004038939,0.9862488],"category_scores_gemma":[0.001499156,0.00081345957,0.0011793475,0.0028501712,0.0016222362,0.00455065,0.0025378042,0.0022331562,0.9901544],"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":[0.00031276682,0.00017165426,0.00064139423,0.0005243236,0.00003506704,0.00018691234,0.00010042408,0.00057922484,0.0020224156,0.006269778,0.3555445,0.6336115],"study_design_scores_gemma":[0.000038329334,0.00008068453,0.00047006938,0.00021098716,0.000011925782,0.00021633235,0.000086151485,0.0002903855,0.00037932117,0.00067864126,0.99751914,0.000018037352],"about_ca_topic_score_codex":0.004305909,"about_ca_topic_score_gemma":0.003331103,"teacher_disagreement_score":0.013751209,"about_ca_system_score_codex":0.0010338165,"about_ca_system_score_gemma":0.0010228524,"threshold_uncertainty_score":0.019614458},"labels":[],"label_agreement":null},{"id":"W4406454300","doi":"10.1016/s0967-0653(97)83544-2","title":"10.1016/s0967-0653(97)83544-2","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Water Systems and 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":"Stability (learning theory); Flux (metallurgy); Boundary (topology); Environmental science; Mathematics; Materials science; Mathematical analysis; Computer science","score_opus":0.004628925001259566,"score_gpt":0.1399553931391217,"score_spread":0.13532646813786214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406454300","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":[0.00047996457,0.0004480965,0.0011909365,0.00038038145,0.00035708304,0.000111538655,0.0009113314,0.0010543403,0.9950663],"genre_scores_gemma":[0.000679482,0.00020455413,0.0004652943,0.00014817974,0.000051731124,0.00004999018,0.000400257,0.000185067,0.9978154],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942976,0.000041845113,0.00004473653,0.0001996886,0.00016466355,0.00011939109],"domain_scores_gemma":[0.99827063,0.00044018505,0.00012118742,0.00024668392,0.0004212498,0.000500071],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009358472,0.002414067,0.0015497819,0.0026204637,0.0018148904,0.0036917934,0.002961681,0.0041712266,0.9866485],"category_scores_gemma":[0.0014972764,0.00085663784,0.0012412813,0.0029577946,0.0016710765,0.004648689,0.0026039593,0.0024604239,0.99097943],"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":[0.00033280673,0.00017772031,0.00066333555,0.00053746963,0.000038763472,0.00019549049,0.000097137265,0.0005880304,0.0020973398,0.005977079,0.36784863,0.62144625],"study_design_scores_gemma":[0.00004277584,0.000089073554,0.00044331592,0.00024085781,0.000012668871,0.0002336951,0.000088777575,0.00030633158,0.00037747764,0.00062818965,0.99751675,0.000019966148],"about_ca_topic_score_codex":0.004339138,"about_ca_topic_score_gemma":0.0032979364,"teacher_disagreement_score":0.0133515,"about_ca_system_score_codex":0.0010984517,"about_ca_system_score_gemma":0.0010599701,"threshold_uncertainty_score":0.01904428},"labels":[],"label_agreement":null},{"id":"W4407278886","doi":"10.1016/j.jprocont.2025.103386","title":"Predictive control of flow rates and concentrations in sewage transport and treatment systems","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Water Systems and 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 Laval; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Sewage; Environmental science; Flow (mathematics); Sewage treatment; Control (management); Volumetric flow rate; Environmental engineering; Control theory (sociology); Computer science; Mechanics; Physics; Artificial intelligence","score_opus":0.0035154603664495267,"score_gpt":0.20115579067784362,"score_spread":0.19764033031139408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407278886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10975037,0.0006294798,0.88310033,0.000506415,0.00013097333,0.00010208193,0.00008947821,0.00067067775,0.005020276],"genre_scores_gemma":[0.98407274,0.00020582744,0.014266568,0.00005637053,0.000022621174,0.00007576873,0.000038624516,0.000019187082,0.0012422428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995384,0.000121060904,0.000023205266,0.000090924834,0.00015941805,0.00006697788],"domain_scores_gemma":[0.9990451,0.00052893156,0.00019319545,0.000027333474,0.0001749797,0.000030514655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011240006,0.0010458386,0.00070512865,0.00036398685,0.0003596648,0.0010056065,0.0008506204,0.0007829664,0.0007213758],"category_scores_gemma":[0.0022374538,0.0005399385,0.00038805578,0.00041367084,0.0006937013,0.0005946032,0.00065883587,0.0009337576,0.0001321909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005181758,0.000041582192,0.00023084182,0.00004428867,0.000009491206,0.000027362696,0.000025448542,0.9862299,0.002838385,0.0011989361,0.00022706625,0.00907501],"study_design_scores_gemma":[0.00000582687,0.00002386157,0.0000621188,0.0000016361862,0.0000025729655,0.0000020875057,0.0000021289825,0.9987054,0.0007714419,0.0003230295,0.00009733529,0.0000026406922],"about_ca_topic_score_codex":0.010810654,"about_ca_topic_score_gemma":0.006838662,"teacher_disagreement_score":0.010810654,"about_ca_system_score_codex":0.0007032198,"about_ca_system_score_gemma":0.0010821017,"threshold_uncertainty_score":0.021495461},"labels":[],"label_agreement":null},{"id":"W4407537037","doi":"10.1002/awwa.2401","title":"Water Main Break Rates in the United States and Canada","year":2025,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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":"Geography; Environmental science","score_opus":0.0019528695589344878,"score_gpt":0.17164486671172083,"score_spread":0.16969199715278635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407537037","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":[0.55178815,0.014960989,0.00097197626,0.01425552,0.0006041063,0.00018932085,0.2908655,0.00069627893,0.12566812],"genre_scores_gemma":[0.8929497,0.0082019055,0.00079013186,0.000947015,0.00005810162,0.000051137766,0.063494846,0.00013685357,0.033370364],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979944,0.00004638352,0.00008961093,0.00016393466,0.0011560541,0.0005496779],"domain_scores_gemma":[0.9932274,0.00017045147,0.0005281239,0.00008427529,0.0047960123,0.001193847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066071504,0.00037524055,0.00034293448,0.004353508,0.0016589377,0.0023744837,0.0011237293,0.0005395213,0.009328208],"category_scores_gemma":[0.0044945464,0.00026613433,0.0008860527,0.0072226636,0.0005673549,0.00075467455,0.0010645327,0.0012297947,0.0010086462],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033418808,0.000077224235,0.63585967,0.00035505957,0.0002546023,0.0004414367,0.0014719534,0.0041116048,0.000452541,0.009644729,0.28602713,0.060969822],"study_design_scores_gemma":[0.000020696192,0.000026066058,0.9327586,0.00022008712,0.000048823178,0.00019828262,0.0034711736,0.0019098327,0.00028803875,0.0003290254,0.06065218,0.00007718678],"about_ca_topic_score_codex":0.9969348,"about_ca_topic_score_gemma":0.9978295,"teacher_disagreement_score":0.039561033,"about_ca_system_score_codex":0.039561033,"about_ca_system_score_gemma":0.036461975,"threshold_uncertainty_score":0.28703672},"labels":[],"label_agreement":null},{"id":"W4408146199","doi":"10.1109/icmla61862.2024.00273","title":"Enhancing Pipeline Monitoring: Optimizing Window Size with Monte Carlo Search and CB-AttentionNet","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Monte Carlo method; Window (computing); Pipeline (software); Computer science; Statistics; Mathematics; World Wide Web; Operating system","score_opus":0.008153981183295383,"score_gpt":0.20295991541823968,"score_spread":0.1948059342349443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408146199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16337217,0.0021732522,0.8242053,0.0010017288,0.00021114825,0.00014981639,0.00023812302,0.005263119,0.003385321],"genre_scores_gemma":[0.85512817,0.00036983038,0.14178617,0.00036412932,0.00006290375,0.00015517851,0.00035102633,0.00027845002,0.0015040798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996126,0.00009115308,0.000026839853,0.00011256848,0.00008327054,0.000073609364],"domain_scores_gemma":[0.9986744,0.00083352835,0.00010176256,0.000095234194,0.00021273497,0.00008238328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016044413,0.0014577585,0.0012595281,0.0008409891,0.00038591638,0.00095412426,0.002195908,0.0015463524,0.0019041699],"category_scores_gemma":[0.0057365038,0.0005825032,0.0005805765,0.00070990616,0.0005345912,0.0024167958,0.0010182302,0.0015847167,0.00040125835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035536746,0.000219493,0.0019387451,0.000082709616,0.00006933501,0.000072947,0.000046591605,0.8877155,0.0047428627,0.0019259248,0.0021934414,0.10063698],"study_design_scores_gemma":[0.0000065862846,0.000011704328,0.00007743621,0.000003586858,0.000005719137,0.0000039263296,0.000002264193,0.99882096,0.0006335162,0.00035474138,0.00007731557,0.0000021393346],"about_ca_topic_score_codex":0.022971861,"about_ca_topic_score_gemma":0.021819238,"teacher_disagreement_score":0.022971861,"about_ca_system_score_codex":0.0014878701,"about_ca_system_score_gemma":0.0025952181,"threshold_uncertainty_score":0.04567629},"labels":[],"label_agreement":null},{"id":"W4408146382","doi":"10.1109/icmla61862.2024.00270","title":"Advancing Energy Monitoring: Deep Learning for Automated Non-Smart Gas Meter Readings","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"National Research Council Canada; Concordia University","funders":"Concordia University","keywords":"Computer science; Electricity meter; Smart meter; Metre; Engineering; Smart grid; Electrical engineering; Physics","score_opus":0.005099347885513977,"score_gpt":0.2107617760130572,"score_spread":0.20566242812754323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408146382","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066475116,0.0008835216,0.92043793,0.0006284025,0.00011056382,0.00007128631,0.0008579697,0.0067348983,0.00380036],"genre_scores_gemma":[0.72100514,0.00041881364,0.2711314,0.00050961453,0.000063268315,0.00009508601,0.0019539106,0.00018088384,0.0046418766],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971575,0.000057143967,0.000011165077,0.000105082094,0.00006815498,0.000042626507],"domain_scores_gemma":[0.9996705,0.00012225892,0.000039540726,0.000060361905,0.00009034963,0.000016933232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065248844,0.00073778426,0.00037022808,0.00042674615,0.00017006526,0.0005940628,0.0010043293,0.0006476507,0.00097376504],"category_scores_gemma":[0.0018232837,0.00028567505,0.00032474438,0.0005408123,0.00032240886,0.00096126867,0.00084018573,0.0013335173,0.0004939465],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027106155,0.00033434798,0.007097405,0.00016030179,0.000077899866,0.00011993039,0.000098913275,0.36227775,0.017330494,0.003770392,0.0138200885,0.59464145],"study_design_scores_gemma":[0.000004708009,0.00001756434,0.00058226375,0.000007646934,0.000004537698,0.000010095512,0.000007838451,0.99203295,0.0046623526,0.0015388294,0.0011269487,0.000004320085],"about_ca_topic_score_codex":0.007053458,"about_ca_topic_score_gemma":0.011768429,"teacher_disagreement_score":0.007053458,"about_ca_system_score_codex":0.00071634125,"about_ca_system_score_gemma":0.00068538915,"threshold_uncertainty_score":0.014024794},"labels":[],"label_agreement":null},{"id":"W4408656463","doi":"10.14796/jwmm.c543","title":"Revised Chlorine Mass Balance for Chlorine Loss Assessment in Water Distribution Networks","year":2025,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Kasetsart University","keywords":"Chlorine; Balance (ability); Water balance; Environmental science; Chemistry; Environmental chemistry; Engineering; Medicine; Organic chemistry","score_opus":0.008005249773741369,"score_gpt":0.2235309134238429,"score_spread":0.21552566365010153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408656463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18119057,0.00094133575,0.8029799,0.00056439085,0.00011320492,0.00030555102,0.0014007948,0.00060295605,0.011901324],"genre_scores_gemma":[0.9534231,0.00040687443,0.04127973,0.00009830632,0.000031168758,0.00019557035,0.00065037917,0.00007394899,0.0038410176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992059,0.00024607274,0.000043986878,0.00011675972,0.00031884538,0.00006847774],"domain_scores_gemma":[0.9992988,0.00022084037,0.000121084995,0.000039080285,0.00029532268,0.00002487081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013120895,0.0010432915,0.00060375343,0.0014364832,0.00034922326,0.0013884102,0.001102744,0.0010604479,0.0016489523],"category_scores_gemma":[0.0029257552,0.00034661012,0.0008300751,0.00074777316,0.00043183277,0.0019645249,0.0010638138,0.0006135093,0.00022229868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043613214,0.000020370673,0.0026332303,0.00007167189,0.000031988726,0.00007521063,0.000036142905,0.97174084,0.004693001,0.0041528186,0.00057328027,0.015927803],"study_design_scores_gemma":[0.000009036119,0.00003501663,0.0008313766,0.00001145683,0.000014163875,0.000023906656,0.000018961855,0.9949982,0.0010832291,0.0020346392,0.000928283,0.000011848296],"about_ca_topic_score_codex":0.012192513,"about_ca_topic_score_gemma":0.008429247,"teacher_disagreement_score":0.012192513,"about_ca_system_score_codex":0.0015496572,"about_ca_system_score_gemma":0.0011299757,"threshold_uncertainty_score":0.024243116},"labels":[],"label_agreement":null},{"id":"W4408663586","doi":"10.1007/s11269-025-04160-3","title":"Laboratory-Validated Model of Air Discharge at User Connections under Intermittent Water Supply","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and 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":"Università degli Studi di Perugia","keywords":"Hydrogeology; Environmental science; Water discharge; Water supply; Hydrology (agriculture); Environmental engineering; Water resource management; Engineering; Geotechnical engineering","score_opus":0.006885201086736449,"score_gpt":0.19080753278777504,"score_spread":0.18392233170103858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408663586","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":[0.9376258,0.00012692815,0.050970763,0.00031797515,0.000055320703,0.000115388546,0.0012778934,0.00041301822,0.009096971],"genre_scores_gemma":[0.99694914,0.00003337342,0.001609304,0.000012961234,0.000003181841,0.00005043751,0.00014402496,0.000009708146,0.001187916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997174,0.00007395026,0.000016891188,0.00007864964,0.000058055906,0.000055015473],"domain_scores_gemma":[0.99894625,0.0006210141,0.000120392746,0.000070496026,0.00019922592,0.000042626125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005344019,0.0003775609,0.0005322387,0.0003489983,0.00038193288,0.0007546742,0.0010650378,0.0015003183,0.0018110813],"category_scores_gemma":[0.0021899284,0.00027112954,0.00047525953,0.00031201672,0.0007963466,0.000616084,0.00037276826,0.0006896825,0.00022887648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003052302,0.000046125544,0.00134701,0.00001964449,0.0000045574184,0.000047530764,0.000013563536,0.99568224,0.0014016088,0.00044526576,0.00013829135,0.0008236455],"study_design_scores_gemma":[0.000007950226,0.000024638975,0.0003232772,0.0000018322228,0.0000018473505,0.000004138487,0.0000073316387,0.99902713,0.0004303809,0.0001030423,0.00006523784,0.000003228711],"about_ca_topic_score_codex":0.027757706,"about_ca_topic_score_gemma":0.0119282715,"teacher_disagreement_score":0.027757706,"about_ca_system_score_codex":0.0012200222,"about_ca_system_score_gemma":0.0010566169,"threshold_uncertainty_score":0.05519229},"labels":[],"label_agreement":null},{"id":"W4409234837","doi":"10.1088/1755-1315/1483/1/012013","title":"Virtual sensors for indirect strain measurement during Francis turbine startup","year":2025,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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":"Strain (injury); Francis turbine; Turbine; Computer science; Engineering; Mechanical engineering; Biology","score_opus":0.010770936348355523,"score_gpt":0.173780186273326,"score_spread":0.16300924992497048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409234837","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":[0.68177825,0.00045622222,0.31090158,0.00010682225,0.00014569977,0.000062624604,0.0006056526,0.0010335894,0.004909458],"genre_scores_gemma":[0.97939533,0.000058279027,0.01969455,0.0000156972,0.00001099593,0.000026268215,0.00010462459,0.000014904546,0.0006794227],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968016,0.00006266687,0.000011195538,0.00005848346,0.00016136932,0.00002612264],"domain_scores_gemma":[0.9996383,0.00012458529,0.000057721136,0.000041842155,0.00011979687,0.000017784643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023993175,0.0003678074,0.00031124693,0.0004907701,0.00017076447,0.00043514936,0.00046651997,0.0003928952,0.0008376134],"category_scores_gemma":[0.00096687814,0.00018682494,0.000119130535,0.00036954382,0.00019243143,0.00044000207,0.00042618433,0.00035848792,0.00022788333],"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":[0.0011125136,0.00017813736,0.023387372,0.00032821426,0.000077057346,0.00032496417,0.00049030944,0.092257306,0.5000074,0.0018476928,0.0026374175,0.37735158],"study_design_scores_gemma":[0.000032809883,0.00067096757,0.03990906,0.00004380389,0.000047986818,0.00017467933,0.00016159157,0.7359036,0.21847911,0.00095632306,0.0035355403,0.0000844384],"about_ca_topic_score_codex":0.0010523264,"about_ca_topic_score_gemma":0.0021539673,"teacher_disagreement_score":0.0010523264,"about_ca_system_score_codex":0.00022628091,"about_ca_system_score_gemma":0.00014592308,"threshold_uncertainty_score":0.0028021336},"labels":[],"label_agreement":null},{"id":"W4409497653","doi":"10.5006/c2025-00237","title":"Implications of Solar Facilities on Adjacent Pipeline Infrastructure – Commissioning and Monitoring","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Stantec (Canada)","funders":"","keywords":"Project commissioning; Pipeline (software); Computer science; Systems engineering; Engineering; Publishing; Operating system; Political science","score_opus":0.007306698361286053,"score_gpt":0.21373892513702059,"score_spread":0.20643222677573453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409497653","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":[0.9876324,0.00008127109,0.003293811,0.00021669088,0.000013245624,0.000054046395,0.00031135324,0.00015084186,0.008246246],"genre_scores_gemma":[0.99879336,0.0000217252,0.0006028059,0.0000109432185,0.0000019299614,0.000004104842,0.000049977276,0.000006584583,0.0005084713],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909663,0.00019393144,0.000032360276,0.00011408646,0.0004636784,0.00009935674],"domain_scores_gemma":[0.99772555,0.0008295247,0.0004381313,0.0001828062,0.0007138957,0.000110091736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009372838,0.00019667372,0.00018582243,0.0007627157,0.0006611046,0.00082699367,0.00034372802,0.00052867667,0.0026218176],"category_scores_gemma":[0.0026285995,0.00020270246,0.00020566142,0.0007438081,0.00038673214,0.0007591712,0.0004751617,0.000632399,0.0002635889],"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":[0.0017200238,0.000794937,0.3972086,0.00040231884,0.00015042967,0.0040499256,0.0012368148,0.20006363,0.22653246,0.0022736099,0.006078725,0.15948854],"study_design_scores_gemma":[0.0000919373,0.0030936564,0.63679904,0.00008451176,0.0001421867,0.0013667099,0.0037087342,0.15257806,0.19010584,0.0014661552,0.010458697,0.00010446165],"about_ca_topic_score_codex":0.012061163,"about_ca_topic_score_gemma":0.019179756,"teacher_disagreement_score":0.012061163,"about_ca_system_score_codex":0.0014929312,"about_ca_system_score_gemma":0.0004947626,"threshold_uncertainty_score":0.02398187},"labels":[],"label_agreement":null},{"id":"W4409510618","doi":"10.1061/jswbay.sweng-632","title":"Markov Chain Modeling of Combined Sewer Tank Overflow","year":2025,"lang":"en","type":"article","venue":"Journal of Sustainable Water in the Built Environment","topic":"Water Systems and 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":true,"ca_institutions":"McMaster University","funders":"","keywords":"Markov chain; Computer science; Combined sewer; Environmental science; Machine learning; Stormwater; Surface runoff","score_opus":0.0049269433249984574,"score_gpt":0.17977753637591334,"score_spread":0.17485059305091488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409510618","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":[0.6766543,0.00035275996,0.3118458,0.00072018534,0.000076566816,0.00011051328,0.0016860644,0.00033168204,0.008222261],"genre_scores_gemma":[0.9924045,0.00011102144,0.0047598807,0.000028558137,0.000009962709,0.00006342659,0.00039640372,0.000015049368,0.0022112166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959904,0.00010726707,0.000019128032,0.00009321942,0.000060310173,0.000121007186],"domain_scores_gemma":[0.9980856,0.0012554167,0.0002780371,0.00006140663,0.00020879658,0.000110700516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001059031,0.00052702636,0.00069285993,0.00068299583,0.00046297966,0.0009966495,0.00088960986,0.0008618294,0.0027833246],"category_scores_gemma":[0.0032648798,0.00045147652,0.0008427676,0.000593386,0.0007518898,0.0007716479,0.000790766,0.0008191457,0.00016918701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017387405,0.000008025052,0.0020740894,0.000005697021,0.000011750489,0.000036447185,0.00001611774,0.99370676,0.00013995638,0.0032414123,0.00009761552,0.0006448138],"study_design_scores_gemma":[0.0000026030727,0.0000033866747,0.000293196,0.0000014795836,0.0000035496953,0.0000022025893,0.000005996041,0.9987821,0.000038340222,0.0008143634,0.000050199018,0.0000026236464],"about_ca_topic_score_codex":0.07591821,"about_ca_topic_score_gemma":0.043344695,"teacher_disagreement_score":0.07591821,"about_ca_system_score_codex":0.0015143495,"about_ca_system_score_gemma":0.0017578588,"threshold_uncertainty_score":0.15095264},"labels":[],"label_agreement":null},{"id":"W4409528337","doi":"10.5006/c2000-00481","title":"Pipeline Risk Assessment and Effective Resource Allocation","year":2000,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Pipeline (software); Resource (disambiguation); Computer science; Pipeline transport; Resource allocation; Petroleum engineering; Forensic engineering; Environmental science; Risk analysis (engineering); Engineering; Business; Environmental engineering","score_opus":0.002725482206081061,"score_gpt":0.19604032592393006,"score_spread":0.193314843717849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409528337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06827286,0.00077020744,0.9030094,0.0011668318,0.000049112852,0.00024476697,0.00030568923,0.00040546712,0.025775652],"genre_scores_gemma":[0.8406426,0.00051603396,0.15204035,0.000077365796,0.000057127407,0.00018807413,0.00027726137,0.000074261814,0.006126986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964945,0.0015522061,0.00019452962,0.00030748558,0.0012349883,0.0002162631],"domain_scores_gemma":[0.9963821,0.0018753216,0.0005594612,0.00025250268,0.0008090233,0.0001215406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003825483,0.0009429052,0.0008111344,0.0037698916,0.00060632156,0.0031379727,0.00084376713,0.00084446697,0.0050422074],"category_scores_gemma":[0.012811284,0.0003497589,0.00046556618,0.0021839433,0.0018804695,0.0034241586,0.001970873,0.00087113236,0.0004523055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012725987,0.00008627668,0.004635244,0.00014136326,0.000054696637,0.00011010295,0.00022670694,0.5971552,0.0014873159,0.22311483,0.0034411505,0.1694198],"study_design_scores_gemma":[0.000018270368,0.000074980184,0.0011288588,0.000055124245,0.000020680325,0.000056882425,0.00024304983,0.77598554,0.0020100244,0.21367444,0.0067071547,0.000024954943],"about_ca_topic_score_codex":0.004872885,"about_ca_topic_score_gemma":0.00287988,"teacher_disagreement_score":0.0050422074,"about_ca_system_score_codex":0.0024234569,"about_ca_system_score_gemma":0.0027200163,"threshold_uncertainty_score":0.020231366},"labels":[],"label_agreement":null},{"id":"W4409537335","doi":"10.1371/journal.pwat.0000307","title":"Training for the test: Using multi-objective training to improve ANN ensemble forecasts of household residual chlorine in emergencies","year":2025,"lang":"en","type":"article","venue":"PLOS Water","topic":"Water Systems and 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":"Achmea; Grand Challenges Canada; Natural Sciences and Engineering Research Council of Canada; Enhancing Learning and Research for Humanitarian Assistance","keywords":"Training (meteorology); Residual; Test (biology); Training set; Artificial intelligence; Machine learning; Computer science; Statistics; Mathematics; Meteorology; Algorithm; Geology; Geography","score_opus":0.09134220930533829,"score_gpt":0.2589263150914314,"score_spread":0.1675841057860931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409537335","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":[0.6616944,0.000751292,0.33047026,0.0005479014,0.00012363904,0.000108258864,0.00026698952,0.0017154788,0.004321784],"genre_scores_gemma":[0.96086127,0.00008084489,0.037809566,0.000115163646,0.000018689492,0.00003988624,0.00026684804,0.000049595314,0.00075818406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997137,0.000108947585,0.000019257554,0.000069231355,0.00004483536,0.000043917287],"domain_scores_gemma":[0.9986696,0.00075123506,0.00012603203,0.000115389215,0.00029134852,0.00004643243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001947819,0.0012412304,0.0007172191,0.00044891768,0.0002550441,0.00059845025,0.00071137404,0.0008079492,0.0008373329],"category_scores_gemma":[0.0043507623,0.00035630647,0.00059295545,0.00033753747,0.00026006962,0.00087857095,0.0005993516,0.0010451742,0.00017782736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008913207,0.0000721596,0.002495751,0.000022044404,0.000048356113,0.00002078793,0.000019816713,0.9602119,0.00112535,0.0001520852,0.00032871246,0.035413865],"study_design_scores_gemma":[0.000003959632,0.000033863762,0.0003712302,0.0000036071772,0.000004906879,0.0000022582055,0.0000033436236,0.99886984,0.0005493167,0.00009693738,0.000057938407,0.0000028117975],"about_ca_topic_score_codex":0.010993152,"about_ca_topic_score_gemma":0.009050494,"teacher_disagreement_score":0.010993152,"about_ca_system_score_codex":0.00047381365,"about_ca_system_score_gemma":0.0006794105,"threshold_uncertainty_score":0.021858335},"labels":[],"label_agreement":null},{"id":"W4409733369","doi":"10.1061/jccee5.cpeng-6393","title":"A Deep Learning Autoregressive Forecasting Model for Probabilistic Water Pipe Break Prediction","year":2025,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"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":"Autoregressive model; Probabilistic forecasting; Probabilistic logic; Computer science; Deep learning; Artificial intelligence; Autoregressive integrated moving average; Predictive modelling; Statistical model; Engineering; Machine learning; Time series; Econometrics; Mathematics","score_opus":0.008891511838426139,"score_gpt":0.19562682057009675,"score_spread":0.1867353087316706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409733369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1306261,0.0011017111,0.8584093,0.0012247007,0.00018376284,0.000042488053,0.0011051964,0.0018371369,0.0054696035],"genre_scores_gemma":[0.95029676,0.0005389208,0.04330579,0.00014433566,0.00006117906,0.000071032475,0.0012150045,0.00004941295,0.004317448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997954,0.00003455753,0.000014991586,0.00006915248,0.000050164337,0.00003577392],"domain_scores_gemma":[0.9996306,0.0001652194,0.000056027566,0.000020970701,0.000110791545,0.000016369078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058447133,0.0006474371,0.00052581733,0.00038907272,0.0002609108,0.0005247754,0.0010225401,0.0006840853,0.0012935965],"category_scores_gemma":[0.0016103351,0.00033951015,0.00069506455,0.00059754093,0.00023516227,0.00076072244,0.000455325,0.0017026925,0.00034919314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041332845,0.000029889732,0.0020958576,0.000023538374,0.000028158287,0.000052671916,0.00002447844,0.96288544,0.0008547181,0.002093704,0.0012585511,0.03061165],"study_design_scores_gemma":[9.326572e-7,0.000003903269,0.00013217799,0.0000016046091,0.0000025852453,0.0000024985209,0.000001191261,0.9993223,0.00008753227,0.00036345655,0.00008001907,0.0000017658025],"about_ca_topic_score_codex":0.040354613,"about_ca_topic_score_gemma":0.030724794,"teacher_disagreement_score":0.040354613,"about_ca_system_score_codex":0.0007059974,"about_ca_system_score_gemma":0.00087541935,"threshold_uncertainty_score":0.080239415},"labels":[],"label_agreement":null},{"id":"W4410069274","doi":"10.3390/smartcities8030078","title":"A Systematic Literature Review on Flow Data-Based Techniques for Automated Leak Management in Water Distribution Systems","year":2025,"lang":"en","type":"article","venue":"Smart Cities","topic":"Water Systems and 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":"Leak; Computer science; Environmental science; Environmental engineering","score_opus":0.009640102179156259,"score_gpt":0.23311789423311363,"score_spread":0.22347779205395737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410069274","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":[0.00053939334,0.996231,0.0012358581,0.0004897063,0.00013912974,0.0001504423,0.00033702696,0.000024103245,0.0008533677],"genre_scores_gemma":[0.004592702,0.99176264,0.0025843447,0.00035167774,0.000087242974,0.00019868882,0.00026230127,0.000009926494,0.0001503511],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99572766,0.0012298472,0.0015265448,0.000413858,0.0009827075,0.00011938637],"domain_scores_gemma":[0.96885675,0.02526716,0.0026749137,0.00049453333,0.0025081947,0.00019840061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005306597,0.00158929,0.0030082776,0.0123357745,0.0006211284,0.0025915327,0.0017337507,0.0018961275,0.0062300903],"category_scores_gemma":[0.030551454,0.00081145484,0.004618602,0.010631039,0.0008049947,0.0027286315,0.0012979166,0.0012065311,0.0009427916],"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":[0.00010030425,0.000067072404,0.000717675,0.61055636,0.0011827913,0.0001419583,0.00034836587,0.0010693184,0.00038795386,0.0016692037,0.0075126174,0.37624627],"study_design_scores_gemma":[0.00006216251,0.00028340667,0.0041928277,0.7877317,0.009938909,0.00053277635,0.0004459893,0.0007325871,0.00066957885,0.0021743868,0.19314894,0.00008663015],"about_ca_topic_score_codex":0.009061522,"about_ca_topic_score_gemma":0.020552006,"teacher_disagreement_score":0.0123357745,"about_ca_system_score_codex":0.0019926357,"about_ca_system_score_gemma":0.013613713,"threshold_uncertainty_score":0.02806431},"labels":[],"label_agreement":null},{"id":"W4410154364","doi":"10.1061/jwrmd5.wreng-6568","title":"Integration of Incomplete Mixing at Junctions of Water Distribution Networks in Contamination Source Identification Tools","year":2025,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Identification (biology); Contamination; Mixing (physics); Water contamination; Environmental science; Distribution (mathematics); Computer science; Environmental engineering; Physics; Mathematics; Ecology","score_opus":0.010245146782259743,"score_gpt":0.20959620177373423,"score_spread":0.19935105499147449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410154364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020578181,0.00004116534,0.97806257,0.000035825182,0.000006925438,0.000035078992,0.00003873485,0.0007213957,0.00048024434],"genre_scores_gemma":[0.6162691,0.00009802038,0.38224697,0.000043324806,0.000019853387,0.00009769533,0.00022138901,0.0001341695,0.0008696387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980422,0.00072656036,0.00014259772,0.00043609148,0.00050029374,0.00015212722],"domain_scores_gemma":[0.9935173,0.0037267199,0.00091170036,0.00073613226,0.00094146567,0.00016673876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032345625,0.0013113774,0.0013483583,0.0013441138,0.00061482866,0.001977827,0.0016343178,0.0012741528,0.0011927367],"category_scores_gemma":[0.010888781,0.00079422013,0.0011790673,0.00082494266,0.00084700465,0.0031059668,0.0022068752,0.0013093662,0.00031357908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011969103,0.00007767696,0.0047405693,0.00007712446,0.00007883107,0.00015356793,0.00016779888,0.9281471,0.003319827,0.005150472,0.00024325532,0.05772415],"study_design_scores_gemma":[0.00000435255,0.000024258545,0.00027776073,0.0000047885037,0.000009891768,0.00002128767,0.000015938755,0.99572325,0.001953484,0.0016965407,0.00026090295,0.0000075249154],"about_ca_topic_score_codex":0.005306607,"about_ca_topic_score_gemma":0.005145183,"teacher_disagreement_score":0.005306607,"about_ca_system_score_codex":0.00072471664,"about_ca_system_score_gemma":0.0024224494,"threshold_uncertainty_score":0.017106235},"labels":[],"label_agreement":null},{"id":"W4410334891","doi":"10.1117/12.3050511","title":"LeakGPT: detecting water pipe leaks using vision language models","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Okanagan University College; University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing; Computer vision","score_opus":0.00862643323906469,"score_gpt":0.2204501260824663,"score_spread":0.2118236928434016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410334891","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":[0.4620739,0.0046696854,0.2818863,0.0022438646,0.0010287744,0.0021576225,0.06686826,0.16501842,0.014053095],"genre_scores_gemma":[0.5646666,0.00069126417,0.2709846,0.0015683051,0.00015039976,0.0011492313,0.15218247,0.0008606241,0.00774657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986469,0.00021492758,0.00010206835,0.00060821796,0.0002683673,0.00015964813],"domain_scores_gemma":[0.9988953,0.00043830374,0.00010561327,0.0001793738,0.00028175733,0.00009968852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010779775,0.002729056,0.00089476904,0.0018432533,0.00052661286,0.0012128911,0.0026071302,0.0020809874,0.0028097976],"category_scores_gemma":[0.003870301,0.00042769648,0.0018570352,0.000842251,0.00046891728,0.002112243,0.0018590301,0.0021760138,0.0023245676],"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":[0.001241501,0.0025831363,0.018616175,0.0016390868,0.0005119492,0.0010917428,0.0005318107,0.10032291,0.05476589,0.0023272128,0.15225929,0.66410923],"study_design_scores_gemma":[0.00024962553,0.0005538211,0.008104541,0.000068885995,0.000120044024,0.00033612,0.00033916853,0.9438212,0.026451152,0.0025221051,0.01733121,0.00010208896],"about_ca_topic_score_codex":0.026792094,"about_ca_topic_score_gemma":0.036390867,"teacher_disagreement_score":0.026792094,"about_ca_system_score_codex":0.0014767683,"about_ca_system_score_gemma":0.0018419191,"threshold_uncertainty_score":0.053272307},"labels":[],"label_agreement":null},{"id":"W4410410386","doi":"10.1061/9780784486184.087","title":"Leveraging Long Short-Term Memory for Individual Water Main Failure Prediction Using Pipe Intrinsic Variables and Climate Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Term (time); Computer science; Long short term memory; Reliability engineering; Environmental science; Machine learning; Engineering; Artificial neural network","score_opus":0.026619963407314793,"score_gpt":0.23249988700407428,"score_spread":0.2058799235967595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410410386","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":[0.7253246,0.0009760907,0.26556405,0.000642222,0.00010674937,0.00005932002,0.0027152796,0.0013969073,0.0032148112],"genre_scores_gemma":[0.98750323,0.00017072695,0.009803581,0.000052866,0.00001688792,0.000027397764,0.0011925824,0.000017134227,0.00121558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999049,0.000011108387,0.0000057072994,0.00003300085,0.00001850578,0.000026870215],"domain_scores_gemma":[0.99956685,0.0001752458,0.00006282461,0.000033449887,0.0001323226,0.000029348525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033727128,0.00079572044,0.0003791965,0.00059211114,0.00021983722,0.0004486187,0.0007357635,0.00053983147,0.00092473754],"category_scores_gemma":[0.0013224789,0.00026949868,0.00041428712,0.0005643231,0.0002893945,0.0007830212,0.0005372083,0.0009475346,0.00028907432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001404232,0.00014426935,0.033557247,0.00006264053,0.00008317117,0.00014324332,0.00006463524,0.8754955,0.002805408,0.0005604607,0.0020195337,0.08492346],"study_design_scores_gemma":[0.0000021107646,0.000014094684,0.0033804427,0.000005772984,0.0000072087737,0.000006172551,0.000011075627,0.99534386,0.0006802267,0.00039662558,0.00014760633,0.000004637913],"about_ca_topic_score_codex":0.072514504,"about_ca_topic_score_gemma":0.11799202,"teacher_disagreement_score":0.072514504,"about_ca_system_score_codex":0.00082317274,"about_ca_system_score_gemma":0.0011344115,"threshold_uncertainty_score":0.14418483},"labels":[],"label_agreement":null},{"id":"W4410410527","doi":"10.1061/9780784486184.089","title":"Mitigating Pipe Corrosion in Water Distribution Systems: Strategies for Sustainability and Resilience","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Resilience (materials science); Sustainability; Corrosion; Environmental science; Distribution (mathematics); Computer science; Environmental resource management; Materials science; Metallurgy","score_opus":0.004191853191486101,"score_gpt":0.2138842858480979,"score_spread":0.2096924326566118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410410527","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":[0.19995162,0.042441398,0.62947595,0.05598804,0.0005502232,0.00056383753,0.00030628653,0.0011060464,0.069616616],"genre_scores_gemma":[0.9475383,0.012662809,0.034866244,0.0005511858,0.000110685076,0.00014501446,0.00007124848,0.000046569214,0.004007869],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99952483,0.00018515965,0.000022044975,0.000058654492,0.0001249096,0.00008448616],"domain_scores_gemma":[0.9992993,0.00022362465,0.00015524485,0.000058024376,0.00018155605,0.00008231589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011656216,0.00079507125,0.000482974,0.0007761695,0.0005862947,0.0018428821,0.0010150583,0.0011406271,0.0032524837],"category_scores_gemma":[0.0022494358,0.00018776071,0.0004340618,0.0005246406,0.0012689396,0.0027886187,0.002135345,0.00079680135,0.00034116802],"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":[0.00017591783,0.00049775385,0.007761256,0.0036764776,0.0002637175,0.00046163777,0.0012860897,0.30155885,0.045160018,0.21322995,0.013911124,0.41201723],"study_design_scores_gemma":[0.00012599297,0.0019758544,0.011081146,0.0027414397,0.00038540617,0.0005890068,0.010436956,0.37557855,0.043537818,0.34152514,0.2118399,0.00018284484],"about_ca_topic_score_codex":0.0028139104,"about_ca_topic_score_gemma":0.0038944301,"teacher_disagreement_score":0.0032524837,"about_ca_system_score_codex":0.0012858142,"about_ca_system_score_gemma":0.0020004942,"threshold_uncertainty_score":0.0108805895},"labels":[],"label_agreement":null},{"id":"W4410996253","doi":"10.5194/egusphere-2025-644","title":"Optimizing Methane Emission Source Localization in Oil and Gas Facilities Using Lagrangian Stochastic Models and Gradient-Based Detection Tools","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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":"Cancer Care Nova Scotia; St. Francis Xavier University; Memorial University of Newfoundland","funders":"","keywords":"Methane; Lagrangian; Petroleum engineering; Fossil fuel; Environmental science; Computer science; Applied mathematics; Chemistry; Geology; Mathematics; Waste management; Engineering","score_opus":0.026588271447114918,"score_gpt":0.22098102471312617,"score_spread":0.19439275326601124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410996253","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":[0.15185045,0.00010882943,0.84491765,0.00019133142,0.000027870554,0.000044362878,0.0000898467,0.0010578238,0.0017118164],"genre_scores_gemma":[0.9215369,0.000042340365,0.07733082,0.000048646954,0.000007822625,0.000045708388,0.00011876088,0.00004690984,0.00082203065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984205,0.000048132046,0.0000067977567,0.000035620586,0.00004049068,0.000026913996],"domain_scores_gemma":[0.9993931,0.0003386049,0.00009092107,0.00002640421,0.00011280455,0.000038148675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052767404,0.00065516884,0.00046802725,0.00036305113,0.00025633175,0.0004840961,0.0006745488,0.00047148255,0.0006679161],"category_scores_gemma":[0.0018684587,0.00031663576,0.0004292858,0.0002726109,0.00038033407,0.0004791837,0.0006580624,0.0005175812,0.0001800905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033120054,0.0000189905,0.0010218142,0.000016222259,0.00000880169,0.00001848625,0.000011915354,0.989488,0.001508296,0.0004823473,0.00013275226,0.007259359],"study_design_scores_gemma":[0.0000023023447,0.000006536815,0.00005823572,6.2547554e-7,0.0000010287497,0.0000014732112,0.0000017051585,0.9995865,0.00023664054,0.00007958657,0.000024216764,0.0000011530188],"about_ca_topic_score_codex":0.019086936,"about_ca_topic_score_gemma":0.016591754,"teacher_disagreement_score":0.019086936,"about_ca_system_score_codex":0.00080602797,"about_ca_system_score_gemma":0.0014656326,"threshold_uncertainty_score":0.03795165},"labels":[],"label_agreement":null},{"id":"W4411309171","doi":"10.1061/jwrmd5.wreng-6560","title":"Energy Dissipation in Intermittent Water Supplies Depends on Household Behavior","year":2025,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dissipation; Environmental science; Energy (signal processing); Mechanics; Environmental engineering; Physics; Thermodynamics","score_opus":0.008995790174621973,"score_gpt":0.20522597071025706,"score_spread":0.19623018053563507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411309171","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":[0.97966135,0.00003273908,0.016854236,0.00021337123,0.000007640118,0.000032907865,0.00017284273,0.00006332121,0.0029616673],"genre_scores_gemma":[0.9983895,0.000026810078,0.00082947576,0.000011828954,8.8731775e-7,0.000017407343,0.000039576313,0.000007431306,0.0006771846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9997032,0.000109011504,0.000013421981,0.000051421594,0.000046635538,0.0000762609],"domain_scores_gemma":[0.99905044,0.0005615776,0.00013298386,0.00008902081,0.00010262398,0.000063293395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000365417,0.00026278794,0.00032474316,0.00023403896,0.00039590453,0.00083326007,0.00055497425,0.00048701128,0.002877779],"category_scores_gemma":[0.0025929469,0.0003098049,0.0004436917,0.00043085846,0.0007858093,0.0013521435,0.0006702755,0.00048596162,0.00021030576],"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":[0.00022810264,0.00025937398,0.05112036,0.00014409953,0.0000660689,0.00047903694,0.0015289827,0.9106986,0.010972609,0.009775341,0.0012039558,0.013523389],"study_design_scores_gemma":[0.000026067084,0.00022713176,0.031198261,0.000019019302,0.000030672323,0.000070000315,0.0014235526,0.9599506,0.0018015206,0.004052125,0.0011580953,0.000042985062],"about_ca_topic_score_codex":0.015239587,"about_ca_topic_score_gemma":0.009732634,"teacher_disagreement_score":0.015239587,"about_ca_system_score_codex":0.00093806064,"about_ca_system_score_gemma":0.00051218685,"threshold_uncertainty_score":0.03030181},"labels":[],"label_agreement":null},{"id":"W4411493756","doi":"10.2166/wst.2025.079","title":"Optimizing monitoring strategies for urban drainage systems via bilayer iterative clustering","year":2025,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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 Alberta","funders":"National Key Research and Development Program of China","keywords":"Dynamic time warping; Cluster analysis; Computer science; Data mining; Similarity (geometry); Software deployment; Real-time computing; Artificial intelligence","score_opus":0.009242747876322196,"score_gpt":0.23113082943090316,"score_spread":0.22188808155458095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411493756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074998915,0.00014804183,0.92217267,0.00011675466,0.000011035964,0.00012179167,0.00006563661,0.00032286468,0.0020422828],"genre_scores_gemma":[0.79270554,0.00011900541,0.20539752,0.000047418096,0.00001037794,0.00021954006,0.00016581095,0.00006721203,0.0012675686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995727,0.00013254452,0.000025418905,0.000100685706,0.00009663118,0.00007204452],"domain_scores_gemma":[0.99889886,0.0005581096,0.00016693886,0.000067542984,0.00025989916,0.00004861822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007909468,0.0009060718,0.0009076292,0.0011744184,0.0006165927,0.0007999479,0.0012694491,0.0008441326,0.0011374522],"category_scores_gemma":[0.0033584626,0.0005811124,0.00065102556,0.0008872409,0.0005925987,0.0009663933,0.0011253421,0.0005049266,0.00020709274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022477012,0.000027381835,0.0009800518,0.000025913925,0.000019168947,0.00002484581,0.000039981624,0.9782632,0.0012179546,0.0013881854,0.00022558801,0.017765181],"study_design_scores_gemma":[0.0000023895911,0.000011548131,0.00015423434,0.0000020726743,0.0000028742115,0.0000046783603,0.000012397112,0.9985488,0.00031568395,0.0008361857,0.000106272746,0.000002858423],"about_ca_topic_score_codex":0.009206699,"about_ca_topic_score_gemma":0.009417985,"teacher_disagreement_score":0.009206699,"about_ca_system_score_codex":0.0009873351,"about_ca_system_score_gemma":0.0015646949,"threshold_uncertainty_score":0.018306255},"labels":[],"label_agreement":null},{"id":"W4411926896","doi":"10.3390/w17131988","title":"Towards a Realistic Data-Driven Leak Localization in Water Distribution Networks","year":2025,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and 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":"Brock University","funders":"","keywords":"Leak; Environmental science; Leak detection; Distribution (mathematics); Computer science; Environmental engineering; Mathematics","score_opus":0.011309862426710369,"score_gpt":0.21682172706994865,"score_spread":0.20551186464323828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411926896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13000272,0.0001718672,0.8666961,0.00083597266,0.000029091816,0.000048355654,0.00041952168,0.00055403274,0.0012423865],"genre_scores_gemma":[0.9073356,0.00011085452,0.09130797,0.00010317435,0.000020351394,0.000093586415,0.00043797467,0.00007283333,0.0005176986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924123,0.0004074336,0.000034327168,0.00015124118,0.00011351547,0.000052313535],"domain_scores_gemma":[0.9962373,0.0024325142,0.00042070702,0.0002486875,0.0005555576,0.00010528905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00247498,0.0006722545,0.0006850332,0.0005296221,0.00034836092,0.001045348,0.0013706759,0.0014310279,0.00059018255],"category_scores_gemma":[0.012842808,0.00060940685,0.00047464453,0.00056072033,0.0009804817,0.0027715785,0.0011774471,0.0015221839,0.00013969175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009270402,0.000006072099,0.00036697442,0.000010663575,0.0000029022217,0.0000074732275,0.000008076756,0.9970788,0.00016379813,0.0011379201,0.000074781354,0.0011333999],"study_design_scores_gemma":[0.0000010843088,0.0000023098899,0.000032358872,0.0000015249541,3.7550376e-7,0.0000011755086,0.0000031819561,0.9990496,0.0000887632,0.0007870845,0.000031543696,9.320578e-7],"about_ca_topic_score_codex":0.011063246,"about_ca_topic_score_gemma":0.006919919,"teacher_disagreement_score":0.011063246,"about_ca_system_score_codex":0.0015781253,"about_ca_system_score_gemma":0.0012258062,"threshold_uncertainty_score":0.02199769},"labels":[],"label_agreement":null},{"id":"W4411960407","doi":"10.1061/jitse4.iseng-2646","title":"Beyond the Pipes: Performance Management of Water Supply Systems under Uncertainty","year":2025,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and 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 Windsor","funders":"","keywords":"Environmental science; Water supply; Engineering; Environmental resource management; Business; Natural resource economics; Computer science; Risk analysis (engineering); Environmental engineering; Economics","score_opus":0.002857251735146443,"score_gpt":0.18040673594363857,"score_spread":0.17754948420849212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411960407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3435846,0.00068496406,0.6419488,0.0010162558,0.000033524724,0.00019400837,0.00039122018,0.00014841172,0.011998226],"genre_scores_gemma":[0.9881656,0.00018334137,0.01107695,0.000017364731,0.000011577104,0.000035472185,0.000071799645,0.00001283663,0.00042501884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973902,0.001130378,0.00010365886,0.000311036,0.0007621089,0.00030253612],"domain_scores_gemma":[0.99568117,0.002922492,0.00054045796,0.00012308123,0.00063297787,0.000099827375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047386736,0.0009803043,0.00078787387,0.0016805737,0.00067185133,0.0029898325,0.00088847434,0.0009190167,0.0008604624],"category_scores_gemma":[0.012684611,0.0003870355,0.0005803843,0.0017887156,0.0013140023,0.0031607524,0.001238113,0.0009013278,0.0000619566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043427957,0.00002252888,0.0030931192,0.000054007087,0.0000483381,0.00007953304,0.00016255608,0.9672404,0.00065143744,0.014827756,0.00019052421,0.013586318],"study_design_scores_gemma":[0.0000031532163,0.000058740105,0.0030231008,0.000022642096,0.000014403726,0.000020162952,0.00022448257,0.9783342,0.0005973314,0.017255032,0.00042366874,0.000023061515],"about_ca_topic_score_codex":0.02074473,"about_ca_topic_score_gemma":0.010095117,"teacher_disagreement_score":0.02074473,"about_ca_system_score_codex":0.003313454,"about_ca_system_score_gemma":0.0022558342,"threshold_uncertainty_score":0.041247964},"labels":[],"label_agreement":null},{"id":"W4412211738","doi":"","title":"Global Water Monitor 2023, Summary Report","year":2024,"lang":"en","type":"report","venue":"Publication Database GFZ (GFZ German Research Centre for Geosciences)","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"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":"Environmental science; Computer science","score_opus":0.04888039925003252,"score_gpt":0.3477462986933844,"score_spread":0.2988658994433519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412211738","genre_codex":"dataset","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":[0.0019236609,0.0059287744,0.003608705,0.008721301,0.005469291,0.0011034219,0.84693736,0.0028337478,0.123473704],"genre_scores_gemma":[0.02353858,0.010906518,0.012207728,0.005248363,0.0016290037,0.00297252,0.821621,0.0013331493,0.120543085],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902725,0.00014158228,0.00007985139,0.00013943858,0.0004914453,0.00012047866],"domain_scores_gemma":[0.99823964,0.000099227684,0.00013830265,0.00010037954,0.001254672,0.00016776953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025704901,0.0023550338,0.0010436027,0.0022583103,0.0005615804,0.0029576735,0.0018596097,0.0020517744,0.04030025],"category_scores_gemma":[0.0029114375,0.0007425935,0.00072638056,0.0056057153,0.00033622023,0.0031268217,0.0013903328,0.0019324853,0.032713894],"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":[0.000114010545,0.00004464572,0.0008724418,0.00048523053,0.000028933518,0.000031118227,0.0000144889955,0.00038960436,0.00031512484,0.0011430039,0.9717435,0.024817986],"study_design_scores_gemma":[0.00009679215,0.000046833713,0.007837601,0.00020806657,0.000036252746,0.000022289812,0.00004258869,0.00065299793,0.0004404379,0.001225578,0.9893693,0.000021287837],"about_ca_topic_score_codex":0.082769305,"about_ca_topic_score_gemma":0.058206894,"teacher_disagreement_score":0.082769305,"about_ca_system_score_codex":0.0022410417,"about_ca_system_score_gemma":0.008059403,"threshold_uncertainty_score":0.16457504},"labels":[],"label_agreement":null},{"id":"W4412609060","doi":"10.22541/essoar.175330836.63783525/v1","title":"A Filtering Physics-Informed Neural Operator for Online Hydraulic Transient Teconstruction","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and 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 Saskatchewan","funders":"China Scholarship Council","keywords":"Transient (computer programming); Operator (biology); Artificial neural network; Computer science; Physics; Artificial intelligence; Chemistry; Programming language","score_opus":0.018323213775407108,"score_gpt":0.23886272322259713,"score_spread":0.22053950944719003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412609060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005018257,0.000045728473,0.99419373,0.000054105145,0.000018675462,0.000012330173,0.000019336285,0.00014528501,0.0004926038],"genre_scores_gemma":[0.6144486,0.00026649513,0.3796962,0.00024932425,0.00010668663,0.00017940887,0.00028434757,0.00018268067,0.0045863083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997415,0.00005770755,0.000012822072,0.000057980338,0.00010238117,0.000027552442],"domain_scores_gemma":[0.9994111,0.00032463841,0.00006438985,0.000047838617,0.00011501438,0.000037116457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079450017,0.000685971,0.00055898796,0.00029860513,0.00025238396,0.00053447933,0.0010250821,0.0009321478,0.0013138014],"category_scores_gemma":[0.0026692713,0.0002716196,0.000416012,0.00031701545,0.00068686233,0.00097638246,0.001178543,0.0010986191,0.00022274106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007140373,0.00004098983,0.0004155696,0.00006645911,0.000018251509,0.000051386003,0.00004735508,0.9094965,0.006421511,0.010358369,0.0010178325,0.07199447],"study_design_scores_gemma":[0.0000014118455,0.000008569741,0.000020327307,0.0000012698232,9.154064e-7,0.00000427546,0.0000011863232,0.9986645,0.00033134056,0.00084476045,0.00011941406,0.0000018870988],"about_ca_topic_score_codex":0.003630437,"about_ca_topic_score_gemma":0.0037041036,"teacher_disagreement_score":0.003630437,"about_ca_system_score_codex":0.00041310638,"about_ca_system_score_gemma":0.001146829,"threshold_uncertainty_score":0.0072185993},"labels":[],"label_agreement":null},{"id":"W4412799001","doi":"10.2166/wst.2025.097","title":"Exploring wastewater treatment plant operation and performance using k-means and Gaussian mixture clustering","year":2025,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Water Systems and 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":"Current Water Technologies (Canada); Water Security Agency; York University","funders":"Mitacs","keywords":"Cluster analysis; Computer science; Data mining; Downstream (manufacturing); Upstream (networking); Set (abstract data type); Machine learning; Biochemical engineering; Artificial intelligence; Engineering; Operations management","score_opus":0.023696164262542705,"score_gpt":0.20697615502817251,"score_spread":0.1832799907656298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412799001","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":[0.61890686,0.0002743777,0.3744283,0.00028092312,0.000031662526,0.00021404689,0.0012675953,0.0014611529,0.0031351],"genre_scores_gemma":[0.87845844,0.00015062776,0.11849872,0.000029390896,0.000009562653,0.00011339725,0.001258463,0.000110741676,0.0013706598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99925643,0.0001951581,0.000055818688,0.00019645444,0.0002092661,0.000086742984],"domain_scores_gemma":[0.99827087,0.00089040725,0.00022997358,0.00017351836,0.00038781684,0.000047417736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013654168,0.001127537,0.0007979247,0.00248787,0.000696022,0.0013201131,0.0009073738,0.0009921829,0.00075833],"category_scores_gemma":[0.0036808094,0.00041262872,0.0011935169,0.0024850257,0.00040978284,0.00092634914,0.00060278684,0.00063962623,0.00032919474],"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":[0.00046219563,0.00032231302,0.036230437,0.00024427762,0.0002521542,0.000093488015,0.000535984,0.8388145,0.012949006,0.0021275207,0.0014318809,0.10653622],"study_design_scores_gemma":[0.000008697723,0.0000630637,0.014026136,0.000010329354,0.000021655893,0.000022407126,0.00020135571,0.97874624,0.004581941,0.0017253519,0.0005434103,0.00004938756],"about_ca_topic_score_codex":0.033518776,"about_ca_topic_score_gemma":0.033484414,"teacher_disagreement_score":0.033518776,"about_ca_system_score_codex":0.0014454776,"about_ca_system_score_gemma":0.0014527515,"threshold_uncertainty_score":0.06664735},"labels":[],"label_agreement":null},{"id":"W4412809363","doi":"10.1029/2024wr039551","title":"Modeling Intermittent Water Supply in SWMM: A Critical Review With Reproducible Recommendations and a Python Package","year":2025,"lang":"en","type":"review","venue":"Water Resources Research","topic":"Water Systems and 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":"Hudbay Minerals (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Python (programming language); Water supply; Environmental science; Computer science; Hydrology (agriculture); Engineering; Environmental engineering; Operating system; Geotechnical engineering","score_opus":0.09472601433368738,"score_gpt":0.3720715350234275,"score_spread":0.2773455206897401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412809363","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":[0.0021796667,0.3428253,0.5800327,0.039867863,0.010047018,0.0010281417,0.0030952145,0.011029154,0.009895],"genre_scores_gemma":[0.026130604,0.4374881,0.4895179,0.0119568715,0.006892033,0.0033272975,0.005666659,0.009577726,0.009442842],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9824577,0.0071041947,0.0026493324,0.0010912291,0.006307292,0.00039024546],"domain_scores_gemma":[0.9185916,0.03444228,0.0044195345,0.007560776,0.033646327,0.0013395114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041266497,0.0023131147,0.0019770297,0.0055731456,0.0009913859,0.0041194805,0.011564634,0.0028950816,0.0076984297],"category_scores_gemma":[0.111546956,0.0020363,0.0042274003,0.0058209724,0.0026172055,0.008415687,0.005419459,0.007122015,0.0065405164],"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":[0.000120005716,0.00013241709,0.0016210478,0.030359648,0.000691498,0.0003035263,0.0008397783,0.024634399,0.0017867411,0.06647773,0.28166714,0.5913662],"study_design_scores_gemma":[0.000039551243,0.00011949868,0.0008224393,0.018969182,0.00047242682,0.0002327928,0.00017969182,0.019667972,0.003021753,0.018836487,0.93743026,0.0002078447],"about_ca_topic_score_codex":0.01476731,"about_ca_topic_score_gemma":0.010422439,"teacher_disagreement_score":0.041266497,"about_ca_system_score_codex":0.0034084306,"about_ca_system_score_gemma":0.015461651,"threshold_uncertainty_score":0.21824074},"labels":[],"label_agreement":null},{"id":"W4412974672","doi":"10.1061/jpcfev.cfeng-5149","title":"An Active-Learning Framework for Efficient Training and Bias Mitigation in Probabilistic Forecasting of Water Pipeline Failures","year":2025,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Water Systems and 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":"Pipeline (software); Training (meteorology); Probabilistic logic; Computer science; Reliability engineering; Machine learning; Artificial intelligence; Engineering; Environmental science; Risk analysis (engineering); Business; Meteorology","score_opus":0.018385580788080422,"score_gpt":0.2214290503428906,"score_spread":0.20304346955481017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412974672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016506907,0.00026990313,0.98178583,0.00018475983,0.000028985127,0.00003068373,0.00005047716,0.00056583516,0.00057658245],"genre_scores_gemma":[0.77651864,0.00032297845,0.21997844,0.00027162855,0.00010640201,0.0002495686,0.00032119398,0.00009525324,0.0021358863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994253,0.00018458182,0.00004412217,0.00014515848,0.00012421663,0.00007662302],"domain_scores_gemma":[0.99835443,0.0010112275,0.00012800895,0.00008735848,0.00035720845,0.00006183323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023200768,0.0008565223,0.0010528304,0.00063012424,0.0004551369,0.0008842899,0.0023176959,0.0012121714,0.0010913471],"category_scores_gemma":[0.0046222354,0.00069363054,0.0008255568,0.00062080857,0.00080469914,0.001346263,0.0012902896,0.0020309049,0.0002872542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082567596,0.000058585727,0.00079101144,0.000041165284,0.000034284752,0.000043688276,0.00007573499,0.9154214,0.001723062,0.00412605,0.0006333782,0.076969],"study_design_scores_gemma":[0.0000024100177,0.000008650918,0.000040771072,0.0000021141457,0.0000020459918,0.0000024781682,0.0000019071083,0.99882823,0.00024267113,0.0007811811,0.000085683394,0.0000018670247],"about_ca_topic_score_codex":0.012447841,"about_ca_topic_score_gemma":0.010909071,"teacher_disagreement_score":0.012447841,"about_ca_system_score_codex":0.0008261269,"about_ca_system_score_gemma":0.0015262448,"threshold_uncertainty_score":0.02475077},"labels":[],"label_agreement":null},{"id":"W4413029381","doi":"10.2139/ssrn.5381167","title":"Development of a Mechanistic and Dimensional Analysis Model for Multiphase Flow Leak Detection in Pipeline","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"","keywords":"Pipeline (software); Leak detection; Leak; Multiphase flow; Petroleum engineering; Computer science; Flow (mathematics); Risk analysis (engineering); Engineering; Mechanics; Business; Physics; Environmental engineering","score_opus":0.009576819210991376,"score_gpt":0.2237991981155409,"score_spread":0.2142223789045495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413029381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02987701,0.0002692762,0.9603108,0.000411465,0.00005682689,0.00013284896,0.0002568724,0.00058503007,0.008099886],"genre_scores_gemma":[0.8895992,0.00047689673,0.098049596,0.00013455507,0.000050281087,0.00046802347,0.0003013122,0.0001886801,0.010731474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998068,0.000037490532,0.0000121487565,0.000053996457,0.00006537742,0.000024081097],"domain_scores_gemma":[0.9996106,0.000163222,0.000053348256,0.000028567138,0.00011947328,0.00002474346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000520212,0.0008280235,0.0010905348,0.0005260324,0.0006651294,0.0013577318,0.0017933061,0.002315191,0.002606524],"category_scores_gemma":[0.0012069886,0.0007787358,0.0011443534,0.0004348777,0.00066590536,0.0012226682,0.0011481746,0.0012169842,0.0004951405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010599644,0.000026579128,0.00028693935,0.000035464753,0.000008532281,0.00004226902,0.000019989926,0.9904755,0.0025315972,0.002829466,0.00016421724,0.003568822],"study_design_scores_gemma":[0.0000017256375,0.00000555314,0.00003997365,0.0000015309457,0.0000018847231,0.0000045226734,0.0000019219876,0.9990018,0.00031248873,0.00048980105,0.00013624255,0.0000026434793],"about_ca_topic_score_codex":0.009409948,"about_ca_topic_score_gemma":0.0049521383,"teacher_disagreement_score":0.009409948,"about_ca_system_score_codex":0.0010827818,"about_ca_system_score_gemma":0.0019603195,"threshold_uncertainty_score":0.018710315},"labels":[],"label_agreement":null},{"id":"W4413134932","doi":"10.1061/9780784486382.024","title":"The Relevance of Acoustic Pipe Condition Assessment to the Structural Integrity of Water Mains","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Fleming College","funders":"","keywords":"Relevance (law); Structural integrity; Mains electricity; Computer science; Acoustics; Engineering; Structural engineering; Electrical engineering; Physics","score_opus":0.005793743941776158,"score_gpt":0.24231624735835597,"score_spread":0.23652250341657982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413134932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39159596,0.004108709,0.5547226,0.00254142,0.00027252,0.00013676353,0.00033954246,0.0007474562,0.04553496],"genre_scores_gemma":[0.98577917,0.00089986494,0.01052827,0.00004966271,0.0001052141,0.000013811554,0.00003897803,0.000037239948,0.002547826],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991767,0.00018342544,0.00002928603,0.00013753923,0.00042212178,0.00005085488],"domain_scores_gemma":[0.9961003,0.00237119,0.00039673565,0.00028617555,0.00075854675,0.00008702453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008392048,0.00045024283,0.00033039707,0.00064633944,0.00037576226,0.0010305233,0.0003793779,0.0008209989,0.0022034287],"category_scores_gemma":[0.005840077,0.00023526725,0.00016026455,0.00037950068,0.001463379,0.0014005951,0.0006857171,0.00075599324,0.00041922583],"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":[0.0004657384,0.00011943142,0.04999685,0.00055760116,0.00004686972,0.0009619624,0.0005618526,0.20165199,0.2890309,0.022422459,0.0023895272,0.43179488],"study_design_scores_gemma":[0.000023744096,0.0016142738,0.14940241,0.00023285011,0.00011426453,0.0020715422,0.00091801654,0.60859376,0.17687817,0.03559351,0.024348851,0.00020869126],"about_ca_topic_score_codex":0.002456599,"about_ca_topic_score_gemma":0.0018980806,"teacher_disagreement_score":0.002456599,"about_ca_system_score_codex":0.00040447604,"about_ca_system_score_gemma":0.00039689892,"threshold_uncertainty_score":0.007371247},"labels":[],"label_agreement":null},{"id":"W4413134936","doi":"10.1061/9780784486382.057","title":"Enhancing Pipeline Integrity Management: Predictive Analytics and Probabilistic Applied Load Modeling for Water Transmission Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Hatch (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Integrity management; Probabilistic logic; Analytics; Pipeline transport; Predictive analytics; Transmission (telecommunications); Reliability engineering; Data science; Environmental science; Engineering; Telecommunications; Artificial intelligence","score_opus":0.007918373420038241,"score_gpt":0.20026203796338188,"score_spread":0.19234366454334365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413134936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23434468,0.0001612361,0.7582129,0.000536625,0.000019703892,0.00013408961,0.00038613845,0.0014397815,0.0047647464],"genre_scores_gemma":[0.9245798,0.00017685359,0.07364137,0.000032834745,0.00002099234,0.00008231164,0.00034494005,0.00006642638,0.001054538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953866,0.0001595212,0.000026331956,0.00007414492,0.00015326585,0.000048164544],"domain_scores_gemma":[0.9988531,0.0006867661,0.00014514539,0.00008613886,0.0001965314,0.000032257067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001321386,0.00075388496,0.00040044633,0.0010247617,0.00032510835,0.00130619,0.0008042304,0.0006591069,0.0007404153],"category_scores_gemma":[0.004002941,0.00037499468,0.0006013577,0.0008074985,0.00039573113,0.0012663829,0.0008216191,0.0007983091,0.00014466375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015352789,0.00003649371,0.0013492377,0.000010362609,0.0000068989975,0.000017392029,0.000024303401,0.98339754,0.0003173967,0.0015052344,0.00014717573,0.013172671],"study_design_scores_gemma":[4.1729396e-7,0.000004281913,0.000116232026,0.0000011944873,7.8964945e-7,0.0000014303901,0.000004592434,0.9992613,0.00011173906,0.00044525324,0.00005153143,0.0000011936025],"about_ca_topic_score_codex":0.019898323,"about_ca_topic_score_gemma":0.0121300565,"teacher_disagreement_score":0.019898323,"about_ca_system_score_codex":0.0011749314,"about_ca_system_score_gemma":0.0010908939,"threshold_uncertainty_score":0.039565027},"labels":[],"label_agreement":null},{"id":"W4413135713","doi":"10.1061/9780784486382.003","title":"Data-Powered Strategies: Enhancing Pipeline Management for Small Water Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Alberta Crop Industry Development Fund","funders":"","keywords":"Pipeline (software); Computer science; Systems engineering; Engineering; Operating system","score_opus":0.019603414120098087,"score_gpt":0.2312650488489956,"score_spread":0.2116616347288975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413135713","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":[0.17294942,0.0015033091,0.73758435,0.019438442,0.00012642686,0.0023163324,0.00095575786,0.0035718626,0.061554138],"genre_scores_gemma":[0.7770292,0.0010534967,0.21570928,0.00037680817,0.000028179029,0.00040956808,0.0006396161,0.00014786949,0.004605985],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99696785,0.0014386624,0.00014018729,0.00022062277,0.0010072042,0.00022543827],"domain_scores_gemma":[0.9911876,0.0035821653,0.00091318425,0.0009991023,0.0026799054,0.00063800224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066271387,0.0011201814,0.00038793572,0.002288671,0.0011651415,0.004717395,0.0027688385,0.0010403458,0.0040003676],"category_scores_gemma":[0.02064836,0.00034352066,0.00038514478,0.0018866423,0.0014079947,0.0060949847,0.003466491,0.00095195277,0.000677294],"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":[0.00025898346,0.0009172021,0.01693718,0.0012446343,0.000092300164,0.00047936625,0.0030227024,0.21580496,0.0071924175,0.075996205,0.0137438495,0.6643101],"study_design_scores_gemma":[0.00020753258,0.0012456207,0.011177237,0.0012125934,0.00013098343,0.00033126964,0.013538027,0.64448744,0.021844955,0.16923234,0.13637869,0.00021338693],"about_ca_topic_score_codex":0.024811458,"about_ca_topic_score_gemma":0.035721567,"teacher_disagreement_score":0.024811458,"about_ca_system_score_codex":0.003366605,"about_ca_system_score_gemma":0.01003575,"threshold_uncertainty_score":0.04933405},"labels":[],"label_agreement":null},{"id":"W4413135785","doi":"10.1061/9780784486382.058","title":"Striking a Balance in the North: How Edmonton Gets the Most from Its Water Transmission Main Condition Assessment Program","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Balance (ability); Transmission (telecommunications); Water balance; Computer science; Environmental science; Reliability engineering; Engineering; Telecommunications; Physical medicine and rehabilitation; Geotechnical engineering; Medicine","score_opus":0.00709902095415311,"score_gpt":0.2259737496642805,"score_spread":0.2188747287101274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413135785","genre_codex":"commentary","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":[0.040788505,0.012294381,0.0066855536,0.6424728,0.006928595,0.00025017804,0.0005587577,0.0017155928,0.28830573],"genre_scores_gemma":[0.30327374,0.019526228,0.040210593,0.24462083,0.0017272027,0.00037871653,0.0025371846,0.0016676835,0.3860578],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99500567,0.001015994,0.00010033546,0.00047774793,0.001874221,0.0015259923],"domain_scores_gemma":[0.992303,0.00075560895,0.000168631,0.00022625095,0.0023692984,0.004177144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006429817,0.00084187184,0.00037782983,0.0013008001,0.012402624,0.0106739355,0.0034583977,0.0038273167,0.017586593],"category_scores_gemma":[0.0086461725,0.0006237348,0.0005455792,0.0009935962,0.0036299217,0.0058482504,0.008846565,0.006768652,0.0039365906],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000631836,0.00023379807,0.0068619833,0.00026722474,0.00003191155,0.0009258537,0.006717821,0.00077434594,0.0012130663,0.016803915,0.6960639,0.27004302],"study_design_scores_gemma":[0.000025568048,0.000111394445,0.011325786,0.00070841674,0.000032661326,0.00023405495,0.013685774,0.00049957767,0.00046701118,0.0078227855,0.96501476,0.00007211927],"about_ca_topic_score_codex":0.4950416,"about_ca_topic_score_gemma":0.7877713,"teacher_disagreement_score":0.5049584,"about_ca_system_score_codex":0.021028131,"about_ca_system_score_gemma":0.07432161,"threshold_uncertainty_score":0.9843203},"labels":[],"label_agreement":null},{"id":"W4413135898","doi":"10.1061/9780784486368.007","title":"Mountain Home Air Force Base Water Resilience Project: Challenges Associated with Design of Pump Station and Pipeline with Tight Schedule","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Stantec (Canada)","funders":"","keywords":"Pipeline (software); Resilience (materials science); Schedule; Base (topology); Computer science; Base station; Environmental science; Marine engineering; Pipeline transport; Engineering; Telecommunications; Environmental engineering; Operating system; Materials science","score_opus":0.011167625175521088,"score_gpt":0.19791536450347683,"score_spread":0.18674773932795574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413135898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3476566,0.0032774312,0.42807123,0.024776667,0.0009536399,0.0032718442,0.0006717327,0.0028695972,0.18845122],"genre_scores_gemma":[0.54262197,0.00264123,0.39124575,0.001147924,0.00011972175,0.0009867686,0.00043830543,0.0006151612,0.06018324],"study_design_codex":"design_other","study_design_gemma":"case_report","domain_scores_codex":[0.9963231,0.00087037473,0.00013311386,0.00026738536,0.0019126644,0.0004933297],"domain_scores_gemma":[0.99696845,0.00045104843,0.00024824703,0.000239829,0.0015222908,0.0005700753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052507883,0.0006132147,0.00029024386,0.00086955505,0.0025618698,0.0033723067,0.0015408259,0.0011308107,0.0053753997],"category_scores_gemma":[0.005003827,0.00056448183,0.00035084557,0.00060618867,0.0008475476,0.0022513631,0.0016263353,0.0016167037,0.0015409609],"study_design_candidate":"case_report","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":[0.00022816188,0.0009436281,0.019592663,0.0011805464,0.00006420717,0.0027206738,0.007900035,0.071732424,0.041227296,0.035836723,0.061382093,0.7571915],"study_design_scores_gemma":[0.00016856332,0.0040196567,0.033301953,0.001228399,0.00017316257,0.0037660717,0.031456888,0.1065449,0.032388154,0.016769808,0.76990956,0.0002728505],"about_ca_topic_score_codex":0.02127545,"about_ca_topic_score_gemma":0.057130754,"teacher_disagreement_score":0.02127545,"about_ca_system_score_codex":0.0030764109,"about_ca_system_score_gemma":0.009108583,"threshold_uncertainty_score":0.042303264},"labels":[],"label_agreement":null},{"id":"W4413135929","doi":"10.1061/9780784486368.040","title":"Calculating Head Loss due to Air-Binding in Water and Wastewater Pipelines Using an Innovative Transient Model","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"WSP (Canada)","funders":"","keywords":"Transient (computer programming); Pipeline transport; Wastewater; Head (geology); Transient analysis; Hydraulic head; Petroleum engineering; Environmental science; Marine engineering; Computer science; Engineering; Environmental engineering; Transient response; Geotechnical engineering; Geology; Electrical engineering","score_opus":0.022179306933684327,"score_gpt":0.25556318564075853,"score_spread":0.2333838787070742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413135929","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":[0.34140292,0.00022065968,0.6432585,0.00031847274,0.00005761008,0.00012639895,0.00039750178,0.00080968224,0.013408171],"genre_scores_gemma":[0.9827732,0.000116915384,0.0129177235,0.000034761524,0.0000062876443,0.0001025425,0.00009665368,0.000048234728,0.0039036109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990666,0.000016193157,0.0000061497935,0.000016200847,0.000030333804,0.000024485773],"domain_scores_gemma":[0.99978954,0.00010901156,0.00002884032,0.000019210145,0.000039775736,0.000013621773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024369294,0.00036786613,0.00040975434,0.0003036819,0.00035339076,0.00062151067,0.0007244453,0.00114934,0.0013052443],"category_scores_gemma":[0.00071536266,0.00027872287,0.00063296675,0.00034022285,0.0005006703,0.00060776883,0.0005365019,0.00049859955,0.00017839544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020152478,0.0000136651925,0.0004281789,0.000021226737,0.000003319961,0.000048047237,0.00001677737,0.99212635,0.00412562,0.0009769594,0.000096835174,0.0021228814],"study_design_scores_gemma":[0.000002360015,0.0000071802297,0.00006632332,9.4769945e-7,0.0000014013198,0.0000050543176,0.0000043890022,0.9991059,0.00062536955,0.00011101794,0.00006829481,0.0000018452563],"about_ca_topic_score_codex":0.015764335,"about_ca_topic_score_gemma":0.0069218916,"teacher_disagreement_score":0.015764335,"about_ca_system_score_codex":0.00093593315,"about_ca_system_score_gemma":0.0013949256,"threshold_uncertainty_score":0.03134513},"labels":[],"label_agreement":null},{"id":"W4413136071","doi":"10.1061/9780784486382.033","title":"Predicting Degradation Patterns in Water Distribution Networks and Their Influence on Pipe Failure","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Fleming College","funders":"","keywords":"Degradation (telecommunications); Distribution (mathematics); Environmental science; Computer science; Telecommunications; Mathematics","score_opus":0.002776975240334103,"score_gpt":0.16384850845165494,"score_spread":0.16107153321132084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413136071","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":[0.97967225,0.00009078528,0.019574976,0.00006717525,0.0000053128874,0.000011572605,0.0002412404,0.00009554432,0.00024119737],"genre_scores_gemma":[0.9980659,0.000029925728,0.001542129,0.0000037171403,0.0000032699825,0.000004596951,0.00020068731,0.0000040185887,0.00014584363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973005,0.0000805147,0.000019404466,0.00008089113,0.000045602435,0.000043407133],"domain_scores_gemma":[0.9979808,0.0013798313,0.0003217569,0.000098022,0.00015384993,0.00006576789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076848664,0.0006160394,0.00035358281,0.0006054259,0.00014333762,0.00042758396,0.00024351108,0.0005263893,0.0003598107],"category_scores_gemma":[0.003692253,0.00029726923,0.0003109296,0.00035199168,0.00027831367,0.0006813276,0.0002203646,0.00049691193,0.0001100223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006189449,0.00004603066,0.04169138,0.000011291221,0.000022992503,0.000033197288,0.0000147741175,0.94947493,0.000906368,0.00007960259,0.00008828977,0.0075691915],"study_design_scores_gemma":[0.0000010290823,0.000020390187,0.008329632,0.0000010494173,0.0000022736065,0.000008705939,0.0000068692143,0.99122614,0.00028636373,0.000089069086,0.000026115136,0.0000023510997],"about_ca_topic_score_codex":0.01468391,"about_ca_topic_score_gemma":0.011015503,"teacher_disagreement_score":0.01468391,"about_ca_system_score_codex":0.0005849102,"about_ca_system_score_gemma":0.00027659768,"threshold_uncertainty_score":0.029196858},"labels":[],"label_agreement":null},{"id":"W4413353457","doi":"10.1016/j.jwpe.2025.108505","title":"Accurate and efficient junction mixing prediction in water distribution networks via surrogate modelling","year":2025,"lang":"en","type":"article","venue":"Journal of Water Process Engineering","topic":"Water Systems and 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 Laval","funders":"Fonds Wetenschappelijk Onderzoek; Vlaamse regering; Vlaams Supercomputer Centrum; Universiteit Gent; Vermont Studio Center","keywords":"Mixing (physics); Surrogate model; Distribution (mathematics); Computer science; Artificial intelligence; Statistical physics; Biological system; Mathematics; Machine learning; Physics; Mathematical analysis; Biology","score_opus":0.004048707053832705,"score_gpt":0.17298949221421664,"score_spread":0.16894078516038394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413353457","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":[0.21446721,0.00037068498,0.77874815,0.0003073982,0.00006906408,0.000046305344,0.00024143969,0.00048818186,0.005261572],"genre_scores_gemma":[0.9692797,0.00010517908,0.029017346,0.000024579362,0.000015329502,0.00003640741,0.0001755912,0.000046488134,0.001299339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996947,0.00013574726,0.000014733419,0.000042139658,0.00007683762,0.00003580199],"domain_scores_gemma":[0.9991598,0.00052746775,0.00008181833,0.000065898545,0.00012449206,0.000040570234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079042546,0.0005877404,0.0010518474,0.0004039763,0.00040147893,0.0011535271,0.0007139889,0.0013083696,0.00079145306],"category_scores_gemma":[0.0027624262,0.0005867355,0.00058380264,0.00049745024,0.00048277702,0.0011723991,0.00072489725,0.0011195361,0.00022359453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002005182,0.000010101225,0.00017803234,0.0000069250855,0.0000046873743,0.0000090183285,0.0000046714877,0.99684983,0.00039422335,0.00067150744,0.000057600813,0.0017933465],"study_design_scores_gemma":[7.554567e-7,0.0000020038574,0.000014542791,3.8008784e-7,3.3327083e-7,6.266266e-7,5.8104905e-7,0.999718,0.00007479098,0.00017259512,0.00001479332,5.429408e-7],"about_ca_topic_score_codex":0.006501629,"about_ca_topic_score_gemma":0.004763139,"teacher_disagreement_score":0.006501629,"about_ca_system_score_codex":0.00058080786,"about_ca_system_score_gemma":0.00086369796,"threshold_uncertainty_score":0.012927532},"labels":[],"label_agreement":null},{"id":"W4413783551","doi":"10.3390/s25175320","title":"Design of Monitoring Systems for Contaminant Detection in Water Networks Under Pipe Break-Induced Events","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Water Systems and 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; Polytechnique Montréal","keywords":"Environmental science; Computer science; Engineering; Forensic engineering","score_opus":0.01532489893703513,"score_gpt":0.21706043652172552,"score_spread":0.2017355375846904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413783551","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":[0.2351318,0.0002554863,0.7601536,0.00026289912,0.000033398574,0.00025432865,0.00012336508,0.0008080398,0.0029772043],"genre_scores_gemma":[0.9731276,0.000089641515,0.026045397,0.000022258491,0.0000065797167,0.00011057781,0.000038840382,0.00001388005,0.0005451948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999569,0.00011171997,0.000020802656,0.00013229474,0.000096266675,0.00006989157],"domain_scores_gemma":[0.9994079,0.00016817488,0.00017749397,0.000039442595,0.00016532933,0.000041598858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004979638,0.000623026,0.00045670563,0.00036438365,0.00042550964,0.0006552887,0.00096930814,0.00050763995,0.00072417606],"category_scores_gemma":[0.0014890239,0.00031512583,0.00025953556,0.00020308491,0.00044761828,0.000783351,0.00070473936,0.00032445966,0.00017043913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028823628,0.000108511646,0.0045923134,0.00014217019,0.000042807293,0.00010374457,0.0001106928,0.89535666,0.053698074,0.0029256698,0.00043566272,0.0421955],"study_design_scores_gemma":[0.000013671361,0.0002751476,0.0017875162,0.000010028859,0.000022958562,0.00002481208,0.00004921345,0.98353124,0.012569529,0.0010558296,0.0006497759,0.000010250338],"about_ca_topic_score_codex":0.0021548786,"about_ca_topic_score_gemma":0.002231461,"teacher_disagreement_score":0.0021548786,"about_ca_system_score_codex":0.0008589587,"about_ca_system_score_gemma":0.00074724,"threshold_uncertainty_score":0.0062322617},"labels":[],"label_agreement":null},{"id":"W4413839786","doi":"10.1061/jwrmd5.wreng-6866","title":"Comparative Review of Water Main Failure Prediction Models: Physical and Data-Driven Approaches","year":2025,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Computer science; Environmental science; Econometrics; Mathematics","score_opus":0.046527529667127906,"score_gpt":0.24902590019805648,"score_spread":0.20249837053092856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413839786","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":[0.0063721463,0.94014275,0.04445116,0.0023788307,0.00082613213,0.00007646848,0.0008921926,0.0002856954,0.004574611],"genre_scores_gemma":[0.040250562,0.9385728,0.016982451,0.00057419523,0.0009594206,0.00008731855,0.001427074,0.00008130559,0.0010648285],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984804,0.00040139598,0.00024296124,0.00035997052,0.00044190168,0.000073355164],"domain_scores_gemma":[0.9881323,0.009040786,0.0005082441,0.00031296187,0.0018622703,0.00014342097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040096324,0.0019808826,0.0017544384,0.004592876,0.00036807364,0.0025364102,0.0033158502,0.0015702007,0.002211257],"category_scores_gemma":[0.010885496,0.0009045816,0.002537885,0.0057247425,0.0005197753,0.0032104175,0.00092985306,0.0016596359,0.00092246756],"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":[0.00017450104,0.00020381132,0.008766367,0.03019699,0.0007658571,0.000262137,0.000407371,0.065250404,0.001004406,0.019292213,0.02410515,0.8495708],"study_design_scores_gemma":[0.000055959557,0.0009487371,0.020078756,0.03969222,0.003041945,0.0010434327,0.0014897728,0.24668734,0.0038105065,0.037137166,0.64548963,0.0005244098],"about_ca_topic_score_codex":0.008422984,"about_ca_topic_score_gemma":0.005628381,"teacher_disagreement_score":0.008422984,"about_ca_system_score_codex":0.0012124099,"about_ca_system_score_gemma":0.0025981215,"threshold_uncertainty_score":0.021205246},"labels":[],"label_agreement":null},{"id":"W4413920071","doi":"10.1016/j.jpse.2025.100353","title":"A Comprehensive Survey on Pipeline Monitoring Technologies: Advancements, Challenges, Market Opportunities and Future Directions","year":2025,"lang":"en","type":"article","venue":"Journal of Pipeline Science and Engineering","topic":"Water Systems and 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":"Thales (Canada)","funders":"","keywords":"Pipeline (software); Data science; Computer science; Systems engineering; Engineering; Business","score_opus":0.030324992873702095,"score_gpt":0.2453264087996901,"score_spread":0.21500141592598798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413920071","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":[0.0077266423,0.94622785,0.015953043,0.006727392,0.0010224864,0.000061017647,0.0006528796,0.00028219036,0.021346537],"genre_scores_gemma":[0.023651134,0.9623228,0.008232658,0.0011026578,0.00088251743,0.000033022912,0.00075259863,0.000052716747,0.002970014],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99848324,0.00027189587,0.00022220169,0.00025418226,0.00064324925,0.00012524164],"domain_scores_gemma":[0.9926559,0.0044689323,0.0005983483,0.0002461199,0.0018123694,0.00021841124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024502398,0.0009579577,0.00081243744,0.0075681037,0.00060107466,0.0026762076,0.00092489435,0.0014550914,0.005424769],"category_scores_gemma":[0.0058232187,0.0005266603,0.00091184105,0.010165594,0.0006343177,0.006216465,0.0010153367,0.0012347873,0.0016886573],"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":[0.00006460253,0.00008007943,0.0047781467,0.011186518,0.00005884069,0.00021013703,0.0003128018,0.0022460902,0.0022030016,0.020325651,0.037335817,0.9211983],"study_design_scores_gemma":[0.000005575136,0.00019681391,0.007328774,0.007214694,0.00014639573,0.0011570276,0.0010594344,0.0035066355,0.0019896098,0.011582166,0.9657271,0.00008573084],"about_ca_topic_score_codex":0.002085199,"about_ca_topic_score_gemma":0.0028638833,"teacher_disagreement_score":0.0075681037,"about_ca_system_score_codex":0.0008609585,"about_ca_system_score_gemma":0.0018559307,"threshold_uncertainty_score":0.018147647},"labels":[],"label_agreement":null},{"id":"W4414008470","doi":"10.1016/j.asoc.2025.113846","title":"An efficient mathematical-based optimization method to optimize multi-hydropower operating rules","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Water Systems and 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 Prince Edward Island","funders":"King Fahd University of Petroleum and Minerals","keywords":"Computer science; Hydropower; Mathematical optimization; Mathematics; Engineering","score_opus":0.008191508408626398,"score_gpt":0.26200079164773893,"score_spread":0.2538092832391125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414008470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039225677,0.0001584899,0.9911957,0.000111830734,0.000086438435,0.00005654824,0.00005036844,0.0002636554,0.0041543883],"genre_scores_gemma":[0.21025304,0.00030196857,0.78170496,0.00021914944,0.00012173159,0.00051612966,0.0002177852,0.00034871497,0.0063165533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996462,0.000094342686,0.000020407317,0.000042660715,0.00016001223,0.00003635837],"domain_scores_gemma":[0.99918586,0.000489699,0.00004642481,0.000045133827,0.00019981398,0.00003299058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011103055,0.00080218527,0.0013459392,0.00083670043,0.0005578607,0.0011988425,0.0012356986,0.001197603,0.004639958],"category_scores_gemma":[0.0023458726,0.0006854675,0.0010621384,0.0009796134,0.0005573409,0.00079296174,0.0008825332,0.0013906858,0.00086142216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024932328,0.00005173617,0.00012580711,0.00007366296,0.000032114036,0.000031198746,0.000018508616,0.9497299,0.0010256402,0.009559585,0.001122499,0.038204394],"study_design_scores_gemma":[0.0000038098342,0.000005045242,0.00001394566,0.0000031158663,0.0000023389946,0.000003855692,0.0000014120875,0.998811,0.00011917319,0.0007173532,0.0003173403,0.0000014994954],"about_ca_topic_score_codex":0.005227951,"about_ca_topic_score_gemma":0.006423003,"teacher_disagreement_score":0.005227951,"about_ca_system_score_codex":0.00069976045,"about_ca_system_score_gemma":0.0018402849,"threshold_uncertainty_score":0.015522182},"labels":[],"label_agreement":null},{"id":"W4414305697","doi":"10.1016/j.desal.2025.119389","title":"Numerical simulation of desalination brine discharges: Effects of inlet boundary conditions","year":2025,"lang":"en","type":"article","venue":"Desalination","topic":"Water Systems and 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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Inlet; Nozzle; Turbulence; Large eddy simulation; Desalination; Boundary value problem; Computer simulation; Particle image velocimetry","score_opus":0.00446923404761411,"score_gpt":0.23739526581615622,"score_spread":0.2329260317685421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414305697","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":[0.8009822,0.00035469932,0.19058768,0.00016058653,0.00007092187,0.00011546071,0.00027565565,0.0005538706,0.006898907],"genre_scores_gemma":[0.9604178,0.00016539451,0.03773388,0.000032389205,0.000007274395,0.000089999616,0.0001642234,0.00005066473,0.0013382775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980587,0.00004926595,0.00001891905,0.00003234115,0.00006833575,0.000025331172],"domain_scores_gemma":[0.99953806,0.0002448659,0.000066105626,0.000048494152,0.000076553224,0.000026013999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042529835,0.00041008668,0.00055992114,0.00018011249,0.0003628001,0.00074443215,0.0006283461,0.000758981,0.0008386149],"category_scores_gemma":[0.0014876237,0.00021899663,0.00036792437,0.00019030135,0.00042907777,0.00053474435,0.00055208965,0.0005824757,0.00013941801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009821681,0.00008454957,0.0043750126,0.00010857612,0.00001738955,0.00014842633,0.00011854127,0.9460728,0.037843682,0.0018097954,0.0001433779,0.009179617],"study_design_scores_gemma":[0.000014845831,0.000025762547,0.00047598174,0.000004750392,0.0000026525981,0.000010230312,0.000015509233,0.9920351,0.0070137717,0.00011223457,0.000283779,0.000005408421],"about_ca_topic_score_codex":0.0044378587,"about_ca_topic_score_gemma":0.0031070255,"teacher_disagreement_score":0.0044378587,"about_ca_system_score_codex":0.00048324483,"about_ca_system_score_gemma":0.00075952976,"threshold_uncertainty_score":0.00882405},"labels":[],"label_agreement":null},{"id":"W4414471992","doi":"10.2166/hydro.2025.061","title":"Enhancing accuracy and efficiency in calibration of drinking water distribution networks through evolutionary artificial neural networks and expert systems","year":2025,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and 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":"Calibration; Adaptability; Benchmark (surveying); Generalizability theory; Artificial neural network; Expert system","score_opus":0.0064928622251123665,"score_gpt":0.20860364013107568,"score_spread":0.20211077790596332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414471992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24926361,0.00027315877,0.7443402,0.00030644835,0.00005257067,0.00007813229,0.000109554465,0.0008869237,0.004689362],"genre_scores_gemma":[0.9423216,0.000053460655,0.056862243,0.000037494396,0.0000081536455,0.000037495465,0.000086240194,0.000040929743,0.0005523687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908173,0.00040311663,0.000053926226,0.00018772858,0.00019721275,0.000076190736],"domain_scores_gemma":[0.9960509,0.0022609965,0.00042683946,0.00038184528,0.0008129789,0.00006645907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002792489,0.00073431566,0.0005742307,0.0009902564,0.00037512305,0.0010692267,0.001000189,0.0009949224,0.0009040436],"category_scores_gemma":[0.012146152,0.0003923209,0.00045761486,0.0006869176,0.00057317474,0.0011649732,0.0011166566,0.0009844275,0.00018318693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022136792,0.000017476608,0.0008247252,0.000014830351,0.000013333087,0.000017155618,0.000026290196,0.98108655,0.00058277004,0.0006163111,0.000113153255,0.016665254],"study_design_scores_gemma":[0.0000017919222,0.0000068454856,0.00016572714,0.000004235111,0.0000020203381,0.000003407346,0.0000059391004,0.9985876,0.00059345976,0.0005439352,0.00008283192,0.0000023443868],"about_ca_topic_score_codex":0.011299436,"about_ca_topic_score_gemma":0.0065262388,"teacher_disagreement_score":0.011299436,"about_ca_system_score_codex":0.0010133755,"about_ca_system_score_gemma":0.0008927041,"threshold_uncertainty_score":0.022467315},"labels":[],"label_agreement":null},{"id":"W4414774308","doi":"","title":"Comment co-construire des indicateurs pour la durabilité des nappes captives ?","year":2025,"lang":"fr","type":"article","venue":"Agritrop (Cirad)","topic":"Water Systems and 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":"Nova scotia; Poison control; Informal settlements","score_opus":0.010715502700240765,"score_gpt":0.22535320331601968,"score_spread":0.21463770061577891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414774308","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22777283,0.0037880125,0.7147937,0.007144314,0.00042175595,0.00045192175,0.00090304186,0.00313308,0.041591328],"genre_scores_gemma":[0.72660065,0.0009825662,0.2599007,0.00029264746,0.00010544154,0.00032462427,0.0005794343,0.0006341706,0.010579848],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99480754,0.0019400236,0.00030456815,0.0012348864,0.001334692,0.0003782583],"domain_scores_gemma":[0.9853616,0.0070095337,0.0018413214,0.001190612,0.003956346,0.00064053264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00975621,0.0017591362,0.0011470922,0.0020222736,0.0011066203,0.007485004,0.0019463486,0.0021494739,0.0063048988],"category_scores_gemma":[0.018055517,0.00089636305,0.0009682978,0.0018772635,0.001307906,0.0060346974,0.0023109743,0.002378936,0.0013920313],"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":[0.00096412364,0.000380129,0.056923702,0.0016757073,0.00061429886,0.00067524303,0.00551448,0.2655167,0.048349984,0.061544914,0.0103815375,0.5474592],"study_design_scores_gemma":[0.00010281682,0.0011356872,0.045221213,0.0007750101,0.00056734856,0.000586417,0.008768946,0.77863324,0.042849805,0.03305844,0.08790834,0.0003927831],"about_ca_topic_score_codex":0.022344423,"about_ca_topic_score_gemma":0.025273342,"teacher_disagreement_score":0.022344423,"about_ca_system_score_codex":0.00260374,"about_ca_system_score_gemma":0.00369253,"threshold_uncertainty_score":0.051596403},"labels":[],"label_agreement":null},{"id":"W4414883789","doi":"10.20998/2522-9052.2025.4.08","title":"A MULTI-LAYER DELTA LAKEHOUSE FOR EPIDEMIOLOGICAL MONITORING AND FORECASTING UNDER EMERGENCIES","year":2025,"lang":"en","type":"article","venue":"Advanced Information Systems","topic":"Water Systems and 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":"Balsillie School of International Affairs","funders":"National Research Foundation; National Research Foundation of Ukraine","keywords":"Analytics; Interoperability; Software deployment; Workflow; Data quality; Data analysis; Architecture; Testbed; Schema (genetic algorithms)","score_opus":0.060123666061945226,"score_gpt":0.27546835694270505,"score_spread":0.21534469088075983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414883789","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":[0.10331842,0.00042382264,0.8085906,0.0025619322,0.00016425921,0.0008525236,0.0029483892,0.068610154,0.012529846],"genre_scores_gemma":[0.51517403,0.0004704203,0.46367893,0.0009704816,0.00008488332,0.00031903535,0.006379036,0.0014914526,0.0114316065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987778,0.00017551926,0.00016074773,0.00033938832,0.00040407619,0.00014243118],"domain_scores_gemma":[0.99678755,0.0006729749,0.00022352446,0.0010799051,0.0008162252,0.00041983047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030460532,0.0006378927,0.00045963813,0.0013623306,0.0008608854,0.0036334086,0.002969365,0.0007250603,0.004588032],"category_scores_gemma":[0.004762553,0.00082921935,0.0009889214,0.0012687015,0.0009010247,0.00558684,0.0044435444,0.0012263936,0.0016238891],"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":[0.0023049077,0.0007865748,0.10518291,0.0014806348,0.0007414988,0.0013537138,0.004814506,0.14824624,0.061543345,0.08708964,0.08085171,0.5056043],"study_design_scores_gemma":[0.00017576311,0.0006357131,0.018338231,0.00037203802,0.0003480143,0.0005323418,0.001499106,0.74592555,0.046233326,0.047048103,0.13861175,0.00028010734],"about_ca_topic_score_codex":0.01166715,"about_ca_topic_score_gemma":0.012988267,"teacher_disagreement_score":0.01166715,"about_ca_system_score_codex":0.0021063357,"about_ca_system_score_gemma":0.0039678086,"threshold_uncertainty_score":0.023198485},"labels":[],"label_agreement":null},{"id":"W4414918112","doi":"10.1016/j.watres.2025.124737","title":"Evaluation of response measures for water cutoffs using pressure driven analysis simulations","year":2025,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and 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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Water supply; Resilience (materials science); Cutoff; Benchmark (surveying); Measure (data warehouse); Psychological resilience; Water supply network","score_opus":0.1325249989398433,"score_gpt":0.39742348344046446,"score_spread":0.26489848450062115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414918112","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":[0.95077866,0.00010307874,0.039900266,0.00032750703,0.000044318353,0.00021235069,0.0004926718,0.0003636863,0.0077775465],"genre_scores_gemma":[0.99304444,0.000028481734,0.0064157257,0.00002176896,0.0000024437168,0.0000745315,0.00010762095,0.00001728325,0.0002876046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911517,0.00039801694,0.000036897658,0.00008576582,0.00017744419,0.00018669914],"domain_scores_gemma":[0.99156696,0.0063864985,0.0005333402,0.00041717666,0.000791266,0.00030474935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002453909,0.00075351796,0.0005326317,0.0007723181,0.0004840763,0.0007538103,0.0009120265,0.00094328803,0.0015649362],"category_scores_gemma":[0.00885599,0.0002697172,0.0004553415,0.00054154015,0.0006582619,0.0009469895,0.0007573002,0.0011290299,0.00011774134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014020244,0.00012855115,0.0016609106,0.000033328604,0.000015111986,0.00004103858,0.000032729888,0.9915587,0.001642366,0.0012238269,0.00027418594,0.003249123],"study_design_scores_gemma":[0.000017396866,0.00018891803,0.0006328173,0.0000059085746,0.0000068897766,0.0000062630984,0.000056067547,0.99659216,0.0018129447,0.00051087805,0.00016245856,0.0000072547214],"about_ca_topic_score_codex":0.010672344,"about_ca_topic_score_gemma":0.0061044698,"teacher_disagreement_score":0.010672344,"about_ca_system_score_codex":0.0013260955,"about_ca_system_score_gemma":0.0010667685,"threshold_uncertainty_score":0.021220446},"labels":[],"label_agreement":null},{"id":"W4414946493","doi":"10.1016/j.measurement.2025.119214","title":"Advancements in FIV-based flow measurement: full-scale experimental and LES-SSI modeling approaches for buried pipelines","year":2025,"lang":"en","type":"article","venue":"Measurement","topic":"Water Systems and 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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Osteoporosis Canada","keywords":"Pipeline transport; Flow (mathematics); Pipeline (software); Flow conditions; Transient flow","score_opus":0.079650279394183,"score_gpt":0.2300718530971092,"score_spread":0.1504215737029262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414946493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46192896,0.0008404058,0.52907026,0.00037431365,0.000082052924,0.00011865882,0.00050140364,0.00093303155,0.006150945],"genre_scores_gemma":[0.9538092,0.0004288935,0.044592064,0.000026716594,0.000016782364,0.00008639805,0.00020009757,0.000034432447,0.00080537103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996983,0.00007259314,0.000021008836,0.00006246913,0.00011933185,0.000026451899],"domain_scores_gemma":[0.99954945,0.00017250482,0.00008005871,0.00008150633,0.0000956678,0.000020812882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063452206,0.0007170813,0.00061240606,0.00031138674,0.00021312578,0.0008664494,0.0007393147,0.0009758636,0.00065272354],"category_scores_gemma":[0.0012576986,0.00030444548,0.00042226468,0.000419898,0.00070048077,0.001174872,0.0005563603,0.00082093285,0.00014700893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012961011,0.00027572762,0.0062275683,0.000304423,0.00003257464,0.000106119136,0.00018244934,0.82925326,0.105134904,0.004213902,0.00044030207,0.053699184],"study_design_scores_gemma":[0.0000065327595,0.000056632733,0.0009336594,0.0000075526073,0.0000031047523,0.000009062248,0.000028817125,0.98978114,0.008461686,0.00032235216,0.00037706667,0.0000124246835],"about_ca_topic_score_codex":0.0038732283,"about_ca_topic_score_gemma":0.0021169975,"teacher_disagreement_score":0.0038732283,"about_ca_system_score_codex":0.00047440978,"about_ca_system_score_gemma":0.0006026174,"threshold_uncertainty_score":0.007701397},"labels":[],"label_agreement":null},{"id":"W4415136219","doi":"10.2118/227888-ms","title":"Detecting Pipeline Leaks Using a Novel Data-Driven Statistical Methodology with Earth Mover's Distance","year":2025,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Water Systems and 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":"Penn West Exploration (Canada)","funders":"","keywords":"Sequential probability ratio test; Leak; False alarm; Filter (signal processing); Pipeline (software); Sensitivity (control systems); Anomaly detection; Constant false alarm rate; Statistical power; Statistical model","score_opus":0.06492534583992415,"score_gpt":0.2999928198158071,"score_spread":0.23506747397588296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415136219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045927055,0.000059089947,0.9524089,0.0000806031,0.000022092749,0.00003998268,0.000099246245,0.0008902113,0.0004728893],"genre_scores_gemma":[0.7961957,0.000055365646,0.20200552,0.000077452816,0.000040066585,0.00009216109,0.00034040358,0.00008059981,0.0011126619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998185,0.00037231794,0.00014312034,0.00050253834,0.0006801043,0.000116833806],"domain_scores_gemma":[0.99567235,0.0020245174,0.0008194461,0.00035163533,0.00096765906,0.00016434184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017861011,0.00086185563,0.0009602577,0.002197479,0.0003595126,0.0012504958,0.0014892863,0.00076830096,0.0009473419],"category_scores_gemma":[0.0068836506,0.00043967107,0.001064815,0.0010416895,0.00069903664,0.0014463527,0.0012778692,0.0008269697,0.00029649993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041474585,0.00029851805,0.025403768,0.00017078826,0.00026484334,0.00030419003,0.00011942823,0.7568443,0.018164515,0.008607484,0.000896498,0.18851086],"study_design_scores_gemma":[0.0000066445223,0.00007883108,0.0012414698,0.0000027696217,0.00000862661,0.000043395587,0.000009369519,0.9950664,0.0022190376,0.0010513773,0.00025971758,0.000012335395],"about_ca_topic_score_codex":0.0070583154,"about_ca_topic_score_gemma":0.0040150634,"teacher_disagreement_score":0.0070583154,"about_ca_system_score_codex":0.0009988863,"about_ca_system_score_gemma":0.001592321,"threshold_uncertainty_score":0.01403445},"labels":[],"label_agreement":null},{"id":"W4415169177","doi":"10.1049/icp.2025.1450","title":"Innovating for safety: the journey of partial discharge measurement tools at Hydro-Quebec","year":2025,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Water Systems and 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":"Hydro-Québec","funders":"","keywords":"Reliability (semiconductor); Software deployment; Field (mathematics); Key (lock); Exploratory analysis","score_opus":0.042762491374307585,"score_gpt":0.23701911657591862,"score_spread":0.19425662520161102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415169177","genre_codex":"commentary","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":[0.18777548,0.045362953,0.09161929,0.3961489,0.003109743,0.0006430341,0.0018362001,0.0014287276,0.27207577],"genre_scores_gemma":[0.66951114,0.034800038,0.1317889,0.018971821,0.00040254398,0.00018039359,0.00087585294,0.00057749264,0.14289176],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99482167,0.001028545,0.0000874907,0.00040080125,0.0027864045,0.0008750502],"domain_scores_gemma":[0.99277616,0.00085041067,0.00022453262,0.00022983819,0.0045170807,0.0014019378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007182558,0.00064018974,0.0003101075,0.0011900755,0.0065206676,0.006184265,0.0018883105,0.0022333793,0.0048813135],"category_scores_gemma":[0.0062221237,0.00033295897,0.00035431306,0.0015493046,0.0051901094,0.0021795386,0.0024780121,0.0028777118,0.0009274455],"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":[0.00024141946,0.0004774306,0.025872348,0.0006578584,0.00006780957,0.0011632968,0.014131778,0.012761051,0.0139225805,0.09871505,0.1847255,0.6472639],"study_design_scores_gemma":[0.00003267116,0.00021361907,0.01523417,0.00076901994,0.000031384778,0.0003645609,0.012789228,0.010614519,0.006408283,0.0085598575,0.9447731,0.0002095235],"about_ca_topic_score_codex":0.94691604,"about_ca_topic_score_gemma":0.95621955,"teacher_disagreement_score":0.05401392,"about_ca_system_score_codex":0.05401392,"about_ca_system_score_gemma":0.09820699,"threshold_uncertainty_score":0.3919003},"labels":[],"label_agreement":null},{"id":"W4415235545","doi":"10.21203/rs.3.rs-7774650/v1","title":"Mechanistic modelling of in-sewer viral fate and transport ofSARS-CoV-2 to enhance wastewater disease surveillancestrategies","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Water Systems and 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 Guelph; York University; Public Health Agency of Canada","funders":"","keywords":"Population; Wastewater; Sampling (signal processing); Sedimentation; Sewage treatment; Waterborne diseases; Process (computing)","score_opus":0.04895864544510352,"score_gpt":0.32920271906674065,"score_spread":0.2802440736216371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415235545","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":[0.9076287,0.00083287084,0.07768851,0.0006636551,0.00008649542,0.00010430046,0.0006697691,0.00009828659,0.012227458],"genre_scores_gemma":[0.9934209,0.00032678177,0.0030723377,0.000028103572,0.0000067165497,0.000044550125,0.00014202327,0.0000097123075,0.0029487761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984074,0.000040940417,0.000008261296,0.00003862309,0.000028985902,0.00004244049],"domain_scores_gemma":[0.9997559,0.00012713541,0.00003345953,0.000015177138,0.00005121703,0.00001706903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050164026,0.0006449663,0.00076534547,0.00030295493,0.0002910158,0.0012834215,0.00075105723,0.0011675523,0.0017448508],"category_scores_gemma":[0.00082478585,0.0003165084,0.00096465455,0.0002715546,0.00027951403,0.0008778923,0.0005448355,0.00067260244,0.00018740515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005381539,0.000118726224,0.00088781555,0.00005835859,0.000023104865,0.000041760097,0.000015102639,0.9847606,0.009386828,0.002515993,0.00014120307,0.001996843],"study_design_scores_gemma":[0.000006908961,0.000039399183,0.00022615156,0.0000019821177,0.000007784052,0.000005369544,0.0000082781435,0.9967192,0.0021343343,0.00068218174,0.00016451166,0.0000039083234],"about_ca_topic_score_codex":0.01072548,"about_ca_topic_score_gemma":0.0065027103,"teacher_disagreement_score":0.01072548,"about_ca_system_score_codex":0.0013070469,"about_ca_system_score_gemma":0.0011776104,"threshold_uncertainty_score":0.021326065},"labels":[],"label_agreement":null},{"id":"W4415369194","doi":"10.14324/111.444/ucloe.icmb25.a15","title":"Assessing Water Loads Post-Flooding in Envelope Assemblies: Effects of Interstices and Hydrostatic Pressure","year":2025,"lang":"","type":"article","venue":"UCL Open Environment","topic":"Water Systems and 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":"Université de Sherbrooke","funders":"","keywords":"Envelope (radar); Flood myth; Hydrostatic pressure; Hydrostatic equilibrium; Drainage; Residual; Thermocouple; Hydraulics","score_opus":0.006775842897195233,"score_gpt":0.22579949553805623,"score_spread":0.219023652640861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415369194","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":[0.99535865,0.00010789068,0.003111921,0.000018737828,0.0000063704665,0.000013520468,0.00033915153,0.00010213813,0.00094158016],"genre_scores_gemma":[0.99768925,0.00006806536,0.0015186935,0.000008579633,9.14584e-7,0.000008044998,0.00021859116,0.000024740973,0.00046315396],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978393,0.000013200814,0.000008175071,0.000037793114,0.000109643086,0.000047227608],"domain_scores_gemma":[0.9996061,0.00009517171,0.000058907888,0.000035658126,0.0001619427,0.000042329208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035042103,0.00042789668,0.00031266143,0.000360972,0.00045737327,0.000555852,0.00027047814,0.0002656352,0.0013586876],"category_scores_gemma":[0.00054464245,0.00020298173,0.00021897722,0.0003500885,0.0003194709,0.00045306346,0.00033701962,0.0003647187,0.00023659939],"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":[0.00066197297,0.00017335748,0.08312184,0.000288825,0.00006167554,0.00045380736,0.00048588874,0.064469285,0.7916731,0.00018054058,0.0006861461,0.057743493],"study_design_scores_gemma":[0.00001687096,0.0009911454,0.43369454,0.000033886823,0.00009326403,0.0002220687,0.0014018139,0.12222561,0.4377364,0.00027662225,0.0032234387,0.00008447927],"about_ca_topic_score_codex":0.034923315,"about_ca_topic_score_gemma":0.09703734,"teacher_disagreement_score":0.034923315,"about_ca_system_score_codex":0.0008462117,"about_ca_system_score_gemma":0.0004980203,"threshold_uncertainty_score":0.06944007},"labels":[],"label_agreement":null},{"id":"W4415412300","doi":"10.1093/obo/9780197768709-0050","title":"Pipeline Resistance","year":2025,"lang":"en","type":"reference-entry","venue":"","topic":"Water Systems and 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":"Scholarship; Environmental justice; Resistance (ecology); Opposition (politics); Indigenous; Pipeline (software); Capitalism; Sustainability","score_opus":0.0095413838645929,"score_gpt":0.20144256097336563,"score_spread":0.19190117710877272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415412300","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":[0.001813554,0.0014939194,0.0034187448,0.012333508,0.0036888851,0.00019087951,0.0024848878,0.001235762,0.97333986],"genre_scores_gemma":[0.017194832,0.0019226872,0.002047456,0.006206179,0.00065613195,0.00016592005,0.0024392144,0.000668682,0.9686989],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970144,0.00043505075,0.00020019521,0.00063421385,0.0012720007,0.0004442711],"domain_scores_gemma":[0.9966372,0.00045762982,0.00018154082,0.0005319426,0.0015677813,0.0006237964],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016107874,0.0007478266,0.0005256382,0.0012783831,0.0038726877,0.008292465,0.0017573092,0.0036313082,0.5326163],"category_scores_gemma":[0.008473549,0.00046467548,0.0005939484,0.0015468311,0.0014638633,0.0056356974,0.00512538,0.00312068,0.33141395],"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":[0.000048798483,0.000044566987,0.00085992937,0.00027354612,0.000007068987,0.000269519,0.0011262391,0.000098978926,0.0004911712,0.057015505,0.79938865,0.14037591],"study_design_scores_gemma":[0.00000234509,0.0000067939473,0.00023680608,0.00006664043,0.0000013817993,0.00009830819,0.00023625635,0.000028269806,0.00009581079,0.0019554398,0.99726725,0.00000478163],"about_ca_topic_score_codex":0.0056707175,"about_ca_topic_score_gemma":0.008269157,"teacher_disagreement_score":0.5326163,"about_ca_system_score_codex":0.0027476416,"about_ca_system_score_gemma":0.0055108783,"threshold_uncertainty_score":0.6666661},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"agree"},{"id":"W4415534673","doi":"10.1016/j.jwpe.2025.108907","title":"MILP model for optimal day-ahead scheduling of water distribution systems considering pumping efficiency and reliability","year":2025,"lang":"en","type":"article","venue":"Journal of Water Process Engineering","topic":"Water Systems and 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":"Simon Fraser University","funders":"Global Affairs Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Scheduling (production processes); Reliability (semiconductor); Linear programming; Nonlinear system; Nonlinear programming; Constraint (computer-aided design); Job shop scheduling","score_opus":0.008115519732866583,"score_gpt":0.2135963981698919,"score_spread":0.2054808784370253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415534673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1607772,0.0012059219,0.77546763,0.0014830879,0.0004277434,0.00036166134,0.0025237477,0.00096160703,0.056791376],"genre_scores_gemma":[0.9596725,0.00031994996,0.029393138,0.00011784802,0.000052769974,0.0002789283,0.00059783185,0.000087308275,0.009479683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954337,0.00015112427,0.0000145986205,0.000055311943,0.00010096477,0.00013460037],"domain_scores_gemma":[0.99927026,0.00042231393,0.00008276067,0.000020978645,0.00013754792,0.0000660675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007552138,0.0009995664,0.001741991,0.00083309534,0.00077456975,0.0017255233,0.0012657613,0.0016649003,0.005877128],"category_scores_gemma":[0.0014511518,0.0009622519,0.00089230394,0.0010912583,0.00059551856,0.00073188025,0.00081490114,0.0014234892,0.0005172774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016656668,0.0000074041563,0.00004529315,0.00001055663,0.000006515639,0.000022269602,0.000004058037,0.9983334,0.00009929012,0.00068358507,0.00014354981,0.00062740897],"study_design_scores_gemma":[0.000004475916,0.000008814261,0.000039235452,0.0000022027266,0.00000336866,0.0000023112575,0.00000441753,0.99935704,0.000048098696,0.0004266015,0.000101711,0.0000016981759],"about_ca_topic_score_codex":0.023385582,"about_ca_topic_score_gemma":0.016340228,"teacher_disagreement_score":0.023385582,"about_ca_system_score_codex":0.0015918653,"about_ca_system_score_gemma":0.0019530859,"threshold_uncertainty_score":0.046498954},"labels":[],"label_agreement":null},{"id":"W4416371132","doi":"10.5120/ijca2025925915","title":"Design and Implementation of a Multi-Tier Scheduling Framework for Real-Time Urban Water Logging Detection and Dispatch Optimization","year":2025,"lang":"","type":"article","venue":"International Journal of Computer Applications","topic":"Water Systems and 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":"Scheduling (production processes); Logging; Economic dispatch; Optimization algorithm","score_opus":0.009744230962308364,"score_gpt":0.276965922968379,"score_spread":0.26722169200607065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416371132","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":[0.019826708,0.00014852817,0.97244835,0.00019901269,0.00006828052,0.00018368753,0.00011557351,0.00361169,0.003398168],"genre_scores_gemma":[0.5767577,0.00016649831,0.41883135,0.0001840985,0.00003796621,0.00026973718,0.0003095801,0.00035554275,0.0030874927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961615,0.000056320918,0.000024751009,0.00010084173,0.000104565006,0.0000973841],"domain_scores_gemma":[0.99964046,0.00009573894,0.000037268568,0.00004007672,0.00011444375,0.00007196034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009005474,0.00073509495,0.0005234283,0.00033761325,0.00055407523,0.0012300214,0.001492116,0.00066411594,0.0029491924],"category_scores_gemma":[0.0010468803,0.00042701673,0.0006871727,0.0002526517,0.0005403416,0.00079777144,0.000941887,0.0009298747,0.00055855047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018371495,0.0001488267,0.0016717318,0.00008265714,0.000050431834,0.0001161971,0.000108114415,0.92430216,0.012672808,0.011040998,0.002896649,0.046725668],"study_design_scores_gemma":[0.000010846613,0.000020104737,0.00008312878,0.0000019080917,0.000004836364,0.000006345348,0.000010671936,0.99682,0.0011212651,0.0009838868,0.00093269424,0.000004336471],"about_ca_topic_score_codex":0.013945695,"about_ca_topic_score_gemma":0.018151432,"teacher_disagreement_score":0.013945695,"about_ca_system_score_codex":0.0011886536,"about_ca_system_score_gemma":0.0027940643,"threshold_uncertainty_score":0.027729034},"labels":[],"label_agreement":null},{"id":"W4416401301","doi":"10.1109/ismar-adjunct68609.2025.00045","title":"An Immersive Digital Twin with Virtual Agent Interface for Pipeline Leak Simulation and Monitoring","year":2025,"lang":"","type":"article","venue":"","topic":"Water Systems and 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":"University of Calgary","funders":"Alberta Innovates","keywords":"Pipeline (software); Interface (matter); Virtual reality; User interface; Situation awareness; Virtual machine","score_opus":0.011689940621261732,"score_gpt":0.26068353184873694,"score_spread":0.24899359122747522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416401301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054222163,0.000112577494,0.9305382,0.00017101849,0.000111555346,0.00029777325,0.000333142,0.006192879,0.008020814],"genre_scores_gemma":[0.4506277,0.00024108833,0.53632885,0.00022689316,0.00003313623,0.0006011626,0.0007062482,0.00075482466,0.010480088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997814,0.00007857587,0.000015687832,0.00003605255,0.00007078991,0.000017424998],"domain_scores_gemma":[0.99959034,0.0002118111,0.000024993042,0.00005984968,0.00005419957,0.00005879449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004992092,0.0005861053,0.0003525334,0.0002682958,0.00022287617,0.00093102007,0.0012380618,0.0005838622,0.014747627],"category_scores_gemma":[0.001692599,0.00029447887,0.00046288897,0.000135137,0.00033424734,0.0010625295,0.0017861571,0.0005479411,0.0009208013],"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":[0.003795859,0.0015056782,0.0066042496,0.0014089688,0.0003057851,0.001878342,0.0046386933,0.2473614,0.20772594,0.051393274,0.02391945,0.44946238],"study_design_scores_gemma":[0.0002373236,0.0007795238,0.0016504391,0.000074642296,0.000112083,0.0005571999,0.00032214442,0.89530295,0.031180076,0.008188263,0.06149536,0.00010006492],"about_ca_topic_score_codex":0.0009462977,"about_ca_topic_score_gemma":0.0011347353,"teacher_disagreement_score":0.014747627,"about_ca_system_score_codex":0.00022979529,"about_ca_system_score_gemma":0.00041214994,"threshold_uncertainty_score":0.04933572},"labels":[],"label_agreement":null},{"id":"W4416534320","doi":"10.1007/978-3-031-95107-7_27","title":"Data-Driven Leak Detection in Water Distribution Networks","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Water Systems and 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":"Northern Digital (Canada); University of Waterloo","funders":"","keywords":"Leak detection; Leak; Resource (disambiguation); Scale (ratio); Limit (mathematics); SIGNAL (programming language); Pipe network analysis","score_opus":0.007987049489473836,"score_gpt":0.18272875813919037,"score_spread":0.17474170864971653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416534320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05840877,0.0006701127,0.9360657,0.000346319,0.000094718715,0.00004914261,0.00040708983,0.0021832266,0.001774977],"genre_scores_gemma":[0.8952336,0.00036989464,0.09939746,0.00009416943,0.00007079252,0.00006526024,0.0006314996,0.00015905261,0.003978434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997123,0.000058428977,0.000014520963,0.00007725235,0.0000924609,0.00004493216],"domain_scores_gemma":[0.99895763,0.0006943579,0.000051222716,0.00007434107,0.00018888486,0.000033610846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052914093,0.0006649433,0.0007643881,0.0003781646,0.0002210011,0.0008594757,0.0009943249,0.0005851196,0.0013533747],"category_scores_gemma":[0.0022836549,0.00041514475,0.00032120524,0.0005173824,0.00035830407,0.0012118293,0.00068707246,0.0007754039,0.0003419029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003052194,0.000091794805,0.0014923525,0.00011324903,0.000029059247,0.00008628803,0.000039070244,0.7889879,0.0069847633,0.0036327527,0.0040901396,0.1941475],"study_design_scores_gemma":[0.0000018874276,0.00000746211,0.000091064765,0.0000020182476,0.0000012446412,0.000007348431,0.0000043146824,0.99674976,0.0012741924,0.0016644796,0.00019450119,0.0000016837249],"about_ca_topic_score_codex":0.0045051132,"about_ca_topic_score_gemma":0.0045499974,"teacher_disagreement_score":0.0045051132,"about_ca_system_score_codex":0.00069672713,"about_ca_system_score_gemma":0.00062811194,"threshold_uncertainty_score":0.008957803},"labels":[],"label_agreement":null},{"id":"W4416534348","doi":"10.1007/978-3-031-95107-7_2","title":"Multi-objective Optimization for Renewal Planning of Sewer Systems Under Changing Climate","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Water Systems and 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":"Concordia University; National Research Council Canada","funders":"","keywords":"Sanitary sewer; Time horizon; Outcome (game theory); Multi-objective optimization; Service (business); Flexibility (engineering); Optimal maintenance","score_opus":0.011433808856745763,"score_gpt":0.2115920737909062,"score_spread":0.20015826493416042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416534348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07164273,0.0026116187,0.90195346,0.00071508175,0.00017035782,0.00015539046,0.0004647192,0.00033860686,0.021948105],"genre_scores_gemma":[0.8388355,0.0018650484,0.13502552,0.00015811871,0.00014643771,0.00043727356,0.00053948467,0.00037759094,0.022615166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995858,0.00018984854,0.000013329307,0.00005543459,0.00008771582,0.00006785358],"domain_scores_gemma":[0.99930847,0.0005130851,0.000051227515,0.00002114038,0.000066440545,0.000039613198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010838918,0.0011643243,0.001764998,0.00088436704,0.0006018471,0.0014663991,0.001334141,0.001986073,0.004335858],"category_scores_gemma":[0.0022208937,0.0011603881,0.0015895299,0.001493298,0.00067800825,0.0012838177,0.001085498,0.001572737,0.00040519563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007752205,0.000007784848,0.000045873847,0.000016084436,0.000009569163,0.000009878309,0.0000050767194,0.99608827,0.000107904096,0.0008042743,0.00017191829,0.0027257008],"study_design_scores_gemma":[0.0000018102014,0.000008177481,0.000053432555,0.0000034274,0.0000027628164,0.0000023395783,0.000004064843,0.99880695,0.00005233151,0.00091567374,0.00014695882,0.000002023281],"about_ca_topic_score_codex":0.011875196,"about_ca_topic_score_gemma":0.011405552,"teacher_disagreement_score":0.011875196,"about_ca_system_score_codex":0.0014750964,"about_ca_system_score_gemma":0.0010629673,"threshold_uncertainty_score":0.023612142},"labels":[],"label_agreement":null},{"id":"W4416579009","doi":"10.1680/fsts.59757.094","title":"Hydraulic Stability Model Testing for New Marine Container Terminal at Haifa, Israel","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Water Systems and 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":"Canadian Cardiovascular Society","funders":"","keywords":"Terminal (telecommunication); Container (type theory); Stability (learning theory); Mathematical model","score_opus":0.033964730657568956,"score_gpt":0.19998737644584552,"score_spread":0.16602264578827658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416579009","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":[0.71827114,0.00035990382,0.15394065,0.0015772281,0.0002927667,0.00014042278,0.0024523789,0.0018868516,0.12107861],"genre_scores_gemma":[0.9599506,0.00012560633,0.012583828,0.000051736966,0.000018349388,0.000076132084,0.0011155384,0.00017582324,0.025902467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998629,0.000030045898,0.0000058713767,0.000020539372,0.00005928156,0.00002145474],"domain_scores_gemma":[0.9997497,0.00010642836,0.000010128735,0.000019007979,0.00009508504,0.000019662293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046467723,0.00033911053,0.00034253747,0.00030671878,0.0006159425,0.0005450145,0.00072497356,0.00055443525,0.0045970045],"category_scores_gemma":[0.0005689985,0.00016340455,0.00040768564,0.00017349186,0.0003045973,0.0005880024,0.00033927578,0.00050235656,0.000649471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034122393,0.00013614474,0.0035270248,0.000091945825,0.000024935316,0.0002879088,0.00017560867,0.90242296,0.025538329,0.004797112,0.013929055,0.048727755],"study_design_scores_gemma":[0.000026177826,0.00013487824,0.0017889247,0.000017391905,0.000008390868,0.000034093126,0.0001113694,0.9795529,0.012880043,0.0016250595,0.003803864,0.000016793174],"about_ca_topic_score_codex":0.017744318,"about_ca_topic_score_gemma":0.025515992,"teacher_disagreement_score":0.017744318,"about_ca_system_score_codex":0.0011246578,"about_ca_system_score_gemma":0.0008519905,"threshold_uncertainty_score":0.035282016},"labels":[],"label_agreement":null},{"id":"W4416812804","doi":"10.1016/j.ress.2025.112038","title":"Intelligent prediction of flammable gas dispersion from urban gas pipeline leakage using physics-informed neural networks","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Pipeline transport; Artificial neural network; Residual; Mean squared error; Pipeline (software); Real gas; Leakage (economics); Flammable liquid","score_opus":0.006411857643165284,"score_gpt":0.1838523367423512,"score_spread":0.17744047909918592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416812804","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":[0.867945,0.00040901787,0.1284712,0.0002293494,0.000049663187,0.000020606356,0.00020300539,0.00057743286,0.0020946686],"genre_scores_gemma":[0.9969187,0.000040345385,0.0024862841,0.0000107856085,0.000008039053,0.000006495191,0.00007227623,0.000006326187,0.00045073073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993813,0.00000943405,0.0000033009928,0.000022145265,0.000013350127,0.000013654521],"domain_scores_gemma":[0.9997824,0.00011843101,0.000039165552,0.000009560704,0.000037776805,0.000012618624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018117212,0.0005590788,0.00041761264,0.00054845284,0.00023305806,0.00041284316,0.0003874717,0.00058282254,0.00038509615],"category_scores_gemma":[0.00066028035,0.00034068534,0.00042335957,0.0004003101,0.00026586757,0.00052890065,0.0003338881,0.0005245199,0.000076271324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050944298,0.000037170517,0.0026541604,0.000010508069,0.000019909987,0.00002610498,0.0000069957987,0.9876208,0.001052126,0.00015170625,0.00012168022,0.008247894],"study_design_scores_gemma":[9.2021384e-7,0.0000023419598,0.0002957044,3.5935193e-7,0.0000012210343,9.1975755e-7,6.8866694e-7,0.9995128,0.00012254882,0.00005369173,0.000008033358,7.900034e-7],"about_ca_topic_score_codex":0.018246412,"about_ca_topic_score_gemma":0.018866204,"teacher_disagreement_score":0.018246412,"about_ca_system_score_codex":0.00061593135,"about_ca_system_score_gemma":0.00057429686,"threshold_uncertainty_score":0.036280394},"labels":[],"label_agreement":null},{"id":"W4417015947","doi":"10.1002/aws2.70044","title":"Climate Change Impacts and Adaptation for Water Distribution Systems: Physical, Hydraulic, and Water Quality Integrity","year":2025,"lang":"en","type":"article","venue":"AWWA Water Science","topic":"Water Systems and 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; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Climate change; Resilience (materials science); Adaptation (eye); Natural disaster; Psychological resilience; Sustainable development; Sustainability; Climate change adaptation; Human systems engineering","score_opus":0.03333639391689203,"score_gpt":0.27339431648887236,"score_spread":0.24005792257198033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417015947","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":[0.36935842,0.2878844,0.055193532,0.037997045,0.0017455933,0.00031921177,0.00088769215,0.00022221626,0.24639185],"genre_scores_gemma":[0.93930256,0.054131433,0.0029460501,0.00049414014,0.0002518882,0.000032775824,0.00007767213,0.00001773363,0.0027457129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912053,0.0003439608,0.00005492089,0.00008368193,0.00026901823,0.00012786817],"domain_scores_gemma":[0.9985208,0.00066156697,0.0003091879,0.00006107473,0.00036460016,0.00008280284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013760456,0.00046531422,0.0003256742,0.00091178063,0.0005717831,0.002791483,0.00046021593,0.00095435564,0.003801752],"category_scores_gemma":[0.0023780104,0.00011381298,0.00047076202,0.0018225627,0.0011441839,0.0016711304,0.0014783054,0.0007495254,0.0001920681],"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":[0.00020167671,0.0003536451,0.048124555,0.011194826,0.0012696438,0.0016104453,0.002236334,0.1135415,0.019040085,0.18416701,0.020499747,0.5977605],"study_design_scores_gemma":[0.000038329476,0.0009961579,0.1968379,0.0060988674,0.0011319587,0.0014547132,0.015881218,0.05825466,0.025973542,0.1892805,0.5037407,0.0003114909],"about_ca_topic_score_codex":0.0058907196,"about_ca_topic_score_gemma":0.0063470574,"teacher_disagreement_score":0.0058907196,"about_ca_system_score_codex":0.0019444191,"about_ca_system_score_gemma":0.0015396344,"threshold_uncertainty_score":0.014107823},"labels":[],"label_agreement":null},{"id":"W4417218174","doi":"10.1130/abs/2025am-9238","title":"A 3D Flow Simulator Approach for Characterizing the Urban Hydro(geo)logy of Edmonton, Alberta, Canada","year":2025,"lang":"","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Water Systems and 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":"Flow (mathematics); Field (mathematics); Work (physics)","score_opus":0.00938593480991731,"score_gpt":0.20050749721804298,"score_spread":0.19112156240812567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417218174","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":[0.622832,0.0004956099,0.2911972,0.0009488624,0.00020543506,0.00047049095,0.015842559,0.0058480552,0.062159754],"genre_scores_gemma":[0.90584975,0.00032962702,0.07816523,0.000097192926,0.000016351767,0.00019441858,0.0041993554,0.0002685175,0.010879582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999126,0.000011574407,0.0000033442489,0.000015246865,0.000038235034,0.000018938637],"domain_scores_gemma":[0.99983335,0.000035418016,0.000007930113,0.000009350766,0.00008593894,0.000027895316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015617658,0.0006515364,0.00041500927,0.0008602697,0.0010441481,0.0009759631,0.0010254405,0.00070233975,0.0042758062],"category_scores_gemma":[0.00043451865,0.00042608724,0.00049352244,0.0012383508,0.000429383,0.00024395401,0.0004967213,0.00042085696,0.0003689398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001458505,0.000021091913,0.0024634406,0.000014215999,0.000008953741,0.000051114213,0.000034000568,0.9885861,0.0006749309,0.0007308142,0.0013540515,0.0060466644],"study_design_scores_gemma":[0.0000056849062,0.0000027853982,0.00073465507,0.000003047822,0.0000023661405,0.000003942182,0.000026543514,0.9981982,0.00015363607,0.00013852028,0.0007247219,0.000005872736],"about_ca_topic_score_codex":0.8810234,"about_ca_topic_score_gemma":0.9076586,"teacher_disagreement_score":0.11897659,"about_ca_system_score_codex":0.004105246,"about_ca_system_score_gemma":0.009138591,"threshold_uncertainty_score":0.23935449},"labels":[],"label_agreement":null},{"id":"W4417328003","doi":"10.1061/jwrmd5.wreng-7040","title":"Optimal Water–Power Flow Solutions Using Polyhedral and Conic Relaxations","year":2025,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and 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":"Conic section; Local optimum; Nonlinear system; Regular polygon; Nonlinear programming; Power flow; Power (physics); Flow (mathematics); Linear programming","score_opus":0.010817224732468026,"score_gpt":0.21186051900674402,"score_spread":0.201043294274276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417328003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015235606,0.00048243525,0.96980065,0.0003602909,0.000092827686,0.000150051,0.00017509812,0.0002562886,0.013446702],"genre_scores_gemma":[0.39694178,0.00076625706,0.5922272,0.00029393053,0.000111803514,0.0006441664,0.0005679454,0.00034190904,0.008105025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949,0.00019254282,0.000024262785,0.00009354909,0.0001246707,0.00007491932],"domain_scores_gemma":[0.9980385,0.0014377763,0.00016277285,0.00007298065,0.00022117459,0.000066910994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001352416,0.0015704832,0.0014348692,0.0008471419,0.00062612223,0.0015758255,0.001030089,0.0016686224,0.0047771554],"category_scores_gemma":[0.004355218,0.00089138665,0.0010771223,0.0009926159,0.0010969834,0.0009974721,0.0014161512,0.0020649168,0.0005983678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001574231,0.00002011388,0.000098328186,0.000033550525,0.00000710183,0.000020132667,0.000017061102,0.99061614,0.00015327544,0.003614313,0.0004033038,0.0050008977],"study_design_scores_gemma":[0.000008724085,0.0000108503955,0.00002057063,0.0000083046225,0.000002076993,0.000004185343,0.000011555684,0.997471,0.000106083244,0.0019402726,0.00041370737,0.000002714726],"about_ca_topic_score_codex":0.012903242,"about_ca_topic_score_gemma":0.008814758,"teacher_disagreement_score":0.012903242,"about_ca_system_score_codex":0.0009998345,"about_ca_system_score_gemma":0.0022446304,"threshold_uncertainty_score":0.025656283},"labels":[],"label_agreement":null},{"id":"W45339841","doi":"","title":"Performance Assessment Model for Wastewater Treatment Plants","year":2011,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"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":"Analytic hierarchy process; Sewage treatment; Engineering; Wastewater; Robustness (evolution); Rehabilitation; Business; Environmental planning; Environmental economics; Operations management; Environmental engineering; Environmental science; Operations research","score_opus":0.03628514902209016,"score_gpt":0.25913601063630126,"score_spread":0.2228508616142111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W45339841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09543236,0.0009989413,0.8322404,0.0017339331,0.00020692493,0.00063367834,0.0018864885,0.0009067653,0.06596047],"genre_scores_gemma":[0.9279135,0.0008162215,0.037653033,0.00019865246,0.000065250875,0.0010336405,0.0009649619,0.00008536386,0.03126932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986577,0.0004742311,0.00006386266,0.00025531594,0.00029190688,0.0002570486],"domain_scores_gemma":[0.9978976,0.0011927128,0.00021907408,0.000038113467,0.0005940847,0.000058309623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025511782,0.0017458161,0.001149836,0.0012900309,0.00090491737,0.0023842757,0.0019029005,0.002633636,0.0074911495],"category_scores_gemma":[0.004601226,0.00056425884,0.001557135,0.0010393715,0.00076060864,0.0012897981,0.0015088437,0.0019926063,0.0011986975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016652302,0.000022087766,0.00036003345,0.000029823761,0.000010732674,0.000038362017,0.00003060401,0.99264866,0.00020951133,0.003775672,0.00027010558,0.0025876788],"study_design_scores_gemma":[0.0000051795378,0.000018522265,0.00013087409,0.0000063492625,0.0000053041376,0.000007728411,0.000015531998,0.9978231,0.000071658054,0.0014624399,0.00044881843,0.0000044667017],"about_ca_topic_score_codex":0.029172286,"about_ca_topic_score_gemma":0.010347027,"teacher_disagreement_score":0.029172286,"about_ca_system_score_codex":0.0029111207,"about_ca_system_score_gemma":0.0025106883,"threshold_uncertainty_score":0.058004975},"labels":[],"label_agreement":null},{"id":"W500660","doi":"","title":"Modèle pour le contrôle en temps réel des réseaux d'égout sous surcharge.","year":2001,"lang":"fr","type":"article","venue":"","topic":"Water Systems and 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":"Humanities; Political science; Physics; Philosophy","score_opus":0.025834581897446078,"score_gpt":0.20866663450622064,"score_spread":0.18283205260877455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W500660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08152724,0.005576451,0.7907752,0.0040943143,0.0017970292,0.00038535235,0.011335943,0.0040279077,0.10048051],"genre_scores_gemma":[0.8197089,0.002498973,0.041145165,0.00055166084,0.00022044506,0.0007563609,0.004829801,0.00038493518,0.12990376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995146,0.00008704677,0.000026329313,0.00015782213,0.00013470577,0.00007948733],"domain_scores_gemma":[0.99932647,0.000283105,0.00010057166,0.00004086309,0.00021769147,0.000031378095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006199506,0.0010861197,0.0010530876,0.00061946566,0.0005533796,0.0021872043,0.0019235737,0.0025832043,0.017831093],"category_scores_gemma":[0.0018356555,0.00062749686,0.0015318671,0.0007714285,0.0005080456,0.0014955507,0.0008072078,0.001952427,0.00423961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007998097,0.00004646347,0.0011599739,0.00012813618,0.00005604853,0.00010032444,0.00006429036,0.9683839,0.001150896,0.014389877,0.0038256822,0.010614341],"study_design_scores_gemma":[0.000016679984,0.000025289108,0.0004488826,0.000016227903,0.00001872702,0.000018313089,0.000016778964,0.9899663,0.00027540184,0.004030817,0.0051517757,0.000014868486],"about_ca_topic_score_codex":0.047938786,"about_ca_topic_score_gemma":0.027350744,"teacher_disagreement_score":0.047938786,"about_ca_system_score_codex":0.0017170879,"about_ca_system_score_gemma":0.0021094559,"threshold_uncertainty_score":0.09531951},"labels":[],"label_agreement":null},{"id":"W566747533","doi":"10.14796/jwmm.r223-03","title":"A Lifecycle Cost Based Design Optimization Model for Stormwater Management Systems","year":2005,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Stormwater management; System lifecycle; Application lifecycle management; Computer science; Stormwater; Systems engineering; Engineering; Surface runoff","score_opus":0.029434149304012967,"score_gpt":0.21133925203255916,"score_spread":0.18190510272854618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W566747533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008537098,0.00029311804,0.9787133,0.0003297309,0.000031067015,0.00007223938,0.00018192267,0.00013850829,0.011703116],"genre_scores_gemma":[0.6197729,0.0022309439,0.32835323,0.00022316322,0.00009355871,0.0014356775,0.00077768776,0.00028274173,0.0468302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963856,0.00010117729,0.000013563696,0.000044744465,0.00016504969,0.000036865516],"domain_scores_gemma":[0.99981564,0.00009429274,0.000028311493,0.000011022535,0.000041013485,0.000009755316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006743868,0.00088295,0.0008189841,0.0004299181,0.00041600404,0.0012124376,0.0011956231,0.00131897,0.0051549207],"category_scores_gemma":[0.00094398705,0.0005506917,0.0008387911,0.0005736994,0.0004906632,0.0012149452,0.00072644284,0.0012240147,0.00075223297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068923705,0.000009088156,0.000049015543,0.000026070964,0.000004759772,0.000013670745,0.000009852191,0.9802461,0.0005100358,0.01515294,0.00030430252,0.003667368],"study_design_scores_gemma":[0.000004825993,0.0000098937335,0.00003120185,0.0000040177274,0.0000034528518,0.0000045184156,0.000002866815,0.99406034,0.00014576566,0.0043724696,0.0013578306,0.0000027164183],"about_ca_topic_score_codex":0.0047720755,"about_ca_topic_score_gemma":0.0053636623,"teacher_disagreement_score":0.0051549207,"about_ca_system_score_codex":0.001374165,"about_ca_system_score_gemma":0.0015523642,"threshold_uncertainty_score":0.017244875},"labels":[],"label_agreement":null},{"id":"W582650695","doi":"","title":"Numerical stress analysis of the Fredericton Water tower collapse according to a proposed modified reference length in the ECCS rules for liquid-filled conical shells.","year":2001,"lang":"en","type":"article","venue":"Ghent University Academic Bibliography (Ghent University)","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"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":"Tower; Conical surface; Stress (linguistics); Meteorology; Mechanics; Geology; Mathematics; Engineering; Geometry; Geography; Physics; Structural engineering","score_opus":0.021402524053384848,"score_gpt":0.2099622247617279,"score_spread":0.18855970070834305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W582650695","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":[0.8274496,0.0008400235,0.107248016,0.0010914783,0.00034759415,0.00014463629,0.000597957,0.00038948288,0.06189127],"genre_scores_gemma":[0.98844457,0.00012308688,0.006233009,0.00006097439,0.000017176586,0.000040885552,0.00009412177,0.00007564383,0.0049104746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986684,0.000039960025,0.000007522867,0.000014329115,0.000039555052,0.000031774955],"domain_scores_gemma":[0.99966836,0.00009200701,0.00004587385,0.000035547684,0.00010935078,0.00004881491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005031744,0.00027973583,0.00042290697,0.0006309499,0.0005238568,0.000622202,0.00086408935,0.00094582036,0.0032146394],"category_scores_gemma":[0.0014547498,0.00020545775,0.00052086293,0.00046768878,0.0009177427,0.0006548664,0.0006120982,0.0006242998,0.00026322753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019209736,0.00007051101,0.0018117804,0.00008831031,0.000025626541,0.00025773095,0.00016233927,0.90054166,0.009877458,0.07428466,0.0030995272,0.009588276],"study_design_scores_gemma":[0.000008616575,0.000018459092,0.0007202794,0.000004315745,0.000002561385,0.000011419996,0.00002529489,0.99542,0.00048742766,0.0030714148,0.00022039075,0.000009921173],"about_ca_topic_score_codex":0.0077362005,"about_ca_topic_score_gemma":0.0052147848,"teacher_disagreement_score":0.9922638,"about_ca_system_score_codex":0.0007768695,"about_ca_system_score_gemma":0.0005886023,"threshold_uncertainty_score":0.0153823495},"labels":[],"label_agreement":null},{"id":"W68706397","doi":"10.1007/s11269-014-0752-6","title":"Leak Size, Detectability and Test Conditions in Pressurized Pipe Systems","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":75,"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":"Università degli Studi di Perugia; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Leak; Leak detection; Benchmarking; Steady state (chemistry); Transient (computer programming); Hydrogeology; Environmental science; Nuclear engineering; Computer science; Reliability engineering; Mechanics; Engineering; Chemistry; Geotechnical engineering; Physics; Environmental engineering","score_opus":0.003506955429397738,"score_gpt":0.16398003555666973,"score_spread":0.160473080127272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W68706397","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":[0.9910138,0.000076999655,0.008406983,0.000028431168,0.000002515773,0.000011042442,0.000071591785,0.0001382754,0.00025046704],"genre_scores_gemma":[0.9995908,0.000011991044,0.00026356534,0.0000019261051,0.0000010486003,0.0000036231559,0.000033772703,0.00000587334,0.000087493616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993149,0.00023423451,0.000053126776,0.00010548467,0.00018429005,0.00010800429],"domain_scores_gemma":[0.983204,0.013808886,0.0014691254,0.00031504076,0.001030177,0.0001728274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014605772,0.0004732765,0.0006324665,0.00075876835,0.00028600096,0.000571739,0.0005850167,0.00066965644,0.0008569697],"category_scores_gemma":[0.009152072,0.00032409705,0.0003319093,0.00044828973,0.00074650225,0.0013357786,0.000440676,0.00040272358,0.00013149981],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0059354207,0.00065580144,0.060162246,0.00034337904,0.0001353706,0.00055247254,0.00047139483,0.7783415,0.11608656,0.00091799005,0.0004160676,0.03598182],"study_design_scores_gemma":[0.00007453731,0.0018763669,0.04244977,0.000013604684,0.000074362935,0.00013717776,0.00015799576,0.8901234,0.0643236,0.00062137865,0.00008995333,0.00005785694],"about_ca_topic_score_codex":0.004506202,"about_ca_topic_score_gemma":0.0031209504,"teacher_disagreement_score":0.004506202,"about_ca_system_score_codex":0.000660625,"about_ca_system_score_gemma":0.00040663083,"threshold_uncertainty_score":0.008959949},"labels":[],"label_agreement":null},{"id":"W6902144675","doi":"10.6084/m9.figshare.3814308.v1","title":"Water mains renewal planning framework for small to medium sized water utilities: a life cycle cost analysis approach","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Water Systems and 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":"Mains electricity; Water supply; Life cycle costing; Electricity; Cost analysis; Water utility; Life-cycle cost analysis; Economic analysis","score_opus":0.04055463000435721,"score_gpt":0.23799814666839164,"score_spread":0.19744351666403442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902144675","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":[0.029512748,0.00077747996,0.9422132,0.001103086,0.000047645317,0.0004326688,0.001193539,0.00040567442,0.024313932],"genre_scores_gemma":[0.66739106,0.0015112271,0.31121063,0.00024783553,0.00006353851,0.0013928047,0.0014089042,0.00029934128,0.016474597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.999238,0.00033700262,0.000023056054,0.000071341245,0.00020800736,0.00012255042],"domain_scores_gemma":[0.99925166,0.00043074824,0.00005234972,0.000024922416,0.00017929838,0.000061028335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021075697,0.0013595516,0.0012285379,0.002164401,0.00081773644,0.0028107946,0.0020744978,0.001693833,0.0069331005],"category_scores_gemma":[0.0023301002,0.0011139076,0.0015065811,0.0021643157,0.0006513312,0.0015362815,0.0012683559,0.0014128307,0.000531874],"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":[0.000008497026,0.000016413567,0.00017190915,0.000029813591,0.000014690033,0.000038688468,0.000018507779,0.9856806,0.00010971987,0.007158064,0.00052562426,0.006227536],"study_design_scores_gemma":[0.0000047280187,0.000012364565,0.00009744033,0.0000125293955,0.0000089009545,0.00001057322,0.000029479863,0.9943423,0.00007032728,0.0042325966,0.001172184,0.000006523639],"about_ca_topic_score_codex":0.06321489,"about_ca_topic_score_gemma":0.065918475,"teacher_disagreement_score":0.06321489,"about_ca_system_score_codex":0.0049294946,"about_ca_system_score_gemma":0.0060815704,"threshold_uncertainty_score":0.12569392},"labels":[],"label_agreement":null},{"id":"W6907531422","doi":"10.22093/wwj.2023.374161.3307","title":"Selection of the Best Statistical Index of Nodal Pressure Values for Use in Calibrating the Hydraulic Model of the Water Distribution Network Based on Field Data Processing","year":2023,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Water Systems and 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 One (Canada)","funders":"","keywords":"Standard deviation; Range (aeronautics); Histogram; Calibration; Data processing; Probability distribution; Normal distribution; Field (mathematics); Mode (computer interface)","score_opus":0.19672453115584787,"score_gpt":0.44599771415643774,"score_spread":0.24927318300058987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6907531422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37293038,0.00020202994,0.62102234,0.00015626856,0.000042287964,0.00023155933,0.0006438546,0.0017765574,0.0029947022],"genre_scores_gemma":[0.8165585,0.0002238946,0.18023424,0.000030155867,0.00002890075,0.00031273696,0.0018778817,0.00018982834,0.00054387015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998825,0.00029972848,0.00011406284,0.00025970375,0.00037485894,0.0001266928],"domain_scores_gemma":[0.9978523,0.00082238886,0.0002629053,0.00017227729,0.0007967259,0.00009342341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024144654,0.00090636755,0.0009969387,0.0038022827,0.00049652223,0.0013753381,0.0006799706,0.00050202437,0.0010805787],"category_scores_gemma":[0.007857406,0.0003369213,0.000762804,0.002910142,0.0004299134,0.0013659286,0.0004327678,0.00048515905,0.00039581113],"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":[0.0004953934,0.0005583601,0.13048252,0.00060742156,0.00022376841,0.00054175017,0.00069484196,0.40037772,0.056697965,0.0038157278,0.00438482,0.40111974],"study_design_scores_gemma":[0.000044526598,0.00024409863,0.048485994,0.000045567907,0.0000850377,0.00010832008,0.0003630781,0.9221984,0.025108758,0.0016650549,0.0015786366,0.00007250729],"about_ca_topic_score_codex":0.0042809397,"about_ca_topic_score_gemma":0.0025007795,"teacher_disagreement_score":0.0042809397,"about_ca_system_score_codex":0.0005453008,"about_ca_system_score_gemma":0.0013985916,"threshold_uncertainty_score":0.012769043},"labels":[],"label_agreement":null},{"id":"W6908616828","doi":"10.26077/6jhf-md25","title":"Water Main Break Rates in the USA and Canada: A Comprehensive Study","year":2024,"lang":"en","type":"article","venue":"Digital Commons - USU (Utah State University)","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"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":"Pipeline transport; Water quality; Mains electricity; Water supply; Schedule; Piping; Water utility; Water use; Economic impact analysis","score_opus":0.010737575883017563,"score_gpt":0.1775915625956528,"score_spread":0.16685398671263524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6908616828","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":[0.8188661,0.022024203,0.00086667034,0.0039159628,0.00015665388,0.00052128994,0.12720193,0.00016697441,0.026280265],"genre_scores_gemma":[0.9179957,0.023879722,0.0013700437,0.0015226426,0.000055518896,0.00019226209,0.04687279,0.000079432255,0.008031831],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972144,0.00009365226,0.00021929979,0.00024181389,0.0015489814,0.0006819442],"domain_scores_gemma":[0.99060905,0.00028236778,0.0009599475,0.00013563359,0.0066624284,0.0013505836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013709109,0.00067012856,0.0009882094,0.007743374,0.0043323804,0.00356843,0.002392697,0.0008962145,0.0032900886],"category_scores_gemma":[0.0041991123,0.00063395506,0.0017887478,0.023864813,0.0010329596,0.001309779,0.002039238,0.0018095829,0.0006123471],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006771587,0.00009347829,0.9622642,0.00040842063,0.00024197037,0.00024153877,0.0019486805,0.00019106777,0.00008424531,0.0006230023,0.015441882,0.01839379],"study_design_scores_gemma":[0.0000055173828,0.000020281537,0.9837569,0.00029515446,0.00011147913,0.00013834955,0.0051264847,0.00029729356,0.00004838511,0.000056042874,0.01011068,0.000033468496],"about_ca_topic_score_codex":0.99827325,"about_ca_topic_score_gemma":0.99906284,"teacher_disagreement_score":0.058395468,"about_ca_system_score_codex":0.058395468,"about_ca_system_score_gemma":0.09732514,"threshold_uncertainty_score":0.42369074},"labels":[],"label_agreement":null},{"id":"W6937026322","doi":"10.6047/j.issn.1000-8241.2024.06.013","title":"Self-adaptive simulation method for natural gas pipeline considering temperature and pressure compensations","year":2024,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Water Systems and 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":"Petro-Canada","funders":"","keywords":"SCADA; Pipeline (software); Natural gas; Pipeline transport; Process (computing); Volume (thermodynamics); Control theory (sociology); Flow (mathematics); Approximation error; Computer simulation","score_opus":0.12285856620985065,"score_gpt":0.48281199943747244,"score_spread":0.3599534332276218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6937026322","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":[0.017467124,0.000080906844,0.9790283,0.000065143475,0.000044522305,0.00003309469,0.00002454571,0.0005720418,0.0026843913],"genre_scores_gemma":[0.8362188,0.00023615925,0.15733859,0.0000581977,0.000037525766,0.00023500825,0.0001382718,0.00011678394,0.005620784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998379,0.000032982123,0.000010314465,0.00003974255,0.00006448687,0.000014486849],"domain_scores_gemma":[0.9998147,0.000057882095,0.000025369522,0.000018459325,0.00007112121,0.000012463171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028491535,0.00046912293,0.00044433292,0.00039204943,0.00040045302,0.0005404457,0.00075152225,0.00059977075,0.0022864519],"category_scores_gemma":[0.0006982393,0.00025547147,0.00071329996,0.00025211147,0.00035869883,0.000603198,0.0005962106,0.00048252582,0.00027758811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035934754,0.000020200965,0.00058941875,0.000039946295,0.000014778059,0.0000691667,0.00006946596,0.9686114,0.006064447,0.0039271726,0.00030355755,0.020254482],"study_design_scores_gemma":[0.0000016254746,0.000004598228,0.000023929586,7.934225e-7,0.0000011092006,0.0000040936584,0.0000018343226,0.99934345,0.00024832966,0.00015746936,0.00021161605,0.0000011325284],"about_ca_topic_score_codex":0.0075777643,"about_ca_topic_score_gemma":0.0039961957,"teacher_disagreement_score":0.0075777643,"about_ca_system_score_codex":0.00041433843,"about_ca_system_score_gemma":0.0008757393,"threshold_uncertainty_score":0.015067339},"labels":[],"label_agreement":null},{"id":"W6957686534","doi":"10.60692/wk4np-me823","title":"Framework for optimizing chlorine dose in small- to medium-sized water distribution systems: A case of a residential neighbourhood in Lahore, Pakistan","year":2016,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Water Systems and 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":"Okanagan College; Okanagan University College; University of British Columbia","funders":"","keywords":"Chlorine; Residual; Groundwater; Hydrogeology; Water quality; Fuzzy logic","score_opus":0.021298055774351114,"score_gpt":0.2237086091661751,"score_spread":0.20241055339182398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957686534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14287996,0.00072037854,0.8336496,0.0011753853,0.00005003106,0.000303543,0.00074231287,0.00038894784,0.020089889],"genre_scores_gemma":[0.9507437,0.00037063463,0.043918837,0.000048784717,0.000011999323,0.00016659196,0.00022080628,0.000035485817,0.004483185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995208,0.0001533302,0.000017375125,0.00011256736,0.000098341094,0.00009762336],"domain_scores_gemma":[0.9995666,0.00017054418,0.000057689813,0.000017230808,0.00014852291,0.00003951264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009926916,0.0007391957,0.00060663745,0.0006617416,0.0007638027,0.0016144511,0.001440781,0.0015581249,0.0023118935],"category_scores_gemma":[0.001067918,0.00044550712,0.00084069703,0.00053656066,0.0007311573,0.0008328493,0.0011326682,0.0007012212,0.00017505068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012186886,0.00001403339,0.0005353401,0.000026740583,0.00000739165,0.00012988936,0.000029743242,0.9929206,0.0005291781,0.0029834264,0.0002636122,0.0025479533],"study_design_scores_gemma":[0.0000073098013,0.000021955215,0.00025028214,0.0000071478416,0.000008555352,0.000023327226,0.000056758974,0.9969163,0.0002201217,0.001922133,0.0005589308,0.0000071753516],"about_ca_topic_score_codex":0.07243193,"about_ca_topic_score_gemma":0.049466114,"teacher_disagreement_score":0.07243193,"about_ca_system_score_codex":0.0031301647,"about_ca_system_score_gemma":0.0032521468,"threshold_uncertainty_score":0.14402062},"labels":[],"label_agreement":null},{"id":"W6958015687","doi":"10.60692/haefb-vx776","title":"The effects of resonances on time delay estimation for water leak detection in plastic pipes","year":2018,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Water Systems and 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":"Leak; Noise (video); Leak detection; Range (aeronautics); Replicate; Function (biology); Phase (matter); Control theory (sociology)","score_opus":0.006541042863378961,"score_gpt":0.16634998611467045,"score_spread":0.1598089432512915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958015687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4248705,0.0002472137,0.573354,0.00016207091,0.00002031562,0.000032482785,0.000025906254,0.000324706,0.00096281787],"genre_scores_gemma":[0.9581833,0.0001252195,0.04137317,0.000013971062,0.0000060789384,0.000012567285,0.000021745218,0.00003441583,0.00022962656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99888366,0.00060110627,0.000037153095,0.00012549086,0.00028763633,0.00006487553],"domain_scores_gemma":[0.99092954,0.007696173,0.00053135434,0.0002824559,0.0005053081,0.000055141954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025604703,0.0005154423,0.0004113923,0.0005416811,0.00026108848,0.00043525387,0.00031279738,0.00064602256,0.0003112158],"category_scores_gemma":[0.01342439,0.00032031632,0.00031144373,0.00041438921,0.00061792956,0.0007156197,0.0005360004,0.0006351201,0.00009860143],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007231013,0.00010631065,0.0065167085,0.00015304002,0.000044521898,0.0002079989,0.00026348748,0.8663379,0.06335187,0.0020871724,0.00022014865,0.05998769],"study_design_scores_gemma":[0.0000043462715,0.00010712118,0.0013397142,0.0000056660506,0.000006819814,0.000038475493,0.000023415687,0.9841053,0.014087201,0.00018896592,0.000081689504,0.000011246386],"about_ca_topic_score_codex":0.0070362696,"about_ca_topic_score_gemma":0.0043569426,"teacher_disagreement_score":0.0070362696,"about_ca_system_score_codex":0.00046369733,"about_ca_system_score_gemma":0.00071831077,"threshold_uncertainty_score":0.013990641},"labels":[],"label_agreement":null},{"id":"W6958475809","doi":"10.6084/m9.figshare.3814308","title":"Water mains renewal planning framework for small to medium sized water utilities: a life cycle cost analysis approach","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Water Systems and 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":"Mains electricity; Water supply; Life cycle costing; Electricity; Cost analysis; Water utility; Life-cycle cost analysis; Economic analysis","score_opus":0.04055463000435721,"score_gpt":0.23799814666839164,"score_spread":0.19744351666403442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958475809","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":[0.029512748,0.00077747996,0.9422132,0.001103086,0.000047645317,0.0004326688,0.001193539,0.00040567442,0.024313932],"genre_scores_gemma":[0.66739106,0.0015112271,0.31121063,0.00024783553,0.00006353851,0.0013928047,0.0014089042,0.00029934128,0.016474597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999238,0.00033700262,0.000023056054,0.000071341245,0.00020800736,0.00012255042],"domain_scores_gemma":[0.99925166,0.00043074824,0.00005234972,0.000024922416,0.00017929838,0.000061028335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021075697,0.0013595516,0.0012285379,0.002164401,0.00081773644,0.0028107946,0.0020744978,0.001693833,0.0069331005],"category_scores_gemma":[0.0023301002,0.0011139076,0.0015065811,0.0021643157,0.0006513312,0.0015362815,0.0012683559,0.0014128307,0.000531874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008497026,0.000016413567,0.00017190915,0.000029813591,0.000014690033,0.000038688468,0.000018507779,0.9856806,0.00010971987,0.007158064,0.00052562426,0.006227536],"study_design_scores_gemma":[0.0000047280187,0.000012364565,0.00009744033,0.0000125293955,0.0000089009545,0.00001057322,0.000029479863,0.9943423,0.00007032728,0.0042325966,0.001172184,0.000006523639],"about_ca_topic_score_codex":0.06321489,"about_ca_topic_score_gemma":0.065918475,"teacher_disagreement_score":0.06321489,"about_ca_system_score_codex":0.0049294946,"about_ca_system_score_gemma":0.0060815704,"threshold_uncertainty_score":0.12569392},"labels":[],"label_agreement":null},{"id":"W6963934780","doi":"10.22126/atwe.2024.9839.1066","title":"Intermittent water optimized supply considering consumption pattern change in water distribution network","year":2023,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Water supply; Population; Consumption (sociology); Non-revenue water; Pressure drop; Drop (telecommunication); Energy consumption","score_opus":0.2102767523172671,"score_gpt":0.4568728876815997,"score_spread":0.2465961353643326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6963934780","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":[0.7244609,0.00061641267,0.2586825,0.00054174266,0.000064011525,0.00020486579,0.0005829875,0.00032001277,0.014526629],"genre_scores_gemma":[0.9871838,0.00009808221,0.010528281,0.000020272553,0.000006078652,0.00005358295,0.00014392409,0.000015604355,0.0019503476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.999821,0.000041109568,0.000006907071,0.000036637673,0.000038116304,0.000056185723],"domain_scores_gemma":[0.9997253,0.00012635793,0.000047859816,0.000009276961,0.00007118155,0.000019971556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003206796,0.0005620782,0.0004919129,0.0005252373,0.0003260305,0.0006854525,0.0005210362,0.0004447533,0.002499947],"category_scores_gemma":[0.00079195853,0.00024155361,0.00034456418,0.00062333903,0.00025562156,0.0005859927,0.0003620105,0.00032358698,0.00009261134],"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":[0.000048872807,0.000037412305,0.0018173005,0.00005904571,0.000017558657,0.000057262798,0.000024219462,0.98754996,0.0010612676,0.00069567474,0.00033320335,0.00829815],"study_design_scores_gemma":[0.000007377828,0.000057634505,0.0011795444,0.000004580268,0.000011045232,0.000013518404,0.000048397185,0.99738723,0.00038989945,0.00055776397,0.00033981312,0.0000031096627],"about_ca_topic_score_codex":0.014303001,"about_ca_topic_score_gemma":0.012360007,"teacher_disagreement_score":0.014303001,"about_ca_system_score_codex":0.0010849992,"about_ca_system_score_gemma":0.000766605,"threshold_uncertainty_score":0.028439522},"labels":[],"label_agreement":null},{"id":"W6985017736","doi":"","title":"第28届国际心理学大会总结","year":2004,"lang":"zh","type":"other","venue":"Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences)","topic":"Water Systems and 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":"Process (computing); Identification (biology); Product (mathematics)","score_opus":0.029995883858110223,"score_gpt":0.33654369600465117,"score_spread":0.3065478121465409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6985017736","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":[0.008111484,0.005297874,0.0049025672,0.03171058,0.0011494934,0.00009510429,0.0005813864,0.00013108017,0.9480204],"genre_scores_gemma":[0.17377004,0.00902906,0.009901985,0.0050105643,0.0006723611,0.00022114202,0.0007935534,0.00020915594,0.8003921],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980653,0.0003998856,0.00006222284,0.00037856383,0.00083348807,0.00026063772],"domain_scores_gemma":[0.99893683,0.00017308985,0.0000691312,0.00006155972,0.0005426731,0.00021673218],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017738584,0.0005624471,0.00028826488,0.001293299,0.004591008,0.00934065,0.0010258831,0.0021681315,0.050153963],"category_scores_gemma":[0.0028050002,0.0003362582,0.0003705934,0.0020528857,0.0048568714,0.0046028686,0.0015754813,0.0023473674,0.010400483],"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":[0.00006670027,0.000070692215,0.0025699334,0.00017169012,0.000014543601,0.00026687805,0.0051722513,0.0007961924,0.00063400704,0.53533065,0.300762,0.15414445],"study_design_scores_gemma":[0.000008671552,0.000017054363,0.0023818803,0.00013649977,0.000009719701,0.000068496,0.0022626426,0.00027044537,0.00053179,0.02666044,0.96763504,0.000017349565],"about_ca_topic_score_codex":0.17238551,"about_ca_topic_score_gemma":0.16494213,"teacher_disagreement_score":0.949846,"about_ca_system_score_codex":0.017386764,"about_ca_system_score_gemma":0.026089944,"threshold_uncertainty_score":0.34276426},"labels":[],"label_agreement":null},{"id":"W6986904873","doi":"","title":"Resilience-Driven Management of Water Distribution Networks","year":2020,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Water Systems and 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":"Robustness (evolution); Criticality; Dependability; Reliability (semiconductor); Resilience (materials science); Vulnerability (computing); Fuzzy logic; Metric (unit); Fuzzy set; Cascading failure","score_opus":0.012030168195097809,"score_gpt":0.22129736963664406,"score_spread":0.20926720144154626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986904873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12450454,0.0005897609,0.8584544,0.0010148006,0.00008657988,0.00014173507,0.000285656,0.0004947437,0.014427841],"genre_scores_gemma":[0.9829806,0.00028416287,0.014439509,0.000037869777,0.000016303671,0.000064582964,0.00007026169,0.000021986789,0.0020847477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966156,0.00007945285,0.000014760706,0.00008470345,0.00007871615,0.000080731],"domain_scores_gemma":[0.9995402,0.00016806855,0.000099911114,0.000028905262,0.000096492215,0.000066303386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006387007,0.0006386102,0.0004138361,0.00056710676,0.0005850702,0.0011027561,0.0008849065,0.000571539,0.0015173842],"category_scores_gemma":[0.0016475196,0.00022987551,0.00031738228,0.0004143558,0.0006848955,0.0013527637,0.0015889208,0.0005867019,0.00013521216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015357504,0.000008279199,0.00024864404,0.00001903454,0.000008454891,0.0000531419,0.00003342591,0.97989523,0.001236711,0.010895731,0.00031809093,0.0072678295],"study_design_scores_gemma":[0.0000022107206,0.000013938406,0.00010251561,0.000004511104,0.000003404696,0.0000086163,0.000027398426,0.9916003,0.0002827288,0.0073966165,0.00055406283,0.0000036138392],"about_ca_topic_score_codex":0.0051395968,"about_ca_topic_score_gemma":0.003617144,"teacher_disagreement_score":0.0051395968,"about_ca_system_score_codex":0.0015627198,"about_ca_system_score_gemma":0.0011313321,"threshold_uncertainty_score":0.011338353},"labels":[],"label_agreement":null},{"id":"W6987376913","doi":"","title":"Static and dynamic effects in prioritizing individual water mains for renewal","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":true,"ca_institutions":"National Research Council Canada","funders":"American Water Works Association Research Foundation","keywords":"Mains electricity; Interim; Electricity; Foundation (evidence); System dynamics; Water supply","score_opus":0.0051493435787568305,"score_gpt":0.20664829840602225,"score_spread":0.20149895482726543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987376913","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":[0.8258288,0.001231685,0.12754376,0.0016781448,0.0001860849,0.00016340059,0.0005645173,0.00021188424,0.042591635],"genre_scores_gemma":[0.9942162,0.00014199676,0.0028520634,0.000039482286,0.000019182546,0.000009888521,0.000047885325,0.00001917192,0.0026541078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989737,0.00032172125,0.00002269037,0.000102844664,0.00023796376,0.000340988],"domain_scores_gemma":[0.9959299,0.002823751,0.00029889162,0.0001067792,0.00051429943,0.00032635103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024013333,0.0005983989,0.000744013,0.0010087385,0.0006733522,0.001482901,0.0010001194,0.0011554599,0.0062466734],"category_scores_gemma":[0.0073861824,0.00069385604,0.000626196,0.0009598754,0.0011258272,0.0025789973,0.001014442,0.000781785,0.00030591362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024156913,0.00008527591,0.0046545644,0.000053449567,0.00004036286,0.00013018845,0.000037547197,0.97381115,0.0023998783,0.0057805968,0.0005261744,0.012239268],"study_design_scores_gemma":[0.000044485994,0.00049009157,0.012409573,0.000018996083,0.00011016356,0.00008249917,0.00023782985,0.9755392,0.0018863656,0.008127927,0.0010102483,0.000042643915],"about_ca_topic_score_codex":0.017552067,"about_ca_topic_score_gemma":0.036618337,"teacher_disagreement_score":0.017552067,"about_ca_system_score_codex":0.002279053,"about_ca_system_score_gemma":0.0013819094,"threshold_uncertainty_score":0.03489983},"labels":[],"label_agreement":null},{"id":"W6989685480","doi":"","title":"CCMC evaluates auger-installed steel pipes","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Piping; Welding; Mode (computer interface); Work (physics)","score_opus":0.01048074091469292,"score_gpt":0.19472727494000502,"score_spread":0.1842465340253121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6989685480","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":[0.95711386,0.0003041811,0.017759394,0.00021082391,0.00006434661,0.00019472177,0.005557425,0.0016972981,0.017097965],"genre_scores_gemma":[0.97850513,0.00011443614,0.0123076625,0.00004651563,0.000014299218,0.0000986934,0.0034825064,0.00020194372,0.0052287984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979601,0.00066552014,0.000077929304,0.00031278958,0.0007813414,0.00020231993],"domain_scores_gemma":[0.9934022,0.0024754251,0.00030449167,0.0008036709,0.0027999955,0.0002142113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003205159,0.0012985127,0.0012670048,0.0023709328,0.00088275777,0.0011426476,0.0011615974,0.0019120816,0.0047337464],"category_scores_gemma":[0.0068194964,0.00047379677,0.0012276811,0.002395102,0.000696686,0.001113201,0.0006759662,0.00087329483,0.00104977],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011124865,0.00047089966,0.020274846,0.00031728836,0.00015977549,0.00016327255,0.000051110386,0.91252136,0.010191086,0.0014108531,0.0042498424,0.049077287],"study_design_scores_gemma":[0.00018603724,0.0015427789,0.031653035,0.0000279732,0.00010797148,0.00007484167,0.00012425307,0.92986584,0.031875316,0.00046601865,0.0040120347,0.00006393434],"about_ca_topic_score_codex":0.14025,"about_ca_topic_score_gemma":0.17003082,"teacher_disagreement_score":0.14025,"about_ca_system_score_codex":0.003589021,"about_ca_system_score_gemma":0.0031456829,"threshold_uncertainty_score":0.2788673},"labels":[],"label_agreement":null},{"id":"W6990114730","doi":"","title":"Consideration of static and dynamic effects to plan water main renewal","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"American Water Works Association Research Foundation","keywords":"Mains electricity; Breakage; Water pipe; Plan (archaeology); Failure mechanism; Water pressure; Lead (geology)","score_opus":0.003701582506487678,"score_gpt":0.18603466225528273,"score_spread":0.18233307974879506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990114730","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13487555,0.0006790102,0.83615285,0.00053860777,0.00011716862,0.00012269862,0.0003630849,0.0003820729,0.026768882],"genre_scores_gemma":[0.9666553,0.00023423784,0.02847583,0.000051539453,0.00003767418,0.00006052497,0.00011845942,0.000092876,0.00427359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981636,0.000045573244,0.0000068407508,0.00003143188,0.000057854766,0.00004204759],"domain_scores_gemma":[0.9996221,0.00020594525,0.000033568766,0.000017678094,0.00008366543,0.000036935468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004710399,0.0005868761,0.00082416914,0.0004999292,0.0003437734,0.00068854407,0.00058308116,0.0005437435,0.0051721623],"category_scores_gemma":[0.001621651,0.00042372942,0.0004532905,0.00036788755,0.0003558492,0.00093584356,0.0005517556,0.0004450682,0.0002605931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016842214,0.0000118035205,0.00048770252,0.000031155425,0.0000116021865,0.000030254756,0.000009424589,0.98544145,0.0013217137,0.002676094,0.0002979296,0.009664098],"study_design_scores_gemma":[0.0000023857383,0.00002634032,0.00049666216,0.000003681551,0.000009922164,0.000007720031,0.000013026093,0.9972907,0.00033784556,0.0012926714,0.00051499676,0.0000040147856],"about_ca_topic_score_codex":0.006965207,"about_ca_topic_score_gemma":0.00923495,"teacher_disagreement_score":0.006965207,"about_ca_system_score_codex":0.0006398353,"about_ca_system_score_gemma":0.0010926016,"threshold_uncertainty_score":0.017302632},"labels":[],"label_agreement":null},{"id":"W6990394631","doi":"","title":"Developing a Framework for the Reliability Analysis of Water Distribution Systems","year":2019,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Water Systems and 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":"Queen's University","funders":"Mitacs; Queen's University","keywords":"Reliability (semiconductor); Probabilistic logic; Estimator; Measure (data warehouse); Solver; Population; Reliability theory; Bayesian network","score_opus":0.006003312449837764,"score_gpt":0.18180895471001451,"score_spread":0.17580564226017675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990394631","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":[0.00073553814,0.0004310503,0.9956,0.00020436215,0.000033068136,0.00004211777,0.00008347443,0.00008636155,0.002784103],"genre_scores_gemma":[0.15041001,0.003200792,0.8382614,0.00016981697,0.00032193543,0.000968573,0.0004220724,0.00020491378,0.0060404623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983815,0.00077895995,0.00007898555,0.00019478875,0.00046271083,0.00010302167],"domain_scores_gemma":[0.99827254,0.0010869362,0.00012890139,0.00011559241,0.000346183,0.000049815346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032743986,0.001708405,0.00097299664,0.0016729779,0.00074204203,0.0022307595,0.002530425,0.001693014,0.0043571517],"category_scores_gemma":[0.0056133945,0.0007863891,0.0019722127,0.0014219818,0.0019794672,0.0019827706,0.0016643474,0.002809501,0.0010842392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000544719,0.000017227601,0.000188415,0.00013056933,0.000025902467,0.000090688714,0.00011756379,0.5264387,0.0006354012,0.46017647,0.0013319863,0.01084154],"study_design_scores_gemma":[0.000007662131,0.000018346733,0.00008414302,0.00006830597,0.000010929194,0.000040170406,0.00003843409,0.81732535,0.00021683074,0.16910875,0.013068445,0.000012645749],"about_ca_topic_score_codex":0.009445565,"about_ca_topic_score_gemma":0.0056491275,"teacher_disagreement_score":0.009445565,"about_ca_system_score_codex":0.0021124824,"about_ca_system_score_gemma":0.0025483477,"threshold_uncertainty_score":0.018781185},"labels":[],"label_agreement":null},{"id":"W6991068403","doi":"","title":"Evaluating integrity of distribution infrastructure based on potential water quality changes","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; American Water Works Association Research Foundation","keywords":"Simple (philosophy); Water quality; Distribution (mathematics); Quality (philosophy); Proof of concept","score_opus":0.024033321389248376,"score_gpt":0.28657956540493484,"score_spread":0.2625462440156865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991068403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3583504,0.0004932923,0.6248972,0.00072120724,0.00004975163,0.00018128027,0.00052102003,0.0004745557,0.014311373],"genre_scores_gemma":[0.9778039,0.00011559401,0.021355955,0.00002091769,0.000008054311,0.000023214288,0.00014870675,0.000021180558,0.0005025112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9947883,0.002024733,0.00020164036,0.00050343625,0.0021097234,0.000372255],"domain_scores_gemma":[0.9935162,0.0034744083,0.0012191326,0.00073760096,0.00083201943,0.00022058538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004452673,0.00079181325,0.0007932479,0.0019981784,0.00040009967,0.0024587987,0.0011482938,0.00096978294,0.0024194806],"category_scores_gemma":[0.0141208265,0.00030139118,0.0006724038,0.0015456473,0.0012417199,0.005016112,0.0015870127,0.0007662417,0.00019866666],"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":[0.00021165423,0.000090767215,0.015523598,0.00011522365,0.00014888852,0.00014276922,0.00009790942,0.9076299,0.0045213723,0.026626097,0.0003903676,0.044501517],"study_design_scores_gemma":[0.000013011906,0.00020657941,0.004320673,0.000016765252,0.00007304425,0.000050011484,0.00016092732,0.97124994,0.005320208,0.01770096,0.000857261,0.000030549083],"about_ca_topic_score_codex":0.0063682934,"about_ca_topic_score_gemma":0.0070433896,"teacher_disagreement_score":0.0063682934,"about_ca_system_score_codex":0.002610598,"about_ca_system_score_gemma":0.0013313922,"threshold_uncertainty_score":0.023548305},"labels":[],"label_agreement":null},{"id":"W6991814855","doi":"","title":"Identifying research-needs related to impacts of water quality on the integrity of distribution infrastructure","year":2006,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Research Council Canada; American Water Works Association Research Foundation","keywords":"Water quality; Distribution (mathematics); General partnership; Quality (philosophy); Water resources; Foundation (evidence)","score_opus":0.02987423752174064,"score_gpt":0.2941029435088716,"score_spread":0.264228705987131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991814855","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":[0.22856872,0.22013158,0.12666658,0.2932685,0.0013902193,0.0019889385,0.002455823,0.00044830164,0.12508127],"genre_scores_gemma":[0.6825681,0.23688774,0.06264571,0.0063294256,0.0015319226,0.00069032575,0.0013530883,0.00007690106,0.007916802],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938046,0.0031375086,0.00048819504,0.00054463313,0.0013720256,0.0006530161],"domain_scores_gemma":[0.9416188,0.041768927,0.0027845027,0.0010228569,0.011644389,0.0011606427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011414663,0.00065923535,0.0018682745,0.0031992092,0.0014147587,0.004781758,0.0017002076,0.0037323458,0.007655478],"category_scores_gemma":[0.03201443,0.0005017445,0.00077773276,0.0030226505,0.0020749972,0.007339598,0.0018797562,0.0012891828,0.0011802174],"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":[0.0006246395,0.0011629596,0.019119635,0.017834267,0.00013693953,0.0011994594,0.0034636387,0.023352152,0.009032378,0.03619732,0.018973526,0.8689031],"study_design_scores_gemma":[0.00022667412,0.0042155357,0.05541115,0.017679263,0.00063552783,0.001973123,0.12962042,0.079741076,0.028250331,0.39278844,0.28894243,0.00051605626],"about_ca_topic_score_codex":0.008681934,"about_ca_topic_score_gemma":0.011550144,"teacher_disagreement_score":0.011414663,"about_ca_system_score_codex":0.0026433042,"about_ca_system_score_gemma":0.00868262,"threshold_uncertainty_score":0.060367227},"labels":[],"label_agreement":null},{"id":"W6993040356","doi":"","title":"A Novel modelling approach to predict risk of water quality failures in deteriorating water mains","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Mains electricity; Water quality; Prioritization; Process (computing); Water pipe; Quality (philosophy)","score_opus":0.02160306840661039,"score_gpt":0.21821316958210707,"score_spread":0.19661010117549668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6993040356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06786684,0.00029004196,0.9267347,0.00038983652,0.00008247807,0.00007571255,0.00040283508,0.00045340846,0.0037041442],"genre_scores_gemma":[0.9230136,0.0005224382,0.0690701,0.00007487033,0.000109666704,0.00021964099,0.00039255596,0.00008178234,0.006515284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997067,0.0000713651,0.000019251653,0.000079761034,0.00006680663,0.00005605214],"domain_scores_gemma":[0.99908257,0.00053054997,0.00012784821,0.000043055275,0.00015939094,0.000056727968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088993285,0.0009405406,0.0013059599,0.0007341411,0.0004648578,0.0013182221,0.0017654181,0.0018127088,0.002128023],"category_scores_gemma":[0.002349052,0.00065619336,0.0012581887,0.0007109549,0.0005283129,0.0011297195,0.0010065952,0.0012568071,0.00029870393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001342164,0.00001375773,0.00030319646,0.0000096886015,0.00001700255,0.000020378673,0.000010234491,0.99537116,0.00028470994,0.0012959323,0.000119333956,0.0025411858],"study_design_scores_gemma":[0.0000016762559,0.0000074492127,0.00004247104,7.9727727e-7,0.0000036645843,0.000004013283,0.0000013623173,0.9992741,0.000041397878,0.0005484501,0.00007283898,0.000001784195],"about_ca_topic_score_codex":0.01873905,"about_ca_topic_score_gemma":0.010521727,"teacher_disagreement_score":0.01873905,"about_ca_system_score_codex":0.0009151694,"about_ca_system_score_gemma":0.0015571208,"threshold_uncertainty_score":0.037259936},"labels":[],"label_agreement":null},{"id":"W6996161940","doi":"","title":"Report on water supply for the city of Ottawa.","year":2014,"lang":"en","type":"article","venue":"QSpace (Queen's University Library)","topic":"Water Systems and 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":"Water supply; Production (economics); Work (physics); Water industry","score_opus":0.004999913498289769,"score_gpt":0.15427657006004059,"score_spread":0.14927665656175082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996161940","genre_codex":"dataset","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":[0.037192393,0.008645499,0.0012149271,0.014720274,0.0030474048,0.0005560385,0.7586029,0.00068555394,0.17533502],"genre_scores_gemma":[0.12454307,0.010206734,0.002581613,0.0013870437,0.00031487877,0.0004199334,0.17482841,0.00024867375,0.6854697],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998938,0.000050277,0.00006498947,0.00007597271,0.00060919335,0.00026154044],"domain_scores_gemma":[0.9976301,0.00010419776,0.00008167141,0.000066254994,0.0018293493,0.00028840356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051429414,0.0008173894,0.00041364483,0.002661582,0.0015988613,0.0017318183,0.001214848,0.00089094,0.041221958],"category_scores_gemma":[0.0015960983,0.00050253764,0.0005411044,0.0057208897,0.00030771844,0.0008522929,0.0010745499,0.0010298819,0.005961631],"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":[0.00039306935,0.000063601125,0.022922253,0.0011274279,0.00011694356,0.00036963396,0.0006371787,0.0019612275,0.0011983886,0.0023080881,0.93330926,0.035592865],"study_design_scores_gemma":[0.000033637898,0.000074353935,0.12998606,0.00018814999,0.00009801606,0.000069918235,0.0027840997,0.00070442323,0.0015118306,0.00026769904,0.86419237,0.000089501016],"about_ca_topic_score_codex":0.9750656,"about_ca_topic_score_gemma":0.9879561,"teacher_disagreement_score":0.041221958,"about_ca_system_score_codex":0.016047802,"about_ca_system_score_gemma":0.04199289,"threshold_uncertainty_score":0.13790113},"labels":[],"label_agreement":null},{"id":"W6997462248","doi":"","title":"Water mains: the importance of inventory and failure data","year":2008,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Data collection; Work (physics); Information system; Column (typography)","score_opus":0.021746473672996158,"score_gpt":0.19279129348162458,"score_spread":0.17104481980862843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997462248","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":[0.36928225,0.029801965,0.26917392,0.040774275,0.0012807767,0.0002611804,0.20278093,0.0030739023,0.08357092],"genre_scores_gemma":[0.8463874,0.013405929,0.07255532,0.0013116117,0.0006438664,0.00010428438,0.048870888,0.001022909,0.015697913],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99580353,0.0010673252,0.0004834793,0.0008162402,0.0016062119,0.00022318927],"domain_scores_gemma":[0.9248782,0.042892475,0.0062968372,0.010277584,0.014733434,0.00092142116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01188861,0.00083864876,0.00080950826,0.0054360516,0.0007937196,0.006041735,0.0028586208,0.0015325163,0.00569592],"category_scores_gemma":[0.058219105,0.00077587843,0.0006992648,0.01081578,0.00160367,0.010241417,0.0013195787,0.0022063619,0.0014002662],"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":[0.0004628159,0.00011382295,0.20564382,0.001426009,0.0005251528,0.00028279956,0.0012844612,0.11713765,0.0014273723,0.06153877,0.0531139,0.55704343],"study_design_scores_gemma":[0.00009588496,0.00019879862,0.20720676,0.0024311517,0.00041031433,0.00071118685,0.0050728344,0.26621905,0.008759657,0.09711996,0.41130975,0.00046471876],"about_ca_topic_score_codex":0.23387975,"about_ca_topic_score_gemma":0.24687721,"teacher_disagreement_score":0.23387975,"about_ca_system_score_codex":0.0034186468,"about_ca_system_score_gemma":0.0045401747,"threshold_uncertainty_score":0.4650368},"labels":[],"label_agreement":null},{"id":"W6999626844","doi":"","title":"3D hydraulic model studies of pipeline secondary stabilization measures","year":2012,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Subsea; Berm; Pipeline transport; Pipeline (software); Reliability (semiconductor); Underwater","score_opus":0.035851219791036205,"score_gpt":0.23972444209451335,"score_spread":0.20387322230347715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6999626844","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":[0.8450956,0.0001973853,0.1304575,0.00024301789,0.000040657782,0.00013112558,0.0029141977,0.0011792324,0.019741323],"genre_scores_gemma":[0.9922884,0.00011224214,0.0052802456,0.000012952935,0.000004084534,0.000071258335,0.00034482425,0.00003734248,0.0018485853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997154,0.00005395724,0.000020102214,0.000042761018,0.00011244968,0.000055207147],"domain_scores_gemma":[0.99960726,0.00014827067,0.000059828435,0.000063163105,0.00009605667,0.000025451272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024369432,0.0004985328,0.00065956055,0.000679137,0.00037106161,0.00075417024,0.00058179215,0.0011921858,0.0028431625],"category_scores_gemma":[0.0009131347,0.00043333502,0.00063908234,0.00067407807,0.0006263541,0.0005174525,0.00049318315,0.00042449858,0.00038457883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025711044,0.000014708353,0.0005658572,0.000017182332,0.0000044583626,0.0000447982,0.000035874666,0.99435246,0.0026642063,0.0005171454,0.000116651805,0.0016408903],"study_design_scores_gemma":[0.000010472471,0.000040147726,0.0007515659,0.0000033480744,0.000003912325,0.000012705972,0.000023594901,0.9966156,0.0019510997,0.00019226027,0.00038403034,0.000011206012],"about_ca_topic_score_codex":0.015208277,"about_ca_topic_score_gemma":0.009846276,"teacher_disagreement_score":0.015208277,"about_ca_system_score_codex":0.00080893456,"about_ca_system_score_gemma":0.0010915014,"threshold_uncertainty_score":0.030239522},"labels":[],"label_agreement":null},{"id":"W7001945392","doi":"","title":"Maintaining water pipeline integrity","year":2000,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Pipeline transport; Leak detection; Pipeline (software); Leak; Corrosion; Condition monitoring; Key (lock); Prestressed concrete; Field (mathematics)","score_opus":0.009047541173069641,"score_gpt":0.19308534507780417,"score_spread":0.18403780390473454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001945392","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":[0.0995144,0.0032974565,0.76161766,0.0069736387,0.0008279245,0.00048729134,0.001979422,0.012179426,0.11312274],"genre_scores_gemma":[0.77847844,0.0028818166,0.16732442,0.0009040341,0.00025494694,0.00018387688,0.0016282056,0.0014470397,0.046897225],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967134,0.00036023915,0.00021304113,0.00058893155,0.0017339302,0.00039048886],"domain_scores_gemma":[0.9910499,0.0009690975,0.00085817743,0.0026193422,0.003997002,0.0005065191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028430328,0.00076781283,0.00067751284,0.0021040656,0.0018001306,0.0036041117,0.0018113538,0.0015095199,0.010803284],"category_scores_gemma":[0.010409994,0.00046104824,0.00043427743,0.0017116861,0.0013557961,0.006587681,0.0041035833,0.0012539469,0.0055473777],"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":[0.0002260313,0.0001077242,0.012173995,0.0009428388,0.0000681948,0.0006738338,0.0019712846,0.012857266,0.11187897,0.031847417,0.02258614,0.8046663],"study_design_scores_gemma":[0.00006444578,0.00065723166,0.022809558,0.00059171073,0.0002377665,0.0031218084,0.0029441402,0.055485044,0.19169931,0.036863655,0.68528175,0.00024357912],"about_ca_topic_score_codex":0.0037609462,"about_ca_topic_score_gemma":0.004602469,"teacher_disagreement_score":0.010803284,"about_ca_system_score_codex":0.0014540685,"about_ca_system_score_gemma":0.0031652509,"threshold_uncertainty_score":0.03614056},"labels":[],"label_agreement":null},{"id":"W7002022908","doi":"","title":"Modeling of acoustic wave propagation in turbulent flow","year":2005,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Water Systems and 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":"Orthopaedic Innovation Centre","funders":"","keywords":"Turbulence; Turbulence modeling; Boundary layer; K-epsilon turbulence model; Drag; K-omega turbulence model; Mean flow; Reynolds number; Pipe flow","score_opus":0.010681715331644177,"score_gpt":0.16925025271255328,"score_spread":0.15856853738090912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7002022908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075190544,0.00038317716,0.9139955,0.00013638011,0.000052869847,0.00004342937,0.000105051644,0.000466273,0.00962687],"genre_scores_gemma":[0.89031297,0.0011063435,0.08837503,0.00004304335,0.000045716664,0.0001238243,0.00020763424,0.000113468595,0.019671934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999151,0.000016687449,0.000004626448,0.000013438366,0.000038203783,0.000011854839],"domain_scores_gemma":[0.9999242,0.000031887947,0.000011793073,0.000005414179,0.000020934993,0.0000057642546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001507523,0.00042489392,0.0002930304,0.00027191703,0.00022763555,0.00068273384,0.00039927397,0.00041446823,0.0008264943],"category_scores_gemma":[0.00036010033,0.0002422303,0.00033396803,0.0002633209,0.0003582778,0.00058766454,0.00029151092,0.00031038257,0.0003401455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023603216,0.000022686481,0.00042255787,0.000042131047,0.0000067888113,0.000046925827,0.000055499546,0.9600188,0.0121369185,0.0121659245,0.00033150555,0.01472664],"study_design_scores_gemma":[0.0000021303401,0.000006160747,0.00007031061,0.0000020730597,0.0000012690258,0.000005399863,0.000004621514,0.99799937,0.00083506,0.00064484763,0.0004264142,0.000002336969],"about_ca_topic_score_codex":0.004065801,"about_ca_topic_score_gemma":0.0022130178,"teacher_disagreement_score":0.004065801,"about_ca_system_score_codex":0.00039145246,"about_ca_system_score_gemma":0.00064278964,"threshold_uncertainty_score":0.008084297},"labels":[],"label_agreement":null},{"id":"W7002320176","doi":"","title":"New research project identifies key areas of research needed for drinking water infrastructure","year":2006,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Key (lock); Water supply; Potable water; Water resources; Government (linguistics)","score_opus":0.03790548705563525,"score_gpt":0.30830247216210155,"score_spread":0.2703969851064663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7002320176","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":[0.35630843,0.011020771,0.3107829,0.0811796,0.0013093222,0.0026450094,0.008547164,0.0019655572,0.22624122],"genre_scores_gemma":[0.65386254,0.009570864,0.24288467,0.0030030252,0.0002872941,0.0012692516,0.008738614,0.0003640752,0.08001964],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99625516,0.0013523913,0.00016191862,0.00051843567,0.0010305321,0.0006816314],"domain_scores_gemma":[0.9856588,0.0036987625,0.00086341694,0.0017119476,0.006404195,0.0016628463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008355336,0.0011943527,0.0010215649,0.0018359525,0.0019300852,0.004460816,0.0014939109,0.0017017663,0.014160159],"category_scores_gemma":[0.00833963,0.00040351733,0.0012656166,0.0024496827,0.0013077048,0.0043327073,0.0021000598,0.0018369228,0.0026255434],"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":[0.0014538945,0.0018012591,0.042356703,0.0021855724,0.00029096587,0.00069149915,0.0027251714,0.02130043,0.045310292,0.17493412,0.05167802,0.65527207],"study_design_scores_gemma":[0.00052957016,0.0033999665,0.059855137,0.0011994053,0.0005504905,0.0008397945,0.015367197,0.060600642,0.07688429,0.067126594,0.71338683,0.00025999956],"about_ca_topic_score_codex":0.06280676,"about_ca_topic_score_gemma":0.09049577,"teacher_disagreement_score":0.06280676,"about_ca_system_score_codex":0.0054429653,"about_ca_system_score_gemma":0.027048433,"threshold_uncertainty_score":0.12488234},"labels":[],"label_agreement":null},{"id":"W7006746107","doi":"","title":"Water quality failure in distribution networks: a framework for an aggregative risk analysis","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Water quality; Weighting; Fuzzy logic; Quality (philosophy); Vagueness; Risk management; Risk assessment; Failure mode and effects analysis","score_opus":0.012704705068058578,"score_gpt":0.24570469568331316,"score_spread":0.2329999906152546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006746107","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":[0.007305431,0.0004435779,0.98797774,0.00046721013,0.000021881717,0.00005350329,0.00009510815,0.000053983273,0.003581461],"genre_scores_gemma":[0.618161,0.002131484,0.3743629,0.00012607343,0.00022764118,0.00055476115,0.00023179123,0.00006343876,0.004140806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970523,0.0014555034,0.00014316368,0.000288544,0.0008484931,0.00021195824],"domain_scores_gemma":[0.9970893,0.0018132307,0.0003889759,0.0001661599,0.00041497743,0.00012739058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00480552,0.0016222155,0.0010679023,0.002634603,0.000927017,0.0032238555,0.0015734462,0.0011727096,0.0017084292],"category_scores_gemma":[0.0067979214,0.00048283683,0.0012505709,0.0020327293,0.0024483383,0.0033468427,0.0018505659,0.0017310483,0.00020025924],"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":[0.000009224097,0.000022577487,0.00070328236,0.000066164546,0.00007148515,0.00012568176,0.0002217014,0.57984823,0.0002843487,0.4077577,0.0007614012,0.010128213],"study_design_scores_gemma":[0.000006233567,0.000020220772,0.00028449052,0.000027913235,0.000023427285,0.00003317043,0.00009903625,0.7607632,0.00007613335,0.2369108,0.0017378586,0.000017525372],"about_ca_topic_score_codex":0.009469034,"about_ca_topic_score_gemma":0.005575921,"teacher_disagreement_score":0.009469034,"about_ca_system_score_codex":0.002909961,"about_ca_system_score_gemma":0.0016682618,"threshold_uncertainty_score":0.025414348},"labels":[],"label_agreement":null},{"id":"W7010566834","doi":"","title":"Innovative tool to model remaining service life of water pipes developed at IRC","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Water pipe; Service life; Service (business); Point (geometry); Work (physics)","score_opus":0.01963566625183033,"score_gpt":0.20844103063747751,"score_spread":0.18880536438564718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7010566834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059966292,0.0001926212,0.9227501,0.00018626165,0.00017538728,0.00016365394,0.0021068063,0.003842374,0.010616412],"genre_scores_gemma":[0.6978079,0.0004024487,0.2825698,0.00014017227,0.00007790921,0.001037818,0.0025526027,0.0009587617,0.014452442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997862,0.00004446189,0.000011487427,0.000048213748,0.00007251248,0.00003722065],"domain_scores_gemma":[0.9995473,0.00018987761,0.00003808727,0.00006282698,0.00013673202,0.000025004434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059240044,0.001208617,0.0010559296,0.00082551595,0.00068463315,0.001106876,0.0020603107,0.0023433352,0.0053295074],"category_scores_gemma":[0.0014260223,0.00070855505,0.0016468145,0.0009912683,0.0004378383,0.00096807274,0.000600131,0.0014688623,0.0008646177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012004059,0.000025276295,0.00034209038,0.000028410694,0.0000143182,0.000031486696,0.000018808914,0.991819,0.0008704452,0.0022113498,0.00048366233,0.0041431547],"study_design_scores_gemma":[0.0000051967118,0.000007740168,0.00006225819,0.0000024241162,0.0000038188714,0.0000064047626,0.0000026962548,0.9980248,0.00049962586,0.00039303603,0.0009883177,0.0000037264745],"about_ca_topic_score_codex":0.027627645,"about_ca_topic_score_gemma":0.018709384,"teacher_disagreement_score":0.027627645,"about_ca_system_score_codex":0.0012016704,"about_ca_system_score_gemma":0.0019418787,"threshold_uncertainty_score":0.054933667},"labels":[],"label_agreement":null},{"id":"W7011417278","doi":"","title":"Management of failure risk in large-diameter buried pipes using fuzzy-based techniques","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":true,"ca_institutions":"","funders":"American Water Works Association Research Foundation","keywords":"Fuzzy logic; Mains electricity; Risk management; Fuzzy set; Schedule; Failure mode and effects analysis; Risk assessment","score_opus":0.00701767221881087,"score_gpt":0.2034445182561015,"score_spread":0.19642684603729063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7011417278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02681681,0.00027493754,0.9708471,0.00011531445,0.00001264,0.00006225775,0.000018221468,0.00008557225,0.0017672977],"genre_scores_gemma":[0.7899092,0.00052237615,0.20795241,0.000037967176,0.000037656482,0.00008979338,0.000040419316,0.000014298924,0.0013958515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990947,0.00025208906,0.000060239407,0.00012798933,0.00037680703,0.00008812229],"domain_scores_gemma":[0.99791473,0.0012835258,0.00032932547,0.000070134025,0.0003249921,0.000077348544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002351986,0.0006610098,0.0009795331,0.0015951312,0.00070502213,0.0015568503,0.0015159445,0.0010197588,0.0010619608],"category_scores_gemma":[0.003667791,0.00044227368,0.0013633391,0.0008118848,0.0006660139,0.001373866,0.0010262727,0.000802246,0.00012559834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049670856,0.000052907795,0.00089225435,0.00005251649,0.00004315475,0.00012230729,0.00016676518,0.94705033,0.0025403542,0.0060065873,0.00016066781,0.042862553],"study_design_scores_gemma":[0.0000066693106,0.000036258356,0.00027546642,0.000013794549,0.000016904432,0.00002398207,0.00003294932,0.9944285,0.000455454,0.0044634584,0.00023523968,0.000011368576],"about_ca_topic_score_codex":0.010542682,"about_ca_topic_score_gemma":0.0077425535,"teacher_disagreement_score":0.010542682,"about_ca_system_score_codex":0.0016629447,"about_ca_system_score_gemma":0.0012101706,"threshold_uncertainty_score":0.020962656},"labels":[],"label_agreement":null},{"id":"W7015036450","doi":"","title":"Risk Based Decision Making Tools for Sewer Infrastructure Management","year":2010,"lang":"en","type":"article","venue":"OhioLink ETD Center (Ohio Library and Information Network)","topic":"Water Systems and 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":"Asset management; Asset (computer security); Risk management; Sanitary sewer; Failure mode, effects, and criticality analysis; Process (computing); Service (business); IT asset management","score_opus":0.0041694974232601096,"score_gpt":0.17952144241996476,"score_spread":0.17535194499670465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015036450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007863181,0.00042276236,0.97888935,0.0008896081,0.000084565334,0.00043045686,0.0005116311,0.001600616,0.009307783],"genre_scores_gemma":[0.10696481,0.0006406069,0.8886195,0.00014304905,0.00005457974,0.00069702085,0.00051384355,0.00012621914,0.0022403563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956917,0.0022064243,0.00042385852,0.0003654126,0.0011270011,0.00018563669],"domain_scores_gemma":[0.98136395,0.015116914,0.000988336,0.00041080447,0.0018017305,0.00031830318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009766973,0.002721732,0.0016551615,0.0045907116,0.0013351579,0.006036474,0.0027969703,0.0022760201,0.012181899],"category_scores_gemma":[0.019590164,0.0011910093,0.002306683,0.002473257,0.0011443289,0.003911991,0.0031124877,0.0033582905,0.0016797731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001428362,0.00025737452,0.0014064488,0.00043509706,0.00016725925,0.00038968257,0.0008267924,0.7833784,0.0011768142,0.08816216,0.005299175,0.11835795],"study_design_scores_gemma":[0.000040813098,0.000071482296,0.0002451499,0.0001792098,0.000041554413,0.000062481064,0.0003331246,0.9081099,0.0007866646,0.08167256,0.00841097,0.000046164765],"about_ca_topic_score_codex":0.0067175524,"about_ca_topic_score_gemma":0.0087602865,"teacher_disagreement_score":0.012181899,"about_ca_system_score_codex":0.0031600192,"about_ca_system_score_gemma":0.0032468722,"threshold_uncertainty_score":0.051653266},"labels":[],"label_agreement":null},{"id":"W7015255471","doi":"","title":"Schéma directeur d’alimentation en eau potable de la commune de Seyssel (Ain)","year":2020,"lang":"fr","type":"dissertation","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Water Systems and 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":"Economic shortage; Sugar industry; Pyrometallurgy; Nova scotia","score_opus":0.005409898251775051,"score_gpt":0.20600917270172647,"score_spread":0.20059927444995143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015255471","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":[0.24233791,0.0031170878,0.5913987,0.0012757609,0.00050858426,0.00156477,0.020252516,0.024429766,0.11511491],"genre_scores_gemma":[0.51973146,0.0032608854,0.36661568,0.0005746522,0.00005824974,0.0012452432,0.022551125,0.0029657192,0.08299693],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990722,0.0001608333,0.00007899331,0.00022453067,0.0003704767,0.00009290624],"domain_scores_gemma":[0.9991043,0.00030390057,0.000070790156,0.00015992505,0.00030859507,0.000052488762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088267366,0.0009347265,0.00057812175,0.00085906754,0.00061830523,0.0032501672,0.0010813825,0.001099055,0.019050999],"category_scores_gemma":[0.0015166728,0.00035477508,0.00120399,0.00087701983,0.0006850192,0.0019462558,0.0011620794,0.0008856294,0.00503885],"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":[0.0029469805,0.00054227,0.035753626,0.0045371205,0.00027298584,0.003775817,0.0051955082,0.18093663,0.23738928,0.07799136,0.039494757,0.41116363],"study_design_scores_gemma":[0.00020686824,0.0007750985,0.014545224,0.0006464131,0.0002021517,0.0015369002,0.002444815,0.13394369,0.17546393,0.013077128,0.6569218,0.00023599874],"about_ca_topic_score_codex":0.016831217,"about_ca_topic_score_gemma":0.015570134,"teacher_disagreement_score":0.019050999,"about_ca_system_score_codex":0.0012827929,"about_ca_system_score_gemma":0.0018437341,"threshold_uncertainty_score":0.06373191},"labels":[],"label_agreement":null},{"id":"W7016153495","doi":"","title":"Water main assets: from deterioration to renewal","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"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":"Mains electricity; Cathodic protection; Water storage; Moisture; Hotspot (geology); Water pipe","score_opus":0.0073154339276494855,"score_gpt":0.18049549797848644,"score_spread":0.17318006405083694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7016153495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45831117,0.00072376395,0.5123375,0.0007056982,0.0000602047,0.00018504428,0.0014890643,0.0025542406,0.02363323],"genre_scores_gemma":[0.9534121,0.00035311468,0.041044325,0.000027118158,0.000018332184,0.000054141976,0.00032066592,0.00009630942,0.0046739783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99987686,0.00003645795,0.000004603794,0.000035135443,0.000029820683,0.000017054139],"domain_scores_gemma":[0.99962556,0.00020776322,0.000063435546,0.000025870499,0.0000453254,0.000032162872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030889385,0.00043801471,0.00046002318,0.00024653258,0.00022969102,0.0006728554,0.0005240756,0.0005399897,0.0027037992],"category_scores_gemma":[0.0011633426,0.00028417312,0.00030308837,0.00042409997,0.0004537604,0.0009451425,0.00042694938,0.00063790154,0.00020642187],"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":[0.00010720617,0.000025640833,0.0032014935,0.00004694671,0.000008344365,0.000119907774,0.0000619914,0.95961094,0.0014110234,0.008436843,0.0009552937,0.026014395],"study_design_scores_gemma":[0.0000101260775,0.00003901935,0.00070617197,0.000006183976,0.000007197899,0.00003896517,0.000019702984,0.9901613,0.0014410242,0.005709839,0.0018551585,0.0000054248976],"about_ca_topic_score_codex":0.008577486,"about_ca_topic_score_gemma":0.007645697,"teacher_disagreement_score":0.008577486,"about_ca_system_score_codex":0.00073088665,"about_ca_system_score_gemma":0.0006302999,"threshold_uncertainty_score":0.017055154},"labels":[],"label_agreement":null},{"id":"W7017271219","doi":"","title":"Acoustic methods for locating leaks in municipal water pipe networks","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":49,"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":"Water pipe; Leak detection; Economic shortage; Pipe network analysis; Water scarcity; Pipeline transport; Water supply; Leak","score_opus":0.01495842358061455,"score_gpt":0.26539078930017246,"score_spread":0.2504323657195579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017271219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018338464,0.00033077807,0.97895765,0.0000756202,0.000023445644,0.00003117109,0.00004156385,0.000285195,0.0019161329],"genre_scores_gemma":[0.6733838,0.0010148904,0.31645873,0.00004666247,0.00010596074,0.00021757456,0.00012667457,0.00009217854,0.008553454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995987,0.00014350493,0.000014261124,0.00005493051,0.00015849578,0.00003013646],"domain_scores_gemma":[0.9988004,0.00075499486,0.0001607089,0.000053336844,0.00020742035,0.000023163422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057788513,0.0005712201,0.0004357973,0.0010607768,0.00031242453,0.00047923668,0.00054247055,0.00058870274,0.0015484308],"category_scores_gemma":[0.0033477575,0.00032038928,0.00026906453,0.0012360829,0.00053658546,0.0007488025,0.0004997775,0.00041024975,0.00031264205],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029022025,0.000059114234,0.001996675,0.00021764413,0.000041926825,0.000047963567,0.00017264207,0.60029244,0.016079642,0.008722898,0.0015190423,0.3705597],"study_design_scores_gemma":[0.000015929609,0.000043356293,0.0006625783,0.00000991088,0.00000821619,0.000024284844,0.000047518224,0.9922123,0.0020697627,0.0035586266,0.001331817,0.00001581439],"about_ca_topic_score_codex":0.0033883597,"about_ca_topic_score_gemma":0.004045675,"teacher_disagreement_score":0.0033883597,"about_ca_system_score_codex":0.00048284003,"about_ca_system_score_gemma":0.00054472237,"threshold_uncertainty_score":0.006737232},"labels":[],"label_agreement":null},{"id":"W7019441647","doi":"","title":"GIS-based decision support system for evaluating renewal technologies for municipal sewer and water pipelines","year":2008,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Trenchless technology; Decision support system; Pipeline transport; Pipeline (software); Sanitary sewer; Water pipe","score_opus":0.032214201838411054,"score_gpt":0.2604649528427275,"score_spread":0.22825075100431647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019441647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30394694,0.0002521503,0.65853447,0.00047458714,0.000062148065,0.00094937027,0.0059159417,0.022173446,0.007690956],"genre_scores_gemma":[0.7445176,0.00013201703,0.24997576,0.000056238372,0.00001926767,0.0005113008,0.0030403354,0.00009240608,0.00165509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99934155,0.0002347625,0.00008004411,0.00009283217,0.00021682742,0.00003398026],"domain_scores_gemma":[0.9978523,0.00093030406,0.00018522817,0.0001472734,0.00076339586,0.00012151012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014828932,0.0008871826,0.00091803574,0.0026002564,0.00043077552,0.0013247202,0.00091243285,0.00069728045,0.004185069],"category_scores_gemma":[0.0037221576,0.00035664116,0.0004597102,0.0013355451,0.00030071323,0.0012544302,0.00048264704,0.000358375,0.00086469844],"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":[0.0013604634,0.00047981038,0.01099109,0.00033467493,0.00019486167,0.00038647724,0.000239495,0.7194265,0.017968336,0.003410827,0.0074711046,0.23773631],"study_design_scores_gemma":[0.00008065577,0.00013690327,0.0013258217,0.000015307725,0.00004363889,0.000032061333,0.00007601444,0.98995763,0.0055350466,0.0009751465,0.0018020282,0.000019742358],"about_ca_topic_score_codex":0.007268432,"about_ca_topic_score_gemma":0.006517955,"teacher_disagreement_score":0.007268432,"about_ca_system_score_codex":0.0012492868,"about_ca_system_score_gemma":0.0009606782,"threshold_uncertainty_score":0.014452219},"labels":[],"label_agreement":null},{"id":"W7020253716","doi":"","title":"LeakFinder - new pipeline leak detection system","year":2001,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"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":"Leak detection; Software; Pipeline (software); Leak; Pipeline transport; Flexibility (engineering); Wireless; Transmission (telecommunications)","score_opus":0.00931457570708593,"score_gpt":0.17648414926126316,"score_spread":0.16716957355417722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7020253716","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":[0.05066282,0.0005988581,0.901466,0.00038875028,0.00023275099,0.00032335453,0.0013352204,0.03400278,0.010989405],"genre_scores_gemma":[0.5483353,0.0007744179,0.39142218,0.0005276012,0.00026510144,0.0005803511,0.0043047024,0.000654246,0.053136025],"study_design_codex":"design_other","study_design_gemma":"design_other","domain_scores_codex":[0.9994968,0.000038437865,0.000019300383,0.00012549953,0.00028758982,0.000032322212],"domain_scores_gemma":[0.99975353,0.000022544395,0.000035767647,0.00003327736,0.00013098205,0.000023954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036613274,0.000498205,0.00092168385,0.0009612115,0.00043827173,0.000497442,0.0009838106,0.0005752802,0.008141037],"category_scores_gemma":[0.0006399266,0.00038262244,0.00020233788,0.0005711815,0.0002330676,0.0013949396,0.00088537124,0.000606087,0.0028455576],"study_design_candidate":"design_other","study_design_consensus":"design_other","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009870732,0.00027440672,0.004865082,0.00058847707,0.00007586623,0.0003601941,0.00018762553,0.03144088,0.20121516,0.0060804016,0.0618103,0.69211453],"study_design_scores_gemma":[0.0005614146,0.0011152027,0.013015661,0.00006380548,0.00017399562,0.0014034549,0.000056607074,0.63865155,0.14845647,0.0028166224,0.19339587,0.00028931026],"about_ca_topic_score_codex":0.0014381649,"about_ca_topic_score_gemma":0.0016847462,"teacher_disagreement_score":0.008141037,"about_ca_system_score_codex":0.00057810906,"about_ca_system_score_gemma":0.00080729777,"threshold_uncertainty_score":0.027234495},"labels":[],"label_agreement":null},{"id":"W7023885624","doi":"","title":"Performance Measurement of Water Distribution Systems (WDS). A Critical and Constructive Appraisal of the State-of-the-art","year":2008,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Water Systems and 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":"Performance measurement; Water supply; Constructive; Vulnerability (computing); Performance appraisal; Performance indicator","score_opus":0.002823648251389658,"score_gpt":0.13508583006069882,"score_spread":0.13226218180930915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023885624","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":[0.2179437,0.025216477,0.5851319,0.0050882525,0.0007135621,0.000702676,0.003502168,0.0017753677,0.15992591],"genre_scores_gemma":[0.941605,0.007908325,0.039629474,0.00021708925,0.00015652122,0.00013400271,0.0010784712,0.000074435295,0.009196689],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996872,0.0006650177,0.00012868666,0.00023519222,0.0019687153,0.0001303829],"domain_scores_gemma":[0.9973303,0.00093389105,0.00034356164,0.00023166802,0.0010798894,0.000080684025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002336086,0.0006927553,0.00026735227,0.0017134184,0.00041567205,0.0017407062,0.0005318226,0.00058307976,0.0014221125],"category_scores_gemma":[0.0065109744,0.0001472063,0.00017653478,0.0017340503,0.0007081865,0.0020819958,0.0006746578,0.000449673,0.00051365653],"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":[0.00018624905,0.00026442297,0.034472726,0.0014094572,0.00013233851,0.00010618295,0.000754885,0.11965265,0.042100575,0.066082634,0.02216435,0.7126735],"study_design_scores_gemma":[0.000043725777,0.0020490284,0.12533483,0.0013582152,0.00018867463,0.0006333082,0.0033491016,0.32855025,0.17943643,0.06574185,0.29301026,0.0003043938],"about_ca_topic_score_codex":0.0063575427,"about_ca_topic_score_gemma":0.0052378825,"teacher_disagreement_score":0.0063575427,"about_ca_system_score_codex":0.0019277453,"about_ca_system_score_gemma":0.0014962466,"threshold_uncertainty_score":0.013986886},"labels":[],"label_agreement":null},{"id":"W7025620680","doi":"","title":"Women Dealing with Shame through Story, Dialogue &amp; Drama Techniques","year":2017,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Water Systems and 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 Windsor","funders":"","keywords":"Shame; Drama; Feeling; Narrative; Style (visual arts); Disconnection","score_opus":0.021903050651487004,"score_gpt":0.2055510000079324,"score_spread":0.18364794935644538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025620680","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":[0.3414965,0.015719373,0.08727953,0.05239441,0.0025687516,0.0010989967,0.0003636787,0.001283619,0.4977951],"genre_scores_gemma":[0.8866648,0.006485895,0.031392764,0.0040273215,0.00042343608,0.0010064028,0.000112717025,0.00029674816,0.06958993],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99335057,0.0051431525,0.00010826694,0.00032321367,0.0006068932,0.00046793916],"domain_scores_gemma":[0.9963207,0.0023171068,0.00038608484,0.00037702543,0.00021272337,0.00038629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00543558,0.0010065511,0.000473911,0.0012867068,0.009450742,0.0074980585,0.0016486713,0.0017372809,0.009354215],"category_scores_gemma":[0.00827044,0.00039699484,0.0007113642,0.0010040819,0.011474816,0.0065978053,0.010288996,0.003436498,0.0022404012],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091843496,0.0001887564,0.0010304768,0.0006902151,0.000043896656,0.0011619683,0.8227981,0.00017655735,0.0036177242,0.056493934,0.029708639,0.083997995],"study_design_scores_gemma":[0.000035069188,0.00015487336,0.0011814189,0.00065091794,0.00003942348,0.0025167107,0.4273957,0.00035401538,0.0028822604,0.012934744,0.5518054,0.000049429833],"about_ca_topic_score_codex":0.0008564791,"about_ca_topic_score_gemma":0.001929538,"teacher_disagreement_score":0.009450742,"about_ca_system_score_codex":0.0016240985,"about_ca_system_score_gemma":0.0018227892,"threshold_uncertainty_score":0.031292915},"labels":[],"label_agreement":null},{"id":"W7026750966","doi":"","title":"Application of Machine Learning Algorithms to the Prediction of Water Main Deterioration","year":2021,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Water Systems and 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":"Feature (linguistics); Population; Noise (video); Artificial neural network; Filter (signal processing)","score_opus":0.011632523640679246,"score_gpt":0.2225789515709855,"score_spread":0.21094642793030627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026750966","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":[0.472169,0.0053120092,0.51171434,0.0014442513,0.00027695953,0.00034199547,0.0013941344,0.0020258843,0.005321491],"genre_scores_gemma":[0.9098439,0.00097164,0.087206334,0.0001267293,0.00007565029,0.00011876019,0.00089142367,0.0000326045,0.00073310366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981913,0.00075283955,0.00018032585,0.0002938254,0.0004490479,0.00013265919],"domain_scores_gemma":[0.9891209,0.00862226,0.00072122377,0.00026517845,0.0011650128,0.00010539222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049331095,0.0014297318,0.0010429353,0.0032718533,0.00049931154,0.0011771577,0.0010011108,0.0010630626,0.00050182705],"category_scores_gemma":[0.015871787,0.0003491862,0.0006795333,0.0026270058,0.0004481208,0.00091986783,0.00056013657,0.0015505708,0.00025179342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089214496,0.00017264344,0.032245297,0.000100090314,0.00016966481,0.00008029661,0.00005697832,0.8348756,0.00045814918,0.000614616,0.0011059828,0.13003144],"study_design_scores_gemma":[0.0000049457144,0.000026115691,0.0034020185,0.000015258461,0.000009534451,0.000010153582,0.000022492895,0.9951201,0.00030120256,0.00086537405,0.00021510872,0.000007578516],"about_ca_topic_score_codex":0.053671032,"about_ca_topic_score_gemma":0.0257988,"teacher_disagreement_score":0.053671032,"about_ca_system_score_codex":0.0014633838,"about_ca_system_score_gemma":0.0019297438,"threshold_uncertainty_score":0.10671729},"labels":[],"label_agreement":null},{"id":"W7027297326","doi":"","title":"Cathodic protection of water mains in Ottawa: analysis and planning","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":true,"ca_institutions":"","funders":"","keywords":"Cathodic protection; Mains electricity; Breakage; Electricity; Water supply; Economic analysis","score_opus":0.009205522234099255,"score_gpt":0.19224110313976217,"score_spread":0.1830355809056629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027297326","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":[0.9639062,0.0011680333,0.008134955,0.00044579702,0.000013258797,0.00043996132,0.00811739,0.00013439194,0.017639982],"genre_scores_gemma":[0.9819687,0.0010394707,0.0068458896,0.000023003839,0.0000029381852,0.000094683695,0.003173661,0.000027083855,0.0068245414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995902,0.00005212172,0.000017798959,0.00006018508,0.00007983629,0.00019987997],"domain_scores_gemma":[0.9993243,0.00023612677,0.000087574874,0.000030737534,0.00021686629,0.00010444102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033515485,0.0007480005,0.00040297204,0.0016506771,0.0013747534,0.0020806815,0.001212344,0.0007698327,0.004901041],"category_scores_gemma":[0.0010768304,0.0008604984,0.00085234205,0.0027653186,0.0012717503,0.00074028765,0.00068591355,0.00059441396,0.00035664733],"study_design_candidate":"observational","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":[0.00030533626,0.00010943494,0.08421481,0.00049143797,0.00011637074,0.0012822781,0.0006152203,0.8705201,0.0029674524,0.0060434476,0.0025063097,0.030827697],"study_design_scores_gemma":[0.00007566573,0.00041302457,0.22867851,0.00017973139,0.00026566163,0.00040089843,0.0063920645,0.7322056,0.007100181,0.0042838054,0.019787755,0.00021707984],"about_ca_topic_score_codex":0.9411128,"about_ca_topic_score_gemma":0.965144,"teacher_disagreement_score":0.058887184,"about_ca_system_score_codex":0.030078936,"about_ca_system_score_gemma":0.016085364,"threshold_uncertainty_score":0.21823895},"labels":[],"label_agreement":null},{"id":"W7027442116","doi":"","title":"Contaminant intrusion in water distribution systems : advanced modelling approaches","year":2013,"lang":"en","type":"other","venue":"cIRcle (University of British Columbia)","topic":"Water Systems and 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","keywords":"Intrusion; Reliability (semiconductor); Groundwater; Contamination; Water quality; Seawater intrusion","score_opus":0.008623237832903993,"score_gpt":0.13054760279333302,"score_spread":0.12192436496042904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027442116","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":[0.014589745,0.0017258772,0.9699265,0.0004384508,0.00006348284,0.00010074947,0.00031878147,0.00024721047,0.0125892665],"genre_scores_gemma":[0.75926507,0.008340342,0.21612585,0.00019108754,0.00024108618,0.00061705697,0.00072852836,0.00015297387,0.014337935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995851,0.00012729771,0.000029705103,0.000061497776,0.00015282969,0.00004359502],"domain_scores_gemma":[0.9993924,0.00032332726,0.000099238496,0.000030286761,0.00013469675,0.000019947434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006566472,0.0010504944,0.0008843215,0.001185259,0.0007156739,0.0018911788,0.0017714916,0.0016525147,0.0022721242],"category_scores_gemma":[0.0016610854,0.0005654878,0.0014696677,0.0014580805,0.00065310026,0.0015972005,0.0012521168,0.0013540585,0.00040087255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047297,0.0000112615735,0.0002660205,0.0000378383,0.000010318769,0.000035806854,0.000035146888,0.9830079,0.00032501854,0.010801969,0.00017717932,0.0052868435],"study_design_scores_gemma":[0.0000017238767,0.000006402573,0.00006283454,0.000010149155,0.0000047199983,0.000010072143,0.000013421757,0.99275017,0.000101419624,0.0060595605,0.0009748004,0.0000046195323],"about_ca_topic_score_codex":0.026162429,"about_ca_topic_score_gemma":0.01305401,"teacher_disagreement_score":0.026162429,"about_ca_system_score_codex":0.0015684621,"about_ca_system_score_gemma":0.0015169379,"threshold_uncertainty_score":0.05202025},"labels":[],"label_agreement":null},{"id":"W7028458849","doi":"","title":"Failure modes and mechanisms in gray cast iron pipe","year":2001,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Research Council Canada; University of Toronto","keywords":"Cast iron; Water pipe; Mains electricity; Pipe; Gray iron; Canalisation; Failure mode and effects analysis","score_opus":0.0059946862842492395,"score_gpt":0.17308263082860412,"score_spread":0.16708794454435488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028458849","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":[0.9684004,0.0006181814,0.027383622,0.00010421092,0.000009497742,0.000036615154,0.00007633233,0.0002209057,0.0031502564],"genre_scores_gemma":[0.99760026,0.00009402532,0.0012811265,0.0000035823039,0.0000029624287,0.000008678113,0.000023415403,0.00000498752,0.0009808791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99980503,0.000027049762,0.000009563573,0.000023441251,0.00008988933,0.00004513213],"domain_scores_gemma":[0.9991623,0.00029232993,0.0002680867,0.0000807629,0.00014651577,0.000049934646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000552057,0.00041796613,0.00027929986,0.0019135872,0.0004862002,0.00037542643,0.00058130105,0.0006365424,0.001007824],"category_scores_gemma":[0.0014014536,0.0003167321,0.00029290275,0.00047355107,0.0013470049,0.0003959453,0.00037283084,0.00022457776,0.00014451567],"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":[0.0014439718,0.0003232462,0.12002756,0.000497459,0.00010638587,0.0050825668,0.0043797004,0.5929478,0.085495085,0.06987273,0.004393527,0.11542997],"study_design_scores_gemma":[0.00005440256,0.00041505723,0.07482502,0.0000565941,0.000046091533,0.0013439368,0.0010027756,0.8819047,0.011414772,0.026860401,0.0019819315,0.00009434747],"about_ca_topic_score_codex":0.009295743,"about_ca_topic_score_gemma":0.0045080613,"teacher_disagreement_score":0.009295743,"about_ca_system_score_codex":0.00046660894,"about_ca_system_score_gemma":0.00027944823,"threshold_uncertainty_score":0.018483222},"labels":[],"label_agreement":null},{"id":"W7028581139","doi":"","title":"Forensics of water quality failure in distribution systems - a conceptual framework","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Water quality; Intrusion; Contamination; Quality (philosophy); Corrosion; Human life","score_opus":0.013179182165744398,"score_gpt":0.21617387453959036,"score_spread":0.20299469237384596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028581139","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":[0.032694306,0.0095212115,0.8823108,0.015037285,0.00037111115,0.0005428886,0.0006146937,0.00048086717,0.058426898],"genre_scores_gemma":[0.6336463,0.012065804,0.34291404,0.0009265557,0.0008693293,0.000618032,0.00080112036,0.00010193682,0.008056853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99539137,0.0016652005,0.0004256257,0.0006693194,0.0014813913,0.000367158],"domain_scores_gemma":[0.98642075,0.0062292917,0.0022293893,0.0018700979,0.0027047854,0.00054565084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008291775,0.0011365872,0.0010996175,0.009554598,0.0031127748,0.012670436,0.0039521004,0.0052414765,0.003923204],"category_scores_gemma":[0.02134997,0.0008658486,0.0015536523,0.0034744695,0.014951797,0.018292049,0.006672047,0.002345461,0.0006823926],"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":[0.000024899056,0.0000505469,0.0019464258,0.00021870888,0.00004040976,0.00065834046,0.0005795742,0.017777113,0.0003931425,0.9499209,0.0016891151,0.026700793],"study_design_scores_gemma":[0.000022335042,0.0000660844,0.00095416495,0.0005612687,0.000052596213,0.0014173695,0.0012314003,0.0914366,0.000930504,0.88500243,0.018238708,0.000086579574],"about_ca_topic_score_codex":0.0054866616,"about_ca_topic_score_gemma":0.0022305495,"teacher_disagreement_score":0.012670436,"about_ca_system_score_codex":0.0035036264,"about_ca_system_score_gemma":0.004664943,"threshold_uncertainty_score":0.043851674},"labels":[],"label_agreement":null},{"id":"W7029954846","doi":"","title":"Management and renewal of urban water infrastructure","year":2008,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Water supply; Water resources; Urban planning; Work (physics); Government (linguistics); Sustainability","score_opus":0.003975692693126551,"score_gpt":0.150969032994362,"score_spread":0.14699334030123545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7029954846","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":[0.6195573,0.002471005,0.07323238,0.013936848,0.0010484083,0.0005232125,0.00058980676,0.0003140774,0.2883269],"genre_scores_gemma":[0.967437,0.0009959149,0.0036063218,0.000096817894,0.0001443686,0.000032340024,0.00014898347,0.00003020226,0.02750805],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927837,0.00021195371,0.000026873106,0.00009728456,0.0002246417,0.00016082077],"domain_scores_gemma":[0.9985434,0.00024255634,0.00024431074,0.00016573955,0.000572995,0.00023098581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009684782,0.0001474769,0.00033241056,0.00077921525,0.0007296292,0.0021400738,0.0008014926,0.00083927077,0.0096672075],"category_scores_gemma":[0.004556321,0.00015649185,0.0002585383,0.000967449,0.00088943273,0.0016172765,0.0010101869,0.0005537331,0.0011735886],"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":[0.00022047409,0.00039122754,0.039420135,0.0005318538,0.00010065028,0.000901295,0.00096017413,0.10593063,0.0134534845,0.14864117,0.04713646,0.6423124],"study_design_scores_gemma":[0.00009111578,0.0007425792,0.09811274,0.00035267614,0.00013413526,0.0006898153,0.006540959,0.31743082,0.019491326,0.22279243,0.33350885,0.00011249247],"about_ca_topic_score_codex":0.0038291404,"about_ca_topic_score_gemma":0.009882099,"teacher_disagreement_score":0.0096672075,"about_ca_system_score_codex":0.0010891944,"about_ca_system_score_gemma":0.0026732998,"threshold_uncertainty_score":0.03234005},"labels":[],"label_agreement":null},{"id":"W7030232185","doi":"","title":"Modelling the deterioration of water mains and planning their renewal","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":true,"ca_institutions":"","funders":"","keywords":"Mains electricity; Breakage; Water resources; Water supply; Resource (disambiguation); Moisture","score_opus":0.023036036183903503,"score_gpt":0.17379958761700445,"score_spread":0.15076355143310094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030232185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48203498,0.0007954012,0.50063133,0.00051356433,0.000040390223,0.00020357834,0.0012328112,0.00055587455,0.013992049],"genre_scores_gemma":[0.97056323,0.0005625887,0.021569781,0.0000228608,0.00001492032,0.00012373067,0.00045806993,0.000056973917,0.0066278274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998184,0.000044561337,0.000008460857,0.000044188386,0.0000305626,0.00005397072],"domain_scores_gemma":[0.9997011,0.00015259677,0.00005258637,0.000020167481,0.000041930783,0.00003161533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040818783,0.0006050576,0.00058487424,0.00045975117,0.00032019874,0.0007819873,0.00068240566,0.0012508008,0.0022000053],"category_scores_gemma":[0.001287053,0.0005886318,0.0005502124,0.0006276138,0.0006642102,0.0012038345,0.00045489857,0.00057689706,0.00028439617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011948616,0.0000066793323,0.00052878476,0.000011184316,0.0000023250736,0.000016296923,0.000008365106,0.9959466,0.00037641902,0.000796683,0.00008246683,0.0022122571],"study_design_scores_gemma":[0.0000031137392,0.000012650214,0.0004650251,0.0000023638115,0.000003518806,0.00000727822,0.00001171157,0.99767333,0.00029621288,0.0012289373,0.000292612,0.0000032277478],"about_ca_topic_score_codex":0.029414639,"about_ca_topic_score_gemma":0.018320085,"teacher_disagreement_score":0.029414639,"about_ca_system_score_codex":0.0011280333,"about_ca_system_score_gemma":0.0010660128,"threshold_uncertainty_score":0.05848688},"labels":[],"label_agreement":null},{"id":"W7031057020","doi":"","title":"3am Mixtape 076","year":2015,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Water Systems and 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":"Favourite; Cave; White (mutation); Cover (algebra)","score_opus":0.004884406704218166,"score_gpt":0.1573565550327155,"score_spread":0.15247214832849734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7031057020","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":[0.0003787039,0.00017427008,0.0010836906,0.00046543562,0.0014182759,0.00016877537,0.006606994,0.0047715725,0.98493236],"genre_scores_gemma":[0.0008022543,0.00008823851,0.0002624583,0.00018980395,0.00021934167,0.00008776533,0.0021555696,0.0013688828,0.9948257],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999642,0.000045796492,0.000014011609,0.000044073615,0.00018972147,0.00006430052],"domain_scores_gemma":[0.99841034,0.00029312127,0.00004430435,0.00015538791,0.0006402436,0.00045668348],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004991195,0.0011287357,0.0008516922,0.0011840723,0.0012975662,0.004073131,0.0012488841,0.0011921126,0.9489545],"category_scores_gemma":[0.0033280065,0.0006082565,0.00059213967,0.0012504982,0.00037057747,0.0022849068,0.0029173016,0.0016931059,0.8862076],"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":[0.000045810222,0.000023037332,0.000030248319,0.000051889045,0.000001044302,0.000015650861,0.000023887234,0.000022171864,0.0001636068,0.000491382,0.98023176,0.018899538],"study_design_scores_gemma":[0.000020422016,0.000028254557,0.00039193762,0.000051222396,0.0000012782057,0.000020920392,0.000058639685,0.00007014979,0.00011548188,0.00032937876,0.99890566,0.0000067115257],"about_ca_topic_score_codex":0.0034171524,"about_ca_topic_score_gemma":0.01017504,"teacher_disagreement_score":0.051045477,"about_ca_system_score_codex":0.00079894194,"about_ca_system_score_gemma":0.0006787268,"threshold_uncertainty_score":0.07281017},"labels":[],"label_agreement":null},{"id":"W7033661711","doi":"","title":"Reliability-Based Management of Water Distribution&#13;\\nNetworks","year":2011,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Water Systems and 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":"Rehabilitation; Analytic hierarchy process; Reliability (semiconductor); Schedule; Process (computing); Scheduling (production processes); Management system; Water resources; Intervention (counseling); Index (typography)","score_opus":0.01337111271529129,"score_gpt":0.2177770626370648,"score_spread":0.20440594992177352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033661711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09264837,0.0017920363,0.86931497,0.0021419097,0.00018695951,0.00038248833,0.00033689768,0.0016292947,0.031567074],"genre_scores_gemma":[0.9217353,0.0011795514,0.069728434,0.000070604925,0.000082661,0.00012562513,0.00028440464,0.00010494329,0.006688477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998669,0.0004240493,0.0000895177,0.00019424167,0.0004853732,0.00013785165],"domain_scores_gemma":[0.99722457,0.0006034583,0.00052821985,0.00020773061,0.0012591012,0.00017693655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022842034,0.0004950051,0.00037488082,0.0016050816,0.0006951231,0.0021136699,0.001454013,0.00040550283,0.0022362936],"category_scores_gemma":[0.0043838182,0.00029524407,0.00029493295,0.0010968753,0.00045715578,0.0022278533,0.0013367949,0.000512119,0.0005284517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009828848,0.0001414481,0.008531299,0.00024300642,0.00007011825,0.00016641122,0.00052683917,0.4801052,0.010060326,0.037191406,0.011253618,0.45161211],"study_design_scores_gemma":[0.000016870155,0.00015051688,0.0056091305,0.00008218373,0.00004438285,0.00007753096,0.0005625225,0.9445859,0.005602573,0.025023937,0.018198878,0.000045545552],"about_ca_topic_score_codex":0.0071496437,"about_ca_topic_score_gemma":0.01074998,"teacher_disagreement_score":0.0071496437,"about_ca_system_score_codex":0.002141231,"about_ca_system_score_gemma":0.0019110824,"threshold_uncertainty_score":0.015535831},"labels":[],"label_agreement":null},{"id":"W7036854076","doi":"","title":"Characterization of external corrosion pits in ductile iron pipes","year":2011,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Corrosion; Ductile iron; Characterization (materials science); Volume (thermodynamics); Intergranular corrosion; Cast iron","score_opus":0.011830642347995328,"score_gpt":0.17287776454142692,"score_spread":0.1610471221934316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036854076","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":[0.9937024,0.000028478109,0.0057995566,0.000003026633,5.882091e-7,0.0000061899314,0.000041763375,0.000020017658,0.00039802695],"genre_scores_gemma":[0.997292,0.000018117054,0.0023947675,0.0000011356988,4.8005757e-7,0.0000038658945,0.000055384124,0.000005941937,0.00022835899],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99987173,0.000014568405,0.0000056132308,0.000035825236,0.000049610746,0.000022683991],"domain_scores_gemma":[0.99956745,0.000103439175,0.000120800556,0.000032661515,0.0001410627,0.00003455163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002461439,0.00012035616,0.00018528386,0.00047805638,0.00014361356,0.00022841236,0.00016796267,0.00014885595,0.0004956737],"category_scores_gemma":[0.00050731754,0.00013487466,0.0000891762,0.0002622058,0.00029071828,0.0001775715,0.00018776662,0.00016484824,0.000055496283],"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":[0.00031772832,0.00007208692,0.09366167,0.00010345462,0.000024101348,0.0003741686,0.0004358854,0.009440213,0.87048113,0.0003899789,0.00011778729,0.024581786],"study_design_scores_gemma":[0.000016784612,0.0005314496,0.5706587,0.000011381064,0.000028051414,0.00080025254,0.0004336372,0.0642201,0.3621298,0.00023433755,0.0009091324,0.000026374542],"about_ca_topic_score_codex":0.0009794991,"about_ca_topic_score_gemma":0.0017463879,"teacher_disagreement_score":0.0009794991,"about_ca_system_score_codex":0.000117542,"about_ca_system_score_gemma":0.00009445108,"threshold_uncertainty_score":0.0019475818},"labels":[],"label_agreement":null},{"id":"W7039270819","doi":"","title":"L'observatoire de l'hypertension artérielle au Gabon : un modèle intégré de prise en charge des maladies chroniques pour les pays en développement","year":2005,"lang":"fr","type":"other","venue":"Open MIND","topic":"Water Systems and 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":"Context (archaeology); Population; Government (linguistics); Field (mathematics)","score_opus":0.03736991420413098,"score_gpt":0.2395886080481808,"score_spread":0.20221869384404983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039270819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37910277,0.012774435,0.50718826,0.018735923,0.0008202278,0.00015337301,0.008598796,0.00096477376,0.071661524],"genre_scores_gemma":[0.9232945,0.0038470523,0.030285833,0.00041176722,0.00018687308,0.00014471919,0.00095449935,0.00016054804,0.04071418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997967,0.00006306792,0.0000065507343,0.000050484417,0.000031051066,0.00005206879],"domain_scores_gemma":[0.999169,0.00048511836,0.00010807517,0.00003383258,0.0001206969,0.00008341141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074879965,0.0012553565,0.0012756409,0.0007201967,0.00080484746,0.0025306745,0.0015151008,0.0026145508,0.0046836273],"category_scores_gemma":[0.0026065947,0.0006268256,0.001331332,0.0008659038,0.0014831072,0.0013101919,0.0012164789,0.0019481556,0.00026422678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059363934,0.000029145936,0.002668813,0.00006311916,0.000062664054,0.00009407123,0.000119203985,0.9429031,0.00049835816,0.044222634,0.002769746,0.0065098256],"study_design_scores_gemma":[0.000052416617,0.000024336805,0.001612226,0.000032887743,0.00006517881,0.000020448253,0.00007622072,0.97502124,0.00022590863,0.01837625,0.0044623967,0.000030486963],"about_ca_topic_score_codex":0.53942645,"about_ca_topic_score_gemma":0.37697047,"teacher_disagreement_score":0.53942645,"about_ca_system_score_codex":0.005024036,"about_ca_system_score_gemma":0.0067709056,"threshold_uncertainty_score":0.9265719},"labels":[],"label_agreement":null},{"id":"W7042624931","doi":"","title":"Promptness and Completeness of FYs 2000 and 2001 DOD Payments to the U.S. Treasury for Water and Sewer Services Provided by the District of Columbia","year":2001,"lang":"en","type":"article","venue":"Defense Technical Information Center (DTIC)","topic":"Water Systems and 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":"Treasury; Payment; Water supply; Water industry; Quarter (Canadian coin); Non-revenue water; Sanitary sewer; Capital (architecture)","score_opus":0.009865963708394957,"score_gpt":0.20061873403653324,"score_spread":0.19075277032813828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042624931","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":[0.6319987,0.002115449,0.0035987466,0.008327883,0.0015286754,0.0008942736,0.09471838,0.00090820127,0.25590974],"genre_scores_gemma":[0.713601,0.0025901452,0.0043237135,0.0014242757,0.00025079446,0.0005170496,0.10974232,0.0003260683,0.1672246],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.98932743,0.00040074176,0.00067822903,0.0005181327,0.00810135,0.0009741457],"domain_scores_gemma":[0.9643391,0.0029652612,0.0050841183,0.0013007716,0.02524817,0.001062653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004965714,0.00037937678,0.0003521988,0.0050959196,0.0020867526,0.0042850655,0.00083193637,0.0009624926,0.0060835043],"category_scores_gemma":[0.041979894,0.00045503734,0.0001927357,0.0045749736,0.00052170735,0.0009047338,0.001041648,0.001606547,0.0022562258],"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":[0.0008724927,0.00037907722,0.17302226,0.00026794907,0.000073322255,0.0005516251,0.0022354543,0.006499773,0.0022667823,0.012267303,0.5798046,0.22175936],"study_design_scores_gemma":[0.00007200935,0.0001325755,0.6399602,0.00023489776,0.00003034138,0.0002108575,0.0025711122,0.0038653063,0.003821094,0.00146208,0.34753186,0.00010767484],"about_ca_topic_score_codex":0.459544,"about_ca_topic_score_gemma":0.5708293,"teacher_disagreement_score":0.459544,"about_ca_system_score_codex":0.01194475,"about_ca_system_score_gemma":0.015074915,"threshold_uncertainty_score":0.9137383},"labels":[],"label_agreement":null},{"id":"W7045169010","doi":"","title":"An Advancement in the detection of pipe leaks","year":2001,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Water pipe; Leakage (economics); Leak detection; Leak; Metering mode; Pipeline transport","score_opus":0.008118571089108295,"score_gpt":0.20330852455335102,"score_spread":0.19518995346424273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7045169010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061452135,0.020617774,0.87463284,0.0037033926,0.0015626184,0.00032858757,0.0019528912,0.008191709,0.027558029],"genre_scores_gemma":[0.53935605,0.017592292,0.41555443,0.002053018,0.0010916117,0.00031323583,0.0020573807,0.0005802257,0.021401713],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99596703,0.00087026536,0.00016481889,0.00088857434,0.0019156861,0.00019364282],"domain_scores_gemma":[0.9901689,0.003378498,0.0013267479,0.0013051071,0.00355627,0.00026448278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023685058,0.0015180071,0.001388196,0.0039827107,0.0005416274,0.0017340735,0.00224156,0.0030876996,0.006848619],"category_scores_gemma":[0.0108726835,0.00070358516,0.0008916712,0.0035272879,0.00095026096,0.0031738263,0.0016497461,0.0027333184,0.005888698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006760533,0.00064887526,0.041380025,0.0011093206,0.00015014283,0.00041038645,0.00021763453,0.008561103,0.07160609,0.007959222,0.02161038,0.84567064],"study_design_scores_gemma":[0.00026168305,0.0017864857,0.058441695,0.00080366875,0.00054775085,0.008562616,0.0006618652,0.37022817,0.2918443,0.025912132,0.24016324,0.0007863412],"about_ca_topic_score_codex":0.0018101705,"about_ca_topic_score_gemma":0.0013692954,"teacher_disagreement_score":0.006848619,"about_ca_system_score_codex":0.0005405997,"about_ca_system_score_gemma":0.0006301423,"threshold_uncertainty_score":0.022910953},"labels":[],"label_agreement":null},{"id":"W7045837767","doi":"","title":"Building a shared open research data repository community in Canada","year":2022,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Water Systems and 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":"Data sharing; Research data; Information repository; Open data; Data collection; Data curation; Context (archaeology)","score_opus":0.06129402572226097,"score_gpt":0.2965992277296935,"score_spread":0.23530520200743252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7045837767","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056258667,0.0041435272,0.21417204,0.04701797,0.0034470267,0.004461431,0.42768475,0.10724993,0.13556466],"genre_scores_gemma":[0.13698614,0.0031837823,0.2593045,0.004136331,0.00044770786,0.0023174307,0.46938187,0.025495933,0.09874629],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.96361184,0.0037676604,0.003168778,0.0057448535,0.019582037,0.004124908],"domain_scores_gemma":[0.77787465,0.014494191,0.0045198626,0.054583445,0.11967574,0.028852187],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.036386278,0.001271149,0.0024350432,0.018456325,0.012058351,0.016943656,0.0103407595,0.0028811344,0.018157294],"category_scores_gemma":[0.08233924,0.0018305681,0.0024806347,0.035928242,0.005245817,0.014371918,0.022171954,0.003945092,0.010871057],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010957216,0.0003751476,0.022997998,0.00088677433,0.00047873714,0.0005536674,0.0046462677,0.0069029755,0.0043840655,0.078753084,0.71733546,0.16159008],"study_design_scores_gemma":[0.00022647099,0.000053919517,0.0095077,0.00048065506,0.00017798014,0.000097434626,0.0031486163,0.012416158,0.004965032,0.015952867,0.95265746,0.00031573605],"about_ca_topic_score_codex":0.94115776,"about_ca_topic_score_gemma":0.928377,"teacher_disagreement_score":0.98965925,"about_ca_system_score_codex":0.056114376,"about_ca_system_score_gemma":0.299486,"threshold_uncertainty_score":0.4071402},"labels":[],"label_agreement":null},{"id":"W7046130612","doi":"","title":"Considering Time-dependent Factors in the Statistical Prediction of Water Main Breaks","year":2000,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and Optimization","field":"Engineering","cited_by":14,"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":"Measure (data warehouse); Breakage; Distribution (mathematics); Water utility; Work (physics); Statistical analysis","score_opus":0.007960180218061524,"score_gpt":0.17913863826014334,"score_spread":0.17117845804208182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7046130612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2542711,0.0009080203,0.7418921,0.00047095297,0.00015431052,0.00005856989,0.0006945663,0.000492614,0.0010578693],"genre_scores_gemma":[0.978925,0.0007025616,0.01761611,0.00004820567,0.000097787226,0.000054700442,0.000636523,0.000054961638,0.0018641016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938524,0.00016204627,0.00003507248,0.00018044378,0.00014077469,0.0000963623],"domain_scores_gemma":[0.9904096,0.00799306,0.000648721,0.00024443885,0.00048025642,0.00022380905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021708566,0.0011117534,0.00089153566,0.00091090886,0.00042212787,0.00087287556,0.00091334747,0.0014084022,0.0012199595],"category_scores_gemma":[0.012983359,0.0007479054,0.0007710463,0.0010492551,0.0006948426,0.0015703741,0.0006159604,0.0017129146,0.00035091417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026490063,0.000016746777,0.00254435,0.000012545987,0.000019512496,0.00002005757,0.0000064772353,0.99223715,0.00030640152,0.0007256633,0.00012857091,0.0039560483],"study_design_scores_gemma":[0.000001310364,0.000011916951,0.0005921785,0.0000014524466,0.0000031740933,0.0000037924417,0.0000019891572,0.9988845,0.0000913136,0.0003607511,0.000044071683,0.000003536617],"about_ca_topic_score_codex":0.017892163,"about_ca_topic_score_gemma":0.01765206,"teacher_disagreement_score":0.017892163,"about_ca_system_score_codex":0.0006388595,"about_ca_system_score_gemma":0.0011406809,"threshold_uncertainty_score":0.035576046},"labels":[],"label_agreement":null},{"id":"W7070623798","doi":"","title":"Practical aspects of acoustical leak location on plastic and large diameter pipe","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Leak; Leak detection; Significant difference; Canalisation","score_opus":0.011024177662456081,"score_gpt":0.22595904314212567,"score_spread":0.21493486547966958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7070623798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38620433,0.0005949348,0.6016709,0.00076898566,0.00012892936,0.00018624525,0.00023615565,0.0019533169,0.008256206],"genre_scores_gemma":[0.8538979,0.00022847523,0.14385621,0.000060979302,0.000057426147,0.00006726358,0.00010028134,0.00017785728,0.0015536086],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982603,0.00070212566,0.000061035746,0.00027443765,0.0005858984,0.00011619003],"domain_scores_gemma":[0.9964966,0.0018880492,0.0003790554,0.00042778844,0.0006930758,0.000115478375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015633755,0.000550434,0.0005048388,0.00068152044,0.00048941653,0.000742342,0.0007360847,0.00056397304,0.0023509536],"category_scores_gemma":[0.00637831,0.00048349542,0.00016897598,0.00093545136,0.000963837,0.0012036952,0.0010116132,0.00054379896,0.00072844635],"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":[0.0020876494,0.00025729008,0.03609463,0.0008148032,0.000051515857,0.0014113542,0.0013529259,0.0561508,0.63284075,0.007289403,0.0025224688,0.25912637],"study_design_scores_gemma":[0.00015790392,0.0035636842,0.06376215,0.00008859161,0.0000713397,0.0031773828,0.0023542992,0.27934667,0.62408257,0.009063333,0.014078587,0.0002534499],"about_ca_topic_score_codex":0.0012792697,"about_ca_topic_score_gemma":0.002552795,"teacher_disagreement_score":0.0023509536,"about_ca_system_score_codex":0.0003668437,"about_ca_system_score_gemma":0.00049448916,"threshold_uncertainty_score":0.008267999},"labels":[],"label_agreement":null},{"id":"W7071058974","doi":"","title":"Prototype software helps predict failure in large-diameter water mains","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Software; Mains electricity; Computer software; Key (lock); Reliability (semiconductor)","score_opus":0.005284406246868741,"score_gpt":0.18019571618466765,"score_spread":0.1749113099377989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071058974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.260493,0.00018280873,0.68977326,0.0003343876,0.0000794086,0.00020826544,0.0012968094,0.041464176,0.0061679212],"genre_scores_gemma":[0.8172864,0.00010038114,0.17656246,0.00007347545,0.000017849989,0.00013662894,0.0009131039,0.00087523693,0.004034488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960107,0.00010257298,0.00002192028,0.0001103635,0.00011927906,0.000044851004],"domain_scores_gemma":[0.9965154,0.002226121,0.00020300623,0.0003033219,0.00069050194,0.00006158228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009665646,0.0012633391,0.0006060931,0.0008743844,0.00049812096,0.0010497165,0.0017499061,0.001000077,0.00730747],"category_scores_gemma":[0.005528782,0.0006856642,0.0008221682,0.00042731807,0.00051980925,0.0014796949,0.00060854206,0.00063616,0.0009305825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007868104,0.00029605613,0.027932838,0.0004723414,0.00011812917,0.00046737122,0.0003225283,0.69631517,0.032238998,0.0056376136,0.01375774,0.22165436],"study_design_scores_gemma":[0.000042004325,0.00011014358,0.0014719961,0.000013010001,0.000022173112,0.00006021429,0.000049658578,0.98338634,0.011597745,0.0016680649,0.0015594539,0.000019233275],"about_ca_topic_score_codex":0.011263397,"about_ca_topic_score_gemma":0.015813025,"teacher_disagreement_score":0.011263397,"about_ca_system_score_codex":0.000799071,"about_ca_system_score_gemma":0.0011288723,"threshold_uncertainty_score":0.024445951},"labels":[],"label_agreement":null},{"id":"W7071453193","doi":"","title":"Risk assessment of drinking water supply failures in Canada","year":2006,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Water Systems and 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":"Fault tree analysis; Risk assessment; Water supply; Vulnerability (computing); Vulnerability assessment; Fuzzy logic","score_opus":0.004083210036475121,"score_gpt":0.17103332860530515,"score_spread":0.16695011856883002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071453193","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":[0.9657922,0.0007004539,0.013355872,0.0005497291,0.000010749858,0.00020702455,0.0017697342,0.00017235076,0.017441921],"genre_scores_gemma":[0.9936334,0.0002966879,0.002387035,0.000013377944,0.0000018417591,0.00001238222,0.0004784416,0.000010083508,0.0031668267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99933237,0.00007550883,0.000021073412,0.00005489865,0.0003789884,0.00013711299],"domain_scores_gemma":[0.9989503,0.00023908143,0.00012422806,0.0000259759,0.0005906771,0.000069841044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067694904,0.00049217365,0.00036011596,0.0019732907,0.0010179592,0.0012051791,0.0007263353,0.00036032853,0.0014228609],"category_scores_gemma":[0.00314571,0.00021765397,0.00036493922,0.0017357118,0.00045888143,0.0003684865,0.0005963484,0.00037339175,0.00009089145],"study_design_candidate":"observational","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":[0.000375143,0.000088032335,0.17174686,0.00014137074,0.00014214184,0.00073711533,0.00096023054,0.73804075,0.0028563228,0.0070590684,0.0046164044,0.07323667],"study_design_scores_gemma":[0.000024546844,0.00012169169,0.20516394,0.000080010606,0.00010103096,0.00015947923,0.0025896095,0.7748434,0.0035424486,0.0059367158,0.0073440694,0.00009304292],"about_ca_topic_score_codex":0.96383435,"about_ca_topic_score_gemma":0.9560868,"teacher_disagreement_score":0.036165655,"about_ca_system_score_codex":0.018169789,"about_ca_system_score_gemma":0.011508312,"threshold_uncertainty_score":0.13183165},"labels":[],"label_agreement":null},{"id":"W7095217833","doi":"","title":"Paper Optimal Pump Scheduling for Large Scale Water Transmission System by Linear Programming","year":2016,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Scheduling (production processes); Water supply; Linear programming; Time horizon; Transmission system; Transmission (telecommunications); Transmission network; Scale (ratio)","score_opus":0.005680088005851507,"score_gpt":0.19221061487803281,"score_spread":0.1865305268721813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095217833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07795759,0.00057907007,0.9078691,0.0005405068,0.000062361934,0.00022120973,0.00041438357,0.0003856894,0.01197013],"genre_scores_gemma":[0.89991826,0.0004817769,0.08918645,0.0000886289,0.00005711059,0.00028623757,0.00032916284,0.000112556394,0.00953986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965703,0.00016023402,0.000008852352,0.00004873128,0.000054398417,0.0000708329],"domain_scores_gemma":[0.99952745,0.00031421406,0.000054215496,0.000015868556,0.00004939344,0.000038839593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078882684,0.0006951712,0.000986308,0.00044025443,0.00039386668,0.0011247622,0.0006466213,0.0006201007,0.0070282756],"category_scores_gemma":[0.0011371911,0.0005146092,0.00050394214,0.0007696687,0.00046416017,0.000710878,0.0005237742,0.00076407683,0.00037060716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021454756,0.000018849727,0.00008505817,0.00002662561,0.0000074112786,0.0000119839815,0.000007499799,0.99419576,0.00030856204,0.0014619284,0.0004805881,0.0033743351],"study_design_scores_gemma":[0.0000062026797,0.000020792779,0.00003967215,0.0000018332063,0.0000024025494,0.000002340475,0.000006222973,0.9989134,0.00009592494,0.00065665046,0.0002530628,0.0000014551881],"about_ca_topic_score_codex":0.012928843,"about_ca_topic_score_gemma":0.009555955,"teacher_disagreement_score":0.012928843,"about_ca_system_score_codex":0.0014373433,"about_ca_system_score_gemma":0.0019759696,"threshold_uncertainty_score":0.025707185},"labels":[],"label_agreement":null},{"id":"W7096226658","doi":"","title":"2001, Condition assessment and rehabilitation of large sewers","year":2007,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Sanitary sewer; Relational database; Identification (biology); Terminology; Rehabilitation; Identifier; Coding (social sciences)","score_opus":0.0051867122323828035,"score_gpt":0.25371141168633715,"score_spread":0.24852469945395433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096226658","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":[0.84140396,0.005780761,0.065017655,0.0023554056,0.00013177286,0.0015844633,0.005399764,0.0010628422,0.07726338],"genre_scores_gemma":[0.9245474,0.0027617468,0.028703477,0.00009725813,0.000027717402,0.00017247925,0.0025788704,0.00007973455,0.041031364],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988655,0.00015742541,0.00007116042,0.00011939994,0.0007111477,0.00007537401],"domain_scores_gemma":[0.9988682,0.0001599809,0.00015938196,0.000056432116,0.00065539015,0.000100608995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013548932,0.00042992528,0.0003633602,0.0020149425,0.0007546605,0.0016755563,0.000497695,0.0005575532,0.0041829566],"category_scores_gemma":[0.003052617,0.00025428945,0.00025279605,0.0013917449,0.00067930017,0.0011615974,0.000916161,0.000383224,0.00094766007],"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":[0.00057562767,0.0004448371,0.21484464,0.0004116639,0.00005286772,0.0005112873,0.001449587,0.026938885,0.017613098,0.0034168244,0.02147566,0.712265],"study_design_scores_gemma":[0.0000473024,0.0009246345,0.87376547,0.0002477096,0.00007700274,0.0003521316,0.0028949056,0.047373284,0.0216066,0.003805766,0.04882834,0.000076788965],"about_ca_topic_score_codex":0.13414855,"about_ca_topic_score_gemma":0.30473956,"teacher_disagreement_score":0.13414855,"about_ca_system_score_codex":0.0054811565,"about_ca_system_score_gemma":0.0048138537,"threshold_uncertainty_score":0.26673543},"labels":[],"label_agreement":null},{"id":"W7096552936","doi":"","title":"ALEX FRANCISQUE STRATEGIES FOR IMPROVING THE SURVEILLANCE OF DRINKING WATER QUALITY IN DISTRIBUTION NETWORKS: APPLICATION OF EMERGING MODELING APPROACHES","year":2015,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Distribution (mathematics); Quality (philosophy); Water quality; Emerging technologies","score_opus":0.039686452831867856,"score_gpt":0.23462327619193377,"score_spread":0.1949368233600659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096552936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058612186,0.0022090473,0.9190638,0.004561591,0.00029731833,0.00009306372,0.00018387592,0.0002704361,0.014708609],"genre_scores_gemma":[0.85174257,0.0041677053,0.122112036,0.0005336187,0.00026356714,0.00016326581,0.00021510696,0.00008814983,0.020713948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964225,0.00014419509,0.0000130498,0.000081708866,0.00007806595,0.00004068691],"domain_scores_gemma":[0.9991903,0.00046003438,0.00008666621,0.000024018667,0.00020982417,0.000029050343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011407542,0.00084947026,0.00073311577,0.0008331152,0.00048066705,0.0012931665,0.0008533485,0.00092031003,0.002008251],"category_scores_gemma":[0.003418036,0.0003478661,0.00072065054,0.00056941784,0.0005792224,0.0012330443,0.0009167822,0.00096430595,0.00034017532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011463309,0.000054975444,0.0028074211,0.00011780174,0.00006337255,0.00006773829,0.0000926559,0.8914517,0.0024815428,0.034015153,0.0035314464,0.06520163],"study_design_scores_gemma":[0.00000873971,0.000030243422,0.00027202265,0.000012404277,0.000012163963,0.0000076212236,0.000030132755,0.9925862,0.0007718014,0.0048232456,0.0014357312,0.00000963276],"about_ca_topic_score_codex":0.034854777,"about_ca_topic_score_gemma":0.016317602,"teacher_disagreement_score":0.034854777,"about_ca_system_score_codex":0.0013816468,"about_ca_system_score_gemma":0.0011922704,"threshold_uncertainty_score":0.06930381},"labels":[],"label_agreement":null},{"id":"W7097022484","doi":"","title":"WARP - water mains renewal planner","year":2007,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Mains electricity; Breakage; Planner; Water utility; Water resources; Water supply","score_opus":0.0039709439279424314,"score_gpt":0.16316957758624626,"score_spread":0.15919863365830383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097022484","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":[0.07869585,0.00067621673,0.5749121,0.0023897518,0.00020770771,0.0037881604,0.038447276,0.08547347,0.21540943],"genre_scores_gemma":[0.5244856,0.00070375565,0.37762895,0.00024291105,0.000045198558,0.0015514293,0.015573261,0.002239895,0.07752905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980956,0.00003111535,0.000007083995,0.000040991927,0.00006840414,0.000042938238],"domain_scores_gemma":[0.9996513,0.00012478777,0.000032740292,0.000037246093,0.00008339416,0.00007041719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004736951,0.0007980863,0.00046229613,0.0009517328,0.00043700438,0.0009852317,0.00095897366,0.00061039446,0.058593605],"category_scores_gemma":[0.0011637056,0.00044846477,0.00039512647,0.00074053276,0.00025197884,0.0011588074,0.0010018412,0.00077404187,0.005855246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000638922,0.00022248321,0.003363216,0.00036800132,0.000025288708,0.000480881,0.00019001635,0.57893723,0.003758562,0.020785043,0.12442723,0.26680318],"study_design_scores_gemma":[0.00020484463,0.00016259776,0.0012002666,0.00006116233,0.000016948214,0.00013702137,0.00031371397,0.8388044,0.007046684,0.013850695,0.13816024,0.000041309348],"about_ca_topic_score_codex":0.012197264,"about_ca_topic_score_gemma":0.01688875,"teacher_disagreement_score":0.058593605,"about_ca_system_score_codex":0.0012967334,"about_ca_system_score_gemma":0.0029392205,"threshold_uncertainty_score":0.19601512},"labels":[],"label_agreement":null},{"id":"W7100176818","doi":"","title":"Quantifying the effectiveness of cathodic protection in water mains","year":2007,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"Breakage; Mains electricity; Cathodic protection; Electricity; Water supply; Water use","score_opus":0.020145587607563596,"score_gpt":0.22694633149020943,"score_spread":0.20680074388264583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100176818","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":[0.9960194,0.00008497244,0.0030465196,0.000015807327,0.0000010934217,0.000018926185,0.00015826071,0.00001914311,0.00063598005],"genre_scores_gemma":[0.9978516,0.00004427909,0.0016978502,0.0000021895974,7.878018e-7,0.0000065549843,0.00014356499,0.0000037119923,0.00024946252],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999114,0.00016384643,0.000039046085,0.00013639411,0.00039060056,0.00015608086],"domain_scores_gemma":[0.9967668,0.0014008292,0.0008139209,0.000170358,0.00073213526,0.00011592351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011556823,0.00040553883,0.0004947532,0.0013866158,0.00038506786,0.00070986926,0.0006046549,0.00043426544,0.0008425163],"category_scores_gemma":[0.0042601097,0.00022940325,0.00035874895,0.0016717783,0.000636755,0.0005720412,0.00036973943,0.0003834663,0.00008247639],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019287403,0.00039523,0.48078445,0.00035942852,0.00055054587,0.0003041576,0.0009373745,0.2893251,0.09090206,0.0016343168,0.00044381927,0.13243477],"study_design_scores_gemma":[0.00001602635,0.0008357529,0.8625512,0.0000155626,0.00011290154,0.00006662036,0.0008619011,0.101404004,0.032561485,0.0005038704,0.001022094,0.000048630183],"about_ca_topic_score_codex":0.18599863,"about_ca_topic_score_gemma":0.24896315,"teacher_disagreement_score":0.18599863,"about_ca_system_score_codex":0.0034005935,"about_ca_system_score_gemma":0.0014264943,"threshold_uncertainty_score":0.36983198},"labels":[],"label_agreement":null},{"id":"W7108070211","doi":"10.1088/1755-1315/1561/1/012037","title":"Durability Assessment of Francis Turbine Spiral Casings","year":2025,"lang":"","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Water Systems and 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; Université du Québec à Trois-Rivières","funders":"","keywords":"Durability; Service life; Casing; Francis turbine; Transient (computer programming); Spiral (railway); Hydroelectricity","score_opus":0.007649171708643376,"score_gpt":0.2039381732272157,"score_spread":0.19628900151857231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7108070211","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":[0.99763846,0.00011358355,0.0016957617,0.000008199507,0.0000063535626,0.000006673409,0.00006667814,0.00003115212,0.00043330432],"genre_scores_gemma":[0.99946505,0.000024032995,0.00028925,0.0000012879224,6.699571e-7,0.000001615617,0.000037254926,0.0000029235966,0.00017790627],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998017,0.000019837278,0.000014336491,0.000042936015,0.000087657565,0.00003360186],"domain_scores_gemma":[0.99936,0.00017168834,0.00009785362,0.000069038106,0.0002523039,0.00004910301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033556615,0.00031062766,0.00034378917,0.0009700929,0.00025077784,0.00044631114,0.00041800577,0.0004115812,0.0012581749],"category_scores_gemma":[0.00096460234,0.000118334836,0.0002645184,0.00040504057,0.0003763407,0.00039335503,0.00020332982,0.00020289699,0.0001632566],"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":[0.0018508809,0.0002091273,0.04603398,0.000392693,0.00008569416,0.0009952957,0.0004041277,0.18637203,0.67360663,0.0007595779,0.000723409,0.08856656],"study_design_scores_gemma":[0.000026569289,0.004196548,0.08432992,0.000053511314,0.00010747015,0.0004658506,0.0007750391,0.38560057,0.5220986,0.00041357853,0.0018448767,0.000087475906],"about_ca_topic_score_codex":0.0028298697,"about_ca_topic_score_gemma":0.0035525276,"teacher_disagreement_score":0.0028298697,"about_ca_system_score_codex":0.0004748622,"about_ca_system_score_gemma":0.00020261163,"threshold_uncertainty_score":0.005626738},"labels":[],"label_agreement":null},{"id":"W7114802657","doi":"10.1016/j.aei.2025.104109","title":"WAM-ONTO: A semantic framework for water infrastructure asset management","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Water Systems and 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":"Asset management; Ontology; Workflow; SPARQL; Asset (computer security); IT asset management; Semantic Web; Consistency (knowledge bases); XBRL; Domain (mathematical analysis)","score_opus":0.0020068170314834293,"score_gpt":0.19368361228659228,"score_spread":0.19167679525510886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114802657","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":[0.0026506751,0.00034671722,0.98055077,0.0004152758,0.00008093142,0.00037559465,0.0018169396,0.007874071,0.005888998],"genre_scores_gemma":[0.046288818,0.0009782705,0.93985707,0.00033593594,0.00006908755,0.00056928134,0.008393806,0.0011090756,0.0023986108],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981536,0.0003900173,0.00034329118,0.00029328826,0.0006493278,0.0001704591],"domain_scores_gemma":[0.99895585,0.0002873048,0.00012961963,0.00030851038,0.00020537351,0.00011336654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002959135,0.0012880684,0.0009850848,0.004355731,0.001445071,0.004601779,0.0031017528,0.001741656,0.0022894572],"category_scores_gemma":[0.003841407,0.00091060123,0.003197353,0.002793116,0.0015153721,0.005467594,0.0045032147,0.0022262097,0.0011258691],"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":[0.00015170772,0.00032426426,0.00305303,0.0012301441,0.00031239894,0.0011361418,0.0018588576,0.074991286,0.0076214974,0.7116464,0.028305802,0.16936858],"study_design_scores_gemma":[0.000056602847,0.00007258149,0.0014081196,0.0007249066,0.00015903225,0.0007558622,0.0007348606,0.27584714,0.005849767,0.2972045,0.4170657,0.000120867946],"about_ca_topic_score_codex":0.015136876,"about_ca_topic_score_gemma":0.02550914,"teacher_disagreement_score":0.015136876,"about_ca_system_score_codex":0.0015551767,"about_ca_system_score_gemma":0.0044441097,"threshold_uncertainty_score":0.030097544},"labels":[],"label_agreement":null},{"id":"W7115591436","doi":"10.23977/acss.2025.090404","title":"Research on Pipeline Leak Signal Reconstruction and Multi-Aperture Classification Method Based on Multi-Task 1D U-Net","year":2025,"lang":"","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Water Systems and 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":"Pipeline (software); Noise reduction; Feature (linguistics); Pipeline transport; Noise (video); Encoder; Leak; Signal reconstruction; Key (lock)","score_opus":0.05499362793035016,"score_gpt":0.34640069788143624,"score_spread":0.29140706995108606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115591436","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":[0.031225631,0.0006557449,0.9655854,0.00022070111,0.00006245331,0.0000350586,0.000048272424,0.00095563504,0.0012112025],"genre_scores_gemma":[0.72456557,0.0007397714,0.26653787,0.0003745241,0.00009740205,0.00012536692,0.00038459018,0.00019369803,0.006981082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963915,0.00006940521,0.000020602862,0.00012722814,0.0000886613,0.000054908753],"domain_scores_gemma":[0.9996425,0.000110885674,0.000052513627,0.00004192409,0.00011254908,0.000039677525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087420107,0.001113482,0.001135332,0.0005880001,0.0003736599,0.00086712127,0.0017091745,0.0010989452,0.0014338373],"category_scores_gemma":[0.0012484674,0.0005728203,0.0010653345,0.00059620856,0.0005425691,0.0017625852,0.0010337929,0.001041385,0.00037121624],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003846436,0.00016244454,0.0034877874,0.00019590702,0.0001550608,0.00019638478,0.0001463712,0.6250898,0.025950534,0.005126495,0.0024262106,0.33667836],"study_design_scores_gemma":[0.0000034498455,0.0000264568,0.00017143591,0.0000030465053,0.000008360056,0.000019065461,0.00000618349,0.9972114,0.0018181464,0.0004869853,0.00024107083,0.000004412967],"about_ca_topic_score_codex":0.0058195125,"about_ca_topic_score_gemma":0.0043270644,"teacher_disagreement_score":0.0058195125,"about_ca_system_score_codex":0.0007570739,"about_ca_system_score_gemma":0.00094766874,"threshold_uncertainty_score":0.0115712285},"labels":[],"label_agreement":null},{"id":"W7116869709","doi":"10.1016/j.aei.2025.104263","title":"Co-MixPL: An optimized semi-supervised learning method for tunnel water leakage detection","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Water Systems and 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":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; National Natural Science Foundation of China","keywords":"Discriminative model; Leakage (economics); Reliability (semiconductor); Regression; Labeled data; Training set","score_opus":0.005129122955505167,"score_gpt":0.22762180764984802,"score_spread":0.22249268469434286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116869709","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":[0.007340045,0.00020748939,0.98507625,0.000072403614,0.00005204429,0.000071441646,0.00025562727,0.00635257,0.00057211256],"genre_scores_gemma":[0.15076745,0.00015157665,0.83737975,0.0002568461,0.0001245982,0.00038786803,0.0022672405,0.0012887609,0.0073759784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987263,0.0003846393,0.000061866886,0.0003624809,0.0003533822,0.000111339155],"domain_scores_gemma":[0.9984969,0.00051893416,0.0001297844,0.00027605315,0.00049409166,0.00008428497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018455576,0.0018415265,0.0017343492,0.001599666,0.00075356074,0.0011889848,0.0028762165,0.002128103,0.004345412],"category_scores_gemma":[0.0034215823,0.000916646,0.0014474115,0.001201299,0.00065429084,0.0018019823,0.0019681472,0.0019346791,0.0033196295],"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":[0.00063973345,0.00043590475,0.002190427,0.00026239175,0.00036450877,0.00010431108,0.00008820675,0.20244966,0.017346853,0.003220952,0.018453307,0.7544437],"study_design_scores_gemma":[0.000012813161,0.000034738754,0.00017229772,0.0000056163817,0.0000114780005,0.000025269394,0.0000062034187,0.99498475,0.0027946979,0.00086630456,0.001077324,0.0000084933445],"about_ca_topic_score_codex":0.0042125303,"about_ca_topic_score_gemma":0.010001359,"teacher_disagreement_score":0.004345412,"about_ca_system_score_codex":0.0005940463,"about_ca_system_score_gemma":0.0018902519,"threshold_uncertainty_score":0.014536858},"labels":[],"label_agreement":null},{"id":"W7117472363","doi":"10.3390/fluids11010006","title":"A Unified Drift–Flux Framework for Predictive Analysis of Flow Patterns and Void Fractions in Vertical Gas Lift Systems","year":2025,"lang":"en","type":"article","venue":"Fluids","topic":"Water Systems and 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":"Memorial University of Newfoundland","funders":"","keywords":"Porosity; Parameterized complexity; Parametric statistics; Void (composites); Lift (data mining); Inflection point; Computational fluid dynamics; Gas lift; Flow (mathematics)","score_opus":0.007186493267490645,"score_gpt":0.22832046639242934,"score_spread":0.22113397312493868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117472363","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":[0.015736353,0.00048474816,0.9812507,0.0001672275,0.0000344951,0.00003599798,0.00014131475,0.00034798405,0.001801289],"genre_scores_gemma":[0.8972496,0.0011899809,0.09649535,0.00018297114,0.000109927205,0.00037116717,0.0005281913,0.00020411516,0.0036686114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997795,0.000056598343,0.000012333734,0.000046934296,0.00007028962,0.00003437928],"domain_scores_gemma":[0.999635,0.00019634003,0.00004795998,0.000019556104,0.00008198396,0.000019251813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010805201,0.0014134962,0.0012489413,0.0011041095,0.00047827448,0.0012463529,0.0014549909,0.0013631391,0.0008822746],"category_scores_gemma":[0.0015117127,0.0006683605,0.0011878608,0.0005989187,0.0009178074,0.0012699896,0.0009286649,0.0011141245,0.00024351991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000102918575,0.000009946919,0.00026847195,0.000020043302,0.00001053566,0.000022749278,0.000010149162,0.9926704,0.0006596167,0.0032647185,0.0001102979,0.002942721],"study_design_scores_gemma":[5.805729e-7,0.0000022134222,0.000019165118,0.0000012220776,7.5076633e-7,0.0000012681784,8.1611967e-7,0.99931514,0.000050188315,0.0005476982,0.00005956192,0.0000013995964],"about_ca_topic_score_codex":0.015837688,"about_ca_topic_score_gemma":0.00651665,"teacher_disagreement_score":0.015837688,"about_ca_system_score_codex":0.00094298023,"about_ca_system_score_gemma":0.0015036523,"threshold_uncertainty_score":0.03149104},"labels":[],"label_agreement":null},{"id":"W7120433754","doi":"","title":"Partitioning methods of water supply network: a case study in Pederneiras/SP","year":2019,"lang":"pt","type":"dissertation","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Water Systems and 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); Limiting","score_opus":0.025238793138742686,"score_gpt":0.2770401837450556,"score_spread":0.25180139060631296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7120433754","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":[0.8813105,0.00063916965,0.09801029,0.00078358833,0.00006615626,0.0005411353,0.0006017834,0.00028359165,0.017763706],"genre_scores_gemma":[0.9323518,0.00036773307,0.06060511,0.000048727008,0.00001738688,0.00013503627,0.00035522092,0.00007093864,0.006048118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983877,0.0009117592,0.00006884666,0.00020007712,0.00025578224,0.00017592989],"domain_scores_gemma":[0.9975587,0.0013243064,0.00017829576,0.00023031964,0.00045773396,0.00025062505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018837883,0.00065650174,0.0004888395,0.0013186046,0.0016066724,0.0013165046,0.0011198256,0.0014909996,0.0027910434],"category_scores_gemma":[0.0043590367,0.00039919844,0.0007282291,0.0026924505,0.0007490002,0.001385761,0.0011535331,0.00059990224,0.0003467954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006758032,0.0008307319,0.03824942,0.0008341268,0.00017654154,0.0040881964,0.0041829087,0.7106119,0.008741833,0.019934252,0.005235589,0.20643863],"study_design_scores_gemma":[0.00013299663,0.00085836614,0.03544367,0.00014041887,0.00013778765,0.0009997165,0.013321072,0.89556146,0.009163509,0.011827995,0.03229368,0.000119313176],"about_ca_topic_score_codex":0.0533798,"about_ca_topic_score_gemma":0.08016199,"teacher_disagreement_score":0.0533798,"about_ca_system_score_codex":0.0025690028,"about_ca_system_score_gemma":0.0017350825,"threshold_uncertainty_score":0.10613817},"labels":[],"label_agreement":null},{"id":"W7125584018","doi":"10.1109/icsai68704.2025.11345843","title":"Employing Machine Learning and Deep Learning Techniques for Leak Detection Based on Infrared Images of Pipelines","year":2025,"lang":"","type":"article","venue":"","topic":"Water Systems and 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 Calgary; Dalhousie University","funders":"","keywords":"Deep learning; Leak detection; Pipeline transport; Leak; Pattern recognition (psychology); Object detection","score_opus":0.005588431059708399,"score_gpt":0.22428431632238857,"score_spread":0.21869588526268016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125584018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26820758,0.00054575136,0.7244474,0.00027898265,0.00009386468,0.00005804522,0.00037441912,0.0018550581,0.0041388646],"genre_scores_gemma":[0.8687585,0.00052685646,0.12708999,0.00009727572,0.000045697638,0.000025571475,0.00037667638,0.000085418906,0.0029940065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987113,0.000012724716,0.0000058164064,0.000027399938,0.00005411603,0.000028740862],"domain_scores_gemma":[0.99979526,0.00003521271,0.000048389225,0.000019782778,0.00008427713,0.00001709365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002750742,0.00066385197,0.00042066383,0.0009574267,0.00016470086,0.00056432147,0.00045758302,0.00052903,0.0009506204],"category_scores_gemma":[0.0007010875,0.00026200293,0.00043104845,0.0007412244,0.00029587193,0.0010399935,0.0005305348,0.00073515304,0.00039198142],"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":[0.00066692015,0.00029119485,0.0142536275,0.0004034482,0.00010070577,0.0004036083,0.00017454778,0.15698704,0.39803085,0.003004984,0.0047138873,0.42096925],"study_design_scores_gemma":[0.000010162914,0.000094654686,0.0072220187,0.000021311213,0.000030720123,0.00009836994,0.000055739958,0.9323674,0.057048928,0.0017595289,0.0012669206,0.000024333434],"about_ca_topic_score_codex":0.002409855,"about_ca_topic_score_gemma":0.0027647926,"teacher_disagreement_score":0.002409855,"about_ca_system_score_codex":0.00030673077,"about_ca_system_score_gemma":0.0005120219,"threshold_uncertainty_score":0.004791677},"labels":[],"label_agreement":null},{"id":"W7127932031","doi":"10.22260/crc-csce-2025/0063","title":"Investigating Sewer Corrosion Drivers Using Quantitative Modeling","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and 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":"University Grants Committee","keywords":"Joint (building); Sanitary sewer; Corrosion; Combined sewer","score_opus":0.03003625242986541,"score_gpt":0.24984896913441737,"score_spread":0.21981271670455196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127932031","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":[0.9029055,0.0000908492,0.081949875,0.00035224546,0.000017913955,0.00012246707,0.001010792,0.000357608,0.013192777],"genre_scores_gemma":[0.9941782,0.000038383187,0.004448534,0.0000059821123,0.0000018383524,0.00002677029,0.00022997275,0.00001993011,0.0010504454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953103,0.00016969287,0.00002337307,0.00006900548,0.0001542608,0.00005262317],"domain_scores_gemma":[0.9982008,0.0010723544,0.00015420352,0.000108167595,0.00043321104,0.000031151198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012476369,0.00058177527,0.0003691449,0.0010536781,0.0002754813,0.0016802961,0.00075800956,0.00077778724,0.0015115264],"category_scores_gemma":[0.0036887657,0.0005283547,0.00070453045,0.0008891112,0.0002825756,0.001254705,0.0004204842,0.0005312116,0.00020266729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044406483,0.00013591182,0.010405061,0.000053628126,0.000038615544,0.00003686545,0.00006254093,0.9733146,0.0026451498,0.006861766,0.0003870213,0.0060143624],"study_design_scores_gemma":[0.0000030678277,0.000018895806,0.0011565512,0.0000017320388,0.0000041793023,0.0000030001613,0.000032372773,0.99720985,0.0006307538,0.0007163908,0.00021837947,0.000004859694],"about_ca_topic_score_codex":0.04512682,"about_ca_topic_score_gemma":0.03695989,"teacher_disagreement_score":0.04512682,"about_ca_system_score_codex":0.0020744326,"about_ca_system_score_gemma":0.0015086462,"threshold_uncertainty_score":0.089728296},"labels":[],"label_agreement":null},{"id":"W7132137355","doi":"","title":"Prone to fail","year":2000,"lang":"en","type":"article","venue":"NPARC","topic":"Water Systems and 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":"Pipeline (software); Order (exchange); Water pipe; Work (physics); Subject matter; Catastrophic failure; Lead (geology)","score_opus":0.005303048112626742,"score_gpt":0.17012452043797657,"score_spread":0.16482147232534983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132137355","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":[0.621793,0.007123036,0.109473504,0.015236936,0.0026695405,0.0007001143,0.0057329247,0.0046063103,0.23266466],"genre_scores_gemma":[0.9128863,0.0030736877,0.013799789,0.003930656,0.00048506304,0.0003017969,0.003974142,0.00042419537,0.061124302],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987017,0.00016744046,0.00008062656,0.00023264038,0.00046911227,0.00034849928],"domain_scores_gemma":[0.99337995,0.0018325172,0.0012156579,0.0010839708,0.0014978852,0.000990011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018290057,0.0007351615,0.0008090802,0.0011127414,0.0016367673,0.0023135054,0.0013661257,0.0016036395,0.030721962],"category_scores_gemma":[0.009391871,0.0003800153,0.000848899,0.0008969261,0.00092930655,0.0022068615,0.002093937,0.0015912706,0.012464272],"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":[0.00096051337,0.0007245669,0.13259843,0.0010383832,0.00046524525,0.0068185334,0.0033243764,0.057876453,0.012708541,0.13752937,0.19674489,0.44921073],"study_design_scores_gemma":[0.00016578143,0.0018343,0.084909014,0.0006787833,0.0004372218,0.011918465,0.0061176377,0.06796893,0.010007613,0.37814265,0.43749237,0.0003272514],"about_ca_topic_score_codex":0.0010167146,"about_ca_topic_score_gemma":0.0011366252,"teacher_disagreement_score":0.030721962,"about_ca_system_score_codex":0.0006005438,"about_ca_system_score_gemma":0.000908804,"threshold_uncertainty_score":0.102775216},"labels":[],"label_agreement":null},{"id":"W7132863545","doi":"","title":"Design and Implementation of a Micro-world Simulation Platform for Condition-based Maintenance using Machine Learning Algorithms","year":2020,"lang":"","type":"dissertation","venue":"TSpace","topic":"Water Systems and 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":"Process (computing); Reliability (semiconductor); Decision support system; Hydraulic machinery; Set (abstract data type); Predictive maintenance","score_opus":0.03230280434188083,"score_gpt":0.3280597382881768,"score_spread":0.295756933946296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132863545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023344498,0.000047545993,0.9576241,0.00018285595,0.000085501546,0.0005290789,0.0003210688,0.011167074,0.006698228],"genre_scores_gemma":[0.32912943,0.00017219494,0.6606623,0.00013826371,0.000026876083,0.0013134244,0.0013545525,0.0012972044,0.00590577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994147,0.00014904076,0.00005277575,0.000114386035,0.00020723048,0.000061895],"domain_scores_gemma":[0.9985676,0.00071909843,0.00008230897,0.00017617535,0.00033583323,0.0001190953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014648275,0.00077828806,0.000584697,0.0005989119,0.00036386176,0.0017359597,0.0025049413,0.0008738259,0.010442839],"category_scores_gemma":[0.0030729878,0.00063701026,0.0006627265,0.00036429736,0.0005230408,0.0012526333,0.0010396556,0.0015548726,0.0020831402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037528021,0.0004921988,0.0032044195,0.00025176196,0.000103706116,0.00020384647,0.00038252323,0.87355316,0.01663857,0.018555775,0.004743025,0.08149561],"study_design_scores_gemma":[0.000035056135,0.000044594064,0.00012424614,0.000009267873,0.000008878569,0.000010796798,0.000016645128,0.98913443,0.004816203,0.001259843,0.004530638,0.000009398911],"about_ca_topic_score_codex":0.00326014,"about_ca_topic_score_gemma":0.0023702686,"teacher_disagreement_score":0.010442839,"about_ca_system_score_codex":0.00076389266,"about_ca_system_score_gemma":0.0014009465,"threshold_uncertainty_score":0.03493482},"labels":[],"label_agreement":null},{"id":"W7132992453","doi":"","title":"Exploring the Value of Infrastructure Systems and its Impacts on Decision-Making for Sustainability","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Water Systems and 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; Mitacs; University of Toronto","keywords":"Sustainability; Context (archaeology); Value (mathematics); Critical infrastructure; Position (finance); Value capture; Sustainable Value; Conceptual framework","score_opus":0.020972654411468727,"score_gpt":0.30437670040607867,"score_spread":0.28340404599460994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132992453","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":[0.2677489,0.009690814,0.25450993,0.0449483,0.00044889702,0.00020429915,0.00046910654,0.000099847595,0.42188],"genre_scores_gemma":[0.9766708,0.0038745692,0.015453027,0.00027493315,0.0000687803,0.00008218589,0.000051442537,0.00003302196,0.0034910804],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970392,0.0019678255,0.00006760201,0.0001935932,0.00037269434,0.00035903382],"domain_scores_gemma":[0.99007165,0.008229421,0.00054426,0.0003298869,0.00046998126,0.00035486283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003653625,0.00065506983,0.00059756485,0.0010281692,0.001881705,0.00911791,0.0012342193,0.0024338875,0.00730151],"category_scores_gemma":[0.010939798,0.00043006038,0.00095116213,0.0018155253,0.006703638,0.007979955,0.00459841,0.0033248318,0.0003798065],"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":[0.00002006681,0.00003593572,0.0016457168,0.00014058215,0.000034792443,0.00011179054,0.0006905292,0.0698747,0.00026009188,0.91639674,0.0008337812,0.009955315],"study_design_scores_gemma":[0.000007061687,0.000040949013,0.0012311945,0.00028990148,0.000023625813,0.000053409516,0.0018915872,0.060541887,0.0003127145,0.92259103,0.012987547,0.000029058529],"about_ca_topic_score_codex":0.005706248,"about_ca_topic_score_gemma":0.007023562,"teacher_disagreement_score":0.00911791,"about_ca_system_score_codex":0.0064212964,"about_ca_system_score_gemma":0.0037389423,"threshold_uncertainty_score":0.04658997},"labels":[],"label_agreement":null},{"id":"W7133034682","doi":"","title":"Development of an asset management plan for municipal water infrastructure","year":2007,"lang":"","type":"dissertation","venue":"TSpace","topic":"Water Systems and 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 Waterloo; Canadian Water Network","keywords":"Asset management; Asset (computer security); Wastewater; Grading (engineering); Plan (archaeology); Rail network; Hydraulic structure; Sanitary sewer","score_opus":0.016268571238951725,"score_gpt":0.2996317382250423,"score_spread":0.2833631669860906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133034682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09695301,0.0003370915,0.83056456,0.0038747394,0.00007561971,0.0037870067,0.0011393673,0.0012577272,0.062010985],"genre_scores_gemma":[0.40822628,0.00039128226,0.57590497,0.00009885657,0.000022284294,0.00158284,0.0013569486,0.00004295455,0.012373581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.999463,0.00019954715,0.000030611645,0.00006172033,0.00015624607,0.00008885745],"domain_scores_gemma":[0.9995467,0.00007764442,0.000082572325,0.000033268825,0.00020038926,0.000059389375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011958156,0.00044235,0.000304762,0.001648556,0.0007869809,0.0016147908,0.00077792065,0.00095107016,0.006434961],"category_scores_gemma":[0.0021726824,0.000306166,0.00030955512,0.00090098346,0.0003057591,0.0012563666,0.00080963207,0.0006861031,0.0006010689],"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":[0.00006978641,0.00030475957,0.0057025678,0.00017285171,0.00003745132,0.00046904685,0.00048476583,0.5249541,0.0051773638,0.13727944,0.017596405,0.30775145],"study_design_scores_gemma":[0.000071212904,0.00036373138,0.0073205116,0.0002301332,0.000043526263,0.0001334613,0.002178933,0.85876244,0.0045566224,0.08564041,0.04064752,0.000051524334],"about_ca_topic_score_codex":0.008411808,"about_ca_topic_score_gemma":0.014252701,"teacher_disagreement_score":0.008411808,"about_ca_system_score_codex":0.0021953438,"about_ca_system_score_gemma":0.0071786894,"threshold_uncertainty_score":0.021527052},"labels":[],"label_agreement":null},{"id":"W7133081289","doi":"","title":"Linking water quality and hydraulics in distribution systems through a transient one-dimensional multi-component corrosion model","year":2006,"lang":"","type":"dissertation","venue":"TSpace","topic":"Water Systems and 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":"Corrosion; Water quality; Hydraulics; Transient (computer programming); Ferrous; Quality (philosophy); Groundwater; Ferric","score_opus":0.041375641000967446,"score_gpt":0.2965340154607619,"score_spread":0.25515837445979445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133081289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24024554,0.00066242076,0.72749823,0.00094255793,0.00009890406,0.00013028832,0.00036672715,0.00052601926,0.029529324],"genre_scores_gemma":[0.97066754,0.00035223685,0.018553713,0.00006938422,0.000024524274,0.00010134684,0.000108330794,0.000057702913,0.010065309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998696,0.000043783948,0.000005372654,0.000024680856,0.000031367912,0.000025225747],"domain_scores_gemma":[0.9997682,0.00012376915,0.000031568914,0.000014172776,0.000044026743,0.000018307539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027544922,0.00054704375,0.0007516648,0.0003710036,0.0003616154,0.00077491126,0.0008561413,0.0013176987,0.001876014],"category_scores_gemma":[0.00070806866,0.00039942225,0.00064582523,0.00036339273,0.0007942863,0.0007542824,0.00068052387,0.00089892425,0.00022123258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009673526,0.0000090354315,0.00013204874,0.000012296194,0.0000028996371,0.000018035373,0.000012357829,0.9965178,0.0005571322,0.0019822135,0.00006922895,0.0006773451],"study_design_scores_gemma":[0.0000024330698,0.0000056971694,0.000037133952,8.250738e-7,0.0000014952357,0.0000024536916,0.0000036392887,0.9994875,0.00008871822,0.000262564,0.000106019805,0.0000014141359],"about_ca_topic_score_codex":0.017438399,"about_ca_topic_score_gemma":0.008416264,"teacher_disagreement_score":0.017438399,"about_ca_system_score_codex":0.0010626875,"about_ca_system_score_gemma":0.00086904736,"threshold_uncertainty_score":0.03467381},"labels":[],"label_agreement":null},{"id":"W7161841333","doi":"10.82308/38735","title":"GIS-inventory and condition rating of water supply system at McGill Downtown Campus","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Water Systems and 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":"Water supply; Downtown; Rating system; Work (physics); Geographic information system; Supply and demand","score_opus":0.005965128141926655,"score_gpt":0.1789974258939531,"score_spread":0.17303229775202644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161841333","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":[0.39957774,0.0007398256,0.22309993,0.0015162107,0.00010160662,0.0049282494,0.15290675,0.026988393,0.1901413],"genre_scores_gemma":[0.5953614,0.00077685266,0.27848336,0.00012365088,0.000017067507,0.0010053562,0.062069792,0.0008686127,0.06129391],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988341,0.000106502506,0.000065301545,0.00015829965,0.0006948244,0.00014105307],"domain_scores_gemma":[0.99813724,0.00014342858,0.00014669915,0.00020692869,0.0011193593,0.00024637312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007060442,0.0006818729,0.00039236125,0.004115595,0.0008492788,0.0023403107,0.0012019755,0.00024407731,0.011745907],"category_scores_gemma":[0.0018839604,0.00045882017,0.00030576315,0.0059226183,0.0004991995,0.0010116979,0.001012944,0.00035507334,0.0022886458],"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":[0.0003146684,0.0002773719,0.1956557,0.0007775906,0.00010528895,0.00093698595,0.0037479403,0.12289511,0.0261155,0.023854833,0.108764224,0.5165548],"study_design_scores_gemma":[0.000055722674,0.000254438,0.33341578,0.00032055692,0.00008916608,0.00038972747,0.006920643,0.2599476,0.03466563,0.004103279,0.35948697,0.00035048943],"about_ca_topic_score_codex":0.78464705,"about_ca_topic_score_gemma":0.8319729,"teacher_disagreement_score":0.78464705,"about_ca_system_score_codex":0.010605685,"about_ca_system_score_gemma":0.011707759,"threshold_uncertainty_score":0.43324238},"labels":[],"label_agreement":null},{"id":"W7161961436","doi":"10.82308/11968","title":"Machine learning paradigms for deterioration modeling of water distribution infrastructures under climatic and environmental conditions","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Water Systems and 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":"Distribution (mathematics); Population","score_opus":0.00578418460629789,"score_gpt":0.20147413338948286,"score_spread":0.19568994878318496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161961436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034444198,0.0021235573,0.9560125,0.0009967783,0.00013976915,0.00009773152,0.00031090694,0.00030790898,0.0055666496],"genre_scores_gemma":[0.85292673,0.002806796,0.13253082,0.00037772898,0.00027535588,0.0005193744,0.00064888626,0.000090562666,0.00982369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942696,0.00021204344,0.000039128263,0.00013711918,0.000109376626,0.00007544594],"domain_scores_gemma":[0.99847955,0.00092270295,0.00020425116,0.000058639213,0.00027440707,0.000060559443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001936572,0.0014109651,0.001174858,0.0010966088,0.0005442278,0.0019350133,0.002486959,0.002273505,0.00272101],"category_scores_gemma":[0.004405152,0.0006321203,0.0015927812,0.0009940119,0.0006346926,0.0015002633,0.0012733939,0.002477449,0.00052768283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032308068,0.000044391538,0.0014148575,0.00007709588,0.000061185696,0.00006507087,0.000051214014,0.968947,0.00040231764,0.008677891,0.00048468853,0.019741992],"study_design_scores_gemma":[0.0000020059276,0.0000089911955,0.00010848675,0.0000064853302,0.0000051176576,0.000006556279,0.0000050447006,0.9954195,0.00005491663,0.0041223643,0.00025772347,0.0000029040505],"about_ca_topic_score_codex":0.012703609,"about_ca_topic_score_gemma":0.008618774,"teacher_disagreement_score":0.012703609,"about_ca_system_score_codex":0.001670732,"about_ca_system_score_gemma":0.0012662483,"threshold_uncertainty_score":0.025259316},"labels":[],"label_agreement":null},{"id":"W765562879","doi":"10.26636/jtit.2012.3.1283","title":"Optimal Pump Scheduling for Large Scale Water Transmission System by Linear Programming","year":2012,"lang":"en","type":"article","venue":"Journal of Telecommunications and Information Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":11,"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; Time horizon; Nonlinear system; Scheduling (production processes); Transmission system; Linear programming; Transmission (telecommunications); Mathematical optimization; Water supply; Scale (ratio); Environmental science; Environmental engineering; Telecommunications; Mathematics","score_opus":0.005460131801136458,"score_gpt":0.21165416918807334,"score_spread":0.20619403738693687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W765562879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13703215,0.00083348254,0.8513921,0.00054679473,0.000067496796,0.0002709245,0.0006015735,0.0004557879,0.008799675],"genre_scores_gemma":[0.9266908,0.00060256035,0.0644509,0.000090402296,0.000056038352,0.00039052172,0.00039750067,0.00012141222,0.0071998946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995882,0.00019875029,0.000010990546,0.000056571756,0.000056979796,0.00008858528],"domain_scores_gemma":[0.99926406,0.00051176583,0.000090644506,0.000020982496,0.000055385153,0.000057092388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008661291,0.001070647,0.0015729108,0.0004747341,0.00044890147,0.0013470114,0.00084393047,0.00085597363,0.004182395],"category_scores_gemma":[0.0014469292,0.0008483738,0.0006720857,0.0008682511,0.0006281685,0.0008911926,0.0006514858,0.00088077073,0.0003079714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014681465,0.00000877813,0.000039259634,0.000015768257,0.0000050990607,0.000008121138,0.000004730432,0.99789715,0.00015079683,0.0006195002,0.00014135739,0.0010947094],"study_design_scores_gemma":[0.000006932191,0.000012672993,0.000027970455,8.6244825e-7,0.0000019259107,0.0000011496695,0.0000034990337,0.9993888,0.00004495435,0.00043229412,0.000077549215,0.00000130941],"about_ca_topic_score_codex":0.017445354,"about_ca_topic_score_gemma":0.012702725,"teacher_disagreement_score":0.017445354,"about_ca_system_score_codex":0.001680196,"about_ca_system_score_gemma":0.001998343,"threshold_uncertainty_score":0.03468764},"labels":[],"label_agreement":null},{"id":"W776315790","doi":"10.1016/b978-0-12-801811-8.00006-7","title":"Pressure and Flow Test Data","year":2015,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Water Systems and 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 Calgary","funders":"","keywords":"Hydrostatic test; Transient (computer programming); Pressure measurement; Flow (mathematics); Test data; Pulse pressure; Mechanics; Computer science; Engineering; Mechanical engineering; Physics","score_opus":0.02919172369175018,"score_gpt":0.21368385122769867,"score_spread":0.18449212753594849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W776315790","genre_codex":"other","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024068918,0.0029813088,0.07935008,0.000929047,0.0013637794,0.00040647062,0.19373639,0.017600229,0.6795638],"genre_scores_gemma":[0.13085283,0.0034134274,0.04014551,0.00050983805,0.00041690416,0.00061146123,0.18130778,0.0042309365,0.63851136],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991509,0.000044473825,0.000056047862,0.00010316411,0.00060745864,0.00003787458],"domain_scores_gemma":[0.99913603,0.00021097694,0.00003877254,0.0002579088,0.00032237094,0.000033852088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067383656,0.0010064245,0.0007816506,0.003029943,0.00048780808,0.0013923137,0.0014706623,0.0007770468,0.12714826],"category_scores_gemma":[0.0023465948,0.00046265914,0.00046333883,0.0038107687,0.0004713327,0.0015688116,0.00078459526,0.0008674862,0.067918636],"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":[0.0003018279,0.00019819102,0.0038590187,0.0005807455,0.000025535737,0.00018829545,0.00013871146,0.0057780463,0.013987954,0.010147451,0.3424619,0.62233233],"study_design_scores_gemma":[0.00004710477,0.00019562463,0.01969111,0.00021372458,0.000039260714,0.0005705897,0.00013040859,0.0138623705,0.052374594,0.009997411,0.90275687,0.00012097443],"about_ca_topic_score_codex":0.0046828804,"about_ca_topic_score_gemma":0.005679863,"teacher_disagreement_score":0.12714826,"about_ca_system_score_codex":0.00042085935,"about_ca_system_score_gemma":0.00075807126,"threshold_uncertainty_score":0.4253533},"labels":[],"label_agreement":null},{"id":"W794074179","doi":"10.1002/j.1551-8833.2007.tb08057.x","title":"Setting the course for a brighter future","year":2007,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Systems and 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":"Workforce; Business; Sustainability; Water industry; Service (business); Water supply; Marketing; Finance; Engineering; Economic growth; Economics","score_opus":0.0019123890283878325,"score_gpt":0.190863306471714,"score_spread":0.18895091744332618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W794074179","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":[0.0017610211,0.0115223965,0.0023800714,0.89568484,0.04709813,0.000074965974,0.00023146531,0.00034362325,0.040903464],"genre_scores_gemma":[0.067771,0.026081434,0.019901698,0.6539808,0.022574618,0.00044408088,0.00088906894,0.0006996167,0.20765767],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99183017,0.0027289023,0.00026662188,0.0007439144,0.0019208286,0.002509574],"domain_scores_gemma":[0.9753076,0.0020697953,0.00061227544,0.0008123156,0.005069556,0.016128527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012767581,0.0012339348,0.0012736183,0.0018042773,0.011374491,0.022123754,0.003131656,0.015495367,0.093520954],"category_scores_gemma":[0.019417213,0.0006721847,0.0012307621,0.00092470704,0.006868909,0.030715879,0.018915532,0.023947664,0.03837251],"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":[0.000036883157,0.00011434728,0.00047269708,0.00025869138,0.000014051996,0.00027942494,0.0016083567,0.00008863248,0.000493487,0.048338097,0.9044787,0.043816544],"study_design_scores_gemma":[0.000012249337,0.000056234858,0.000481677,0.0004647728,0.000005641628,0.00011844152,0.008360619,0.00007777399,0.00015182643,0.020809565,0.96943,0.000031095613],"about_ca_topic_score_codex":0.0030816684,"about_ca_topic_score_gemma":0.0066111856,"teacher_disagreement_score":0.093520954,"about_ca_system_score_codex":0.0055731405,"about_ca_system_score_gemma":0.020614348,"threshold_uncertainty_score":0.31285876},"labels":[],"label_agreement":null},{"id":"W823758931","doi":"10.2166/aqua.2015.148","title":"Impact of incomplete mixing in the prediction of chlorine residuals in municipal water distribution systems","year":2015,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and 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":true,"ca_institutions":"Université Laval","funders":"Universidad Nacional Autónoma de México","keywords":"Mixing (physics); Chlorine; Distribution (mathematics); Environmental science; Statistics; Mathematics; Biological system; Chemistry; Physics; Mathematical analysis; Biology","score_opus":0.0479237604676048,"score_gpt":0.29439904452602705,"score_spread":0.24647528405842226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W823758931","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":[0.99688333,0.000026431379,0.0028166692,0.000018506089,0.000001316207,0.000005176151,0.00003389014,0.000031089057,0.00018356605],"genre_scores_gemma":[0.9995901,0.000008145156,0.00033636557,0.0000012553554,4.3049013e-7,0.0000014933028,0.000020315028,0.0000017632564,0.000040187217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958044,0.00014348945,0.000029947196,0.000085515596,0.00008613173,0.00007451039],"domain_scores_gemma":[0.9984642,0.0009768897,0.00023408153,0.00007754111,0.00015344899,0.000093766066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009522421,0.0004547145,0.00052387506,0.00059882255,0.0004242045,0.00083380373,0.00036548232,0.000631405,0.00028253737],"category_scores_gemma":[0.0035275996,0.00025674095,0.00035487692,0.00028403036,0.0003769892,0.0007076467,0.0005527309,0.00040532585,0.000043069627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008749132,0.00003893286,0.038290042,0.000011679411,0.000020495818,0.000068119974,0.000030556126,0.95618737,0.00245732,0.00015561267,0.000022005499,0.0026304303],"study_design_scores_gemma":[0.0000051245484,0.000062837986,0.0073224436,0.0000021540727,0.000006221133,0.000006364662,0.000028650637,0.9903886,0.002062483,0.00008245935,0.000028378296,0.0000041961507],"about_ca_topic_score_codex":0.043658808,"about_ca_topic_score_gemma":0.026545277,"teacher_disagreement_score":0.043658808,"about_ca_system_score_codex":0.0011121681,"about_ca_system_score_gemma":0.0010472898,"threshold_uncertainty_score":0.08680934},"labels":[],"label_agreement":null},{"id":"W90516534","doi":"10.1007/978-1-4020-6027-4_24","title":"Sewerage Infrastructure: Fuzzy Techniques to Manage Failures","year":2007,"lang":"en","type":"book-chapter","venue":"NATO science for peace and security series","topic":"Water Systems and 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":"National Research Council Canada","funders":"","keywords":"Sewerage; Replicate; Robustness (evolution); Fuzzy logic; Computer science; Flexibility (engineering); Operations research; Engineering; Artificial intelligence; Mathematics; Environmental engineering; Statistics","score_opus":0.008251747734274341,"score_gpt":0.2234363419959382,"score_spread":0.21518459426166386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W90516534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010227597,0.0011658714,0.97224605,0.00036087917,0.00009379147,0.000031494983,0.000068076086,0.0005612583,0.015244999],"genre_scores_gemma":[0.46050432,0.0029668184,0.50169003,0.00016588127,0.00019667363,0.000104376646,0.00017597666,0.00014009334,0.034055855],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998572,0.000017074048,0.000007861507,0.000026212896,0.000076613236,0.000015002325],"domain_scores_gemma":[0.9998522,0.000049456437,0.000014655072,0.000019386303,0.000056404104,0.000007932894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003179047,0.00047414887,0.0003794229,0.00056806154,0.000446459,0.00084446254,0.0012063001,0.0007591253,0.0036812432],"category_scores_gemma":[0.0007237842,0.00018901628,0.00036687066,0.0008810871,0.00043797627,0.0013776611,0.00042908147,0.00079531485,0.00051356497],"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":[0.000108115004,0.00006998167,0.00043885698,0.0002621406,0.00004955174,0.00008453476,0.0002490194,0.24771853,0.014410609,0.10404975,0.012463428,0.62009555],"study_design_scores_gemma":[0.00001871881,0.00007870743,0.0007871708,0.00006500337,0.000052988722,0.00011306115,0.00011728603,0.86209905,0.009759102,0.08635733,0.040520933,0.000030665677],"about_ca_topic_score_codex":0.005933434,"about_ca_topic_score_gemma":0.009840151,"teacher_disagreement_score":0.005933434,"about_ca_system_score_codex":0.0006735409,"about_ca_system_score_gemma":0.00056327897,"threshold_uncertainty_score":0.012314975},"labels":[],"label_agreement":null},{"id":"W956403614","doi":"10.14796/jwmm.r245-18","title":"Comparison of Different Methods in Calculating CSS Percent Capture","year":2012,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Computer science; Statistics; Mathematics","score_opus":0.041543624225614785,"score_gpt":0.320453731184427,"score_spread":0.2789101069588122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W956403614","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":[0.3454746,0.0064436025,0.62742126,0.00027130195,0.00032451557,0.000504311,0.0029404084,0.0034977142,0.013122271],"genre_scores_gemma":[0.5303732,0.002073294,0.4592707,0.00012208014,0.00006789807,0.00050055573,0.0023322501,0.0007031172,0.004556928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99546045,0.0015336447,0.00033888844,0.00052885164,0.001936915,0.00020123123],"domain_scores_gemma":[0.9879574,0.0074062743,0.0005707292,0.0007169752,0.0032557244,0.00009282929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070785466,0.0009789659,0.0008137973,0.0046450854,0.0005097723,0.0011313084,0.0017531669,0.0009785636,0.0023058008],"category_scores_gemma":[0.014088881,0.0005289877,0.0010929127,0.0038066672,0.00026363868,0.00090019614,0.00075406174,0.0005162814,0.0009598205],"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":[0.00266535,0.00043839167,0.092858605,0.0018277542,0.0013618462,0.00013203904,0.00054178614,0.10587737,0.03142717,0.0031006665,0.003658982,0.75611],"study_design_scores_gemma":[0.0002281716,0.0009492905,0.13712245,0.0002812542,0.000938089,0.00042284117,0.00049825624,0.7128835,0.13012302,0.002447651,0.013743,0.00036255032],"about_ca_topic_score_codex":0.012339911,"about_ca_topic_score_gemma":0.020144114,"teacher_disagreement_score":0.012339911,"about_ca_system_score_codex":0.0009951342,"about_ca_system_score_gemma":0.0006119002,"threshold_uncertainty_score":0.037435353},"labels":[],"label_agreement":null},{"id":"W96978107","doi":"10.2166/aqua.2005.069","title":"Translation of pipe inspection results into condition ratings using the fuzzy synthetic evaluation technique","year":2006,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and Optimization","field":"Engineering","cited_by":51,"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":"Research council; Icon; Library science; Computer science; Government (linguistics)","score_opus":0.02670336061526972,"score_gpt":0.29306480125383505,"score_spread":0.2663614406385653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W96978107","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":[0.19077273,0.000529103,0.77014226,0.00053236587,0.00038317675,0.001729645,0.0017032003,0.0013393719,0.032868154],"genre_scores_gemma":[0.6300158,0.00037790413,0.36295784,0.000058572954,0.00006191666,0.0008054777,0.0014113041,0.00014445015,0.0041667894],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99483734,0.0022492767,0.000474346,0.0002851394,0.0020633203,0.000090540016],"domain_scores_gemma":[0.9887411,0.0039519663,0.00095141295,0.0009486433,0.005241697,0.0001651921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005856056,0.0008055837,0.0005264209,0.0029715693,0.00042853534,0.001983135,0.0004863166,0.0004007996,0.005741668],"category_scores_gemma":[0.027306166,0.0002401952,0.00083542656,0.0022134958,0.00062593253,0.0012515063,0.00075109093,0.00062957674,0.0012338914],"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":[0.002105118,0.00050883845,0.011196454,0.0014281197,0.00024232778,0.0004832889,0.0031862408,0.046021912,0.03751322,0.018464789,0.009767877,0.8690818],"study_design_scores_gemma":[0.0004329771,0.004251008,0.08716395,0.0007779257,0.00041858622,0.0010253657,0.008320978,0.74711263,0.059630368,0.04229857,0.04806109,0.0005066135],"about_ca_topic_score_codex":0.0033980506,"about_ca_topic_score_gemma":0.002978841,"teacher_disagreement_score":0.005856056,"about_ca_system_score_codex":0.0012305874,"about_ca_system_score_gemma":0.0010426949,"threshold_uncertainty_score":0.030970156},"labels":[],"label_agreement":null},{"id":"W972640662","doi":"10.14796/jwmm.r245-04","title":"Pressure Surges Following Sudden Air Pocket Entrapment in Stormwater Systems","year":2012,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Systems and 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":"Stormwater; Entrapment; Environmental science; Stormwater management; Surface runoff; Medicine","score_opus":0.010537776601160894,"score_gpt":0.18928853788680478,"score_spread":0.17875076128564388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W972640662","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":[0.99663615,0.000048439026,0.0013968531,0.000077726305,0.00001975366,0.000023911234,0.00018518687,0.000070311544,0.0015416596],"genre_scores_gemma":[0.9996581,0.000014780405,0.000089724366,0.0000028035856,0.0000038777584,0.0000032489302,0.00007965188,0.0000019924928,0.00014568842],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998629,0.000016385193,0.00000948351,0.0000146409675,0.00004450807,0.000052047857],"domain_scores_gemma":[0.9994498,0.00015440618,0.00017367242,0.00002406809,0.000103359955,0.00009460036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002614173,0.00015075345,0.0002749308,0.0004498938,0.0003367932,0.00049422245,0.00022442298,0.00040131152,0.002915847],"category_scores_gemma":[0.0017112079,0.00016101493,0.00017381506,0.0004326989,0.00031727215,0.00041702608,0.00052956626,0.00039582208,0.0003044725],"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":[0.0044666626,0.0008698173,0.5145856,0.00054452673,0.00025393727,0.015436933,0.0015419526,0.28790966,0.06619318,0.0054091485,0.007986766,0.0948018],"study_design_scores_gemma":[0.000090114976,0.0011735294,0.64717454,0.00004867271,0.00005908346,0.0009213519,0.0017734863,0.33800045,0.0070179575,0.002421006,0.0012677726,0.000051991807],"about_ca_topic_score_codex":0.007192232,"about_ca_topic_score_gemma":0.006399369,"teacher_disagreement_score":0.007192232,"about_ca_system_score_codex":0.0003738815,"about_ca_system_score_gemma":0.00029690503,"threshold_uncertainty_score":0.014300704},"labels":[],"label_agreement":null}]}