{"meta":{"query_hash":"fa96e9c22652","filters":{"venue":"WDSA / CCWI Joint Conference Proceedings"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"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/fa96e9c22652","api":"https://metacan.xera.ac/api/v1/cohort?venue=WDSA+%2F+CCWI+Joint+Conference+Proceedings"},"results":[{"id":"W2951428172","doi":"","title":"Three-Dimensional Simulation of Hydrodynamics and Water Quality in a Wastewater Stabilization Pond:","year":2018,"lang":"en","type":"article","venue":"WDSA / CCWI Joint Conference Proceedings","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","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":"Alkalinity; Environmental science; Wastewater; Biogeochemical cycle; Water quality; Environmental engineering; Sewage treatment; Stabilization pond; Algae; Hydrology (agriculture); Ecology; Environmental chemistry; Chemistry; Engineering","score_opus":0.0243993136367882,"score_gpt":0.24739478077584456,"score_spread":0.22299546713905635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951428172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9868879,0.000024705672,0.00871963,0.00011770118,0.000012381597,0.0000623914,0.00064013596,0.00016112564,0.0033741517],"genre_scores_gemma":[0.9952389,0.00003202717,0.0033294589,0.000018561075,0.0000025611046,0.0000728328,0.00028828302,0.000011298044,0.0010059235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988794,0.000020648968,0.000007368292,0.0000293406,0.000023360932,0.000031301162],"domain_scores_gemma":[0.99977666,0.00009073443,0.000030477097,0.000019911724,0.000048285667,0.000033979435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021708381,0.00031861832,0.0003678558,0.00025330656,0.00052539,0.00077715935,0.00064127124,0.0011894776,0.0011391671],"category_scores_gemma":[0.0006732406,0.00028888506,0.0005870199,0.00039961803,0.0006714406,0.000388504,0.00049843563,0.00052027794,0.00012557294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055826782,0.000054026776,0.0050097387,0.000017073557,0.000010467937,0.000052301162,0.00003958251,0.99015725,0.0032231493,0.0002379655,0.00010564178,0.001037003],"study_design_scores_gemma":[0.00003202923,0.000021949596,0.0019169133,0.0000012173891,0.0000039088977,0.000005141026,0.00002019719,0.9970386,0.0007621948,0.00007726612,0.00011501414,0.000005500928],"about_ca_topic_score_codex":0.11002147,"about_ca_topic_score_gemma":0.049284667,"teacher_disagreement_score":0.11002147,"about_ca_system_score_codex":0.0022991344,"about_ca_system_score_gemma":0.0018720718,"threshold_uncertainty_score":0.21876216},"labels":[],"label_agreement":null},{"id":"W2965775619","doi":"","title":"Physical Modelling of an Hydropower Generation Station and Simulating Turbine Energy Losses","year":2018,"lang":"en","type":"article","venue":"WDSA / CCWI Joint Conference Proceedings","topic":"Cavitation Phenomena in Pumps","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":"Hydropower; Inflow; Turbine; Inlet; Marine engineering; Spillway; Draft tube; Renewable energy; Electricity generation; Environmental science; Francis turbine; Engineering; Flow (mathematics); Meteorology; Power (physics); Geotechnical engineering; Mechanical engineering; Mechanics","score_opus":0.038533250236588476,"score_gpt":0.2433751780351169,"score_spread":0.20484192779852844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965775619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57131296,0.00036880735,0.3248885,0.0008936926,0.00015027389,0.000542968,0.002524129,0.0012173863,0.09810128],"genre_scores_gemma":[0.9714902,0.00025866381,0.012462657,0.000042248786,0.00001684912,0.00020318368,0.0003346157,0.00009066161,0.015100878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980956,0.000038714075,0.000011363523,0.000039666298,0.00006268921,0.00003801187],"domain_scores_gemma":[0.9997781,0.000083832085,0.000030475338,0.000032082215,0.000052018782,0.000023487926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019587741,0.0006274673,0.0005119921,0.0004208333,0.0007812525,0.0018018084,0.0013461091,0.0013560692,0.0049151652],"category_scores_gemma":[0.00062701775,0.0007211122,0.0006102685,0.0006941583,0.0008646927,0.0013129045,0.00064927543,0.00070114166,0.00067472016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015996502,0.000021272594,0.0008708568,0.000017401222,0.0000053606054,0.00007025719,0.000044503984,0.9937644,0.0016610856,0.0014871144,0.00028785702,0.0017537335],"study_design_scores_gemma":[0.000015735253,0.000030038202,0.0012453,0.0000052157957,0.0000072304506,0.000025040574,0.00005146579,0.996082,0.00051406166,0.0007103854,0.0013030671,0.000010634603],"about_ca_topic_score_codex":0.043281794,"about_ca_topic_score_gemma":0.027443768,"teacher_disagreement_score":0.043281794,"about_ca_system_score_codex":0.0015757461,"about_ca_system_score_gemma":0.0018894442,"threshold_uncertainty_score":0.08605975},"labels":[],"label_agreement":null},{"id":"W2968818053","doi":"","title":"Performance Modelling of Etobicoke Exfiltration System (EES)","year":2018,"lang":"en","type":"article","venue":"WDSA / CCWI Joint Conference Proceedings","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","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":"Stormwater; Sanitary sewer; Environmental science; Combined sewer; Storm Water Management Model; Storm; Surface runoff; Stormwater management; Low-impact development; Hydrology (agriculture); Civil engineering; Environmental engineering; Engineering; Meteorology","score_opus":0.04894755799927145,"score_gpt":0.20049376789725423,"score_spread":0.1515462098979828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968818053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89609194,0.00010678255,0.0755146,0.00023105255,0.000021209195,0.0001545095,0.001357865,0.00074309943,0.025778923],"genre_scores_gemma":[0.99055487,0.000070163784,0.0054472303,0.000012594702,0.0000020405946,0.000058767502,0.00026356813,0.000033458222,0.0035573845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997433,0.00005439201,0.00001602678,0.000054503023,0.0000772518,0.00005465543],"domain_scores_gemma":[0.9996946,0.00011957531,0.000041186064,0.000025726553,0.00010336052,0.000015577762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003831449,0.00058231276,0.00046494996,0.00037850617,0.0004412971,0.0009937102,0.00093396805,0.0009669791,0.002849335],"category_scores_gemma":[0.0007156906,0.00032268406,0.0006761389,0.00039963517,0.00038247617,0.0009140649,0.0005509534,0.00041395664,0.0004072131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044286637,0.000028574656,0.0013383187,0.000023165876,0.0000066326293,0.000037039485,0.00003109204,0.9928564,0.0024774498,0.00064251234,0.0001588163,0.002355663],"study_design_scores_gemma":[0.000008435434,0.000053575157,0.0006669654,0.0000033853244,0.0000053219687,0.000011403459,0.000020631242,0.99704796,0.0016110115,0.0001814309,0.00038330402,0.0000066108396],"about_ca_topic_score_codex":0.0316921,"about_ca_topic_score_gemma":0.017424049,"teacher_disagreement_score":0.0316921,"about_ca_system_score_codex":0.0014053509,"about_ca_system_score_gemma":0.0013475977,"threshold_uncertainty_score":0.06301522},"labels":[],"label_agreement":null},{"id":"W2969093551","doi":"","title":"Monitoring Seasonal Variations in Treatment Performance of a Wastewater Stabilization Pond with Algal Blooms and pH Fluctuations","year":2018,"lang":"en","type":"article","venue":"WDSA / CCWI Joint Conference Proceedings","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","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":"Environmental science; Effluent; Wastewater; Sewage treatment; Water quality; Nutrient; Algal bloom; Phosphorus; Nitrate; Environmental engineering; Eutrophication; Sewage; Ecology; Chemistry; Biology; Phytoplankton","score_opus":0.015255174205406393,"score_gpt":0.20787741594112244,"score_spread":0.19262224173571604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969093551","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99713826,0.0000949047,0.0009793967,0.000016277692,0.0000064068163,0.000065537,0.0007821787,0.000053919706,0.0008631593],"genre_scores_gemma":[0.9947225,0.00020650218,0.0021318817,0.000039907856,0.0000046562222,0.000086608205,0.0012527028,0.000011800633,0.001543467],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999686,0.0000123559375,0.000019763456,0.00007801883,0.00016184528,0.000041976164],"domain_scores_gemma":[0.999622,0.000029872193,0.000103744365,0.000014772064,0.00018791018,0.000041627936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034671425,0.00040834508,0.0004772898,0.000560124,0.00063258887,0.00056771,0.00026786717,0.00027154657,0.0004141473],"category_scores_gemma":[0.00044724278,0.00019030279,0.00032202146,0.0011251427,0.0002861166,0.00026845417,0.00032579937,0.0003361193,0.00018925904],"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":[0.00066954753,0.00033078282,0.5287446,0.00019122474,0.000100856705,0.00026905548,0.0011283072,0.00086766866,0.44316998,0.00004528836,0.0006733014,0.0238094],"study_design_scores_gemma":[0.000012063491,0.0003843416,0.95311725,0.00000587202,0.000049887283,0.00007766928,0.00045115245,0.0023627935,0.042565133,0.000027605183,0.00093167945,0.000014486852],"about_ca_topic_score_codex":0.061655626,"about_ca_topic_score_gemma":0.12282853,"teacher_disagreement_score":0.061655626,"about_ca_system_score_codex":0.0013241085,"about_ca_system_score_gemma":0.001054489,"threshold_uncertainty_score":0.12259352},"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":"W2969981892","doi":"","title":"Hybrid Wavelet and Local Approximation Method for Urban Water Demand Forecasting – Chaotic Approach:","year":2018,"lang":"en","type":"article","venue":"WDSA / CCWI Joint Conference Proceedings","topic":"Energy Load and Power Forecasting","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":"Chaotic; Wavelet; Nonlinear system; Correlation dimension; Phase space; Demand forecasting; Econometrics; Time series; Dimension (graph theory); Mathematics; Computer science; Mathematical optimization; Statistics; Artificial intelligence; Fractal dimension; Operations research; Fractal; Mathematical analysis","score_opus":0.03654169349550443,"score_gpt":0.2240163506905229,"score_spread":0.18747465719501846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969981892","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":[0.09605264,0.00033990294,0.90119463,0.00013559789,0.000051087092,0.000036045214,0.0000804182,0.00026974035,0.0018399232],"genre_scores_gemma":[0.862442,0.0004951616,0.13398352,0.00003732602,0.000048392765,0.000083042636,0.00018336836,0.000055015804,0.0026720236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985623,0.000039646402,0.000009973052,0.000027795944,0.000046927154,0.000019302684],"domain_scores_gemma":[0.99980634,0.00008633066,0.000022443812,0.000016165272,0.000060005732,0.0000088047445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004087474,0.00032632687,0.00048026405,0.00051243615,0.00018060983,0.0004719545,0.00044385836,0.00046953475,0.0009750736],"category_scores_gemma":[0.0010266922,0.00018284962,0.0005700187,0.0007223464,0.00017103889,0.000628873,0.00033433308,0.0005058901,0.00023930188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018944517,0.00010361145,0.0035656034,0.00014923663,0.00009146984,0.00013952274,0.00010954493,0.7696888,0.014506866,0.0060159406,0.0009010307,0.20453891],"study_design_scores_gemma":[0.0000014861287,0.000008558983,0.00018694546,0.0000013844291,0.0000032587266,0.0000068899794,0.0000049380596,0.9991043,0.00039414316,0.00018469084,0.00010129793,0.000002045679],"about_ca_topic_score_codex":0.0042278036,"about_ca_topic_score_gemma":0.002411854,"teacher_disagreement_score":0.0042278036,"about_ca_system_score_codex":0.0002451623,"about_ca_system_score_gemma":0.00042046257,"threshold_uncertainty_score":0.008406401},"labels":[],"label_agreement":null}]}