{"meta":{"query_hash":"b84001c576d9","filters":{"venue":"Geodesy and Geodynamics"},"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/b84001c576d9","api":"https://metacan.xera.ac/api/v1/cohort?venue=Geodesy+and+Geodynamics"},"results":[{"id":"W2076647913","doi":"10.3724/sp.j.1246.2012.00034.1","title":"A method for rapid transmission of multi-scale vector river data via the Internet","year":2012,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"Data Management and Algorithms","field":"Computer Science","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 Waterloo","funders":"","keywords":"Bottleneck; Vector map; Computer science; The Internet; Scale (ratio); Data mining; Data transmission; Transmission (telecommunications); Bandwidth (computing); Computer network; Telecommunications; Cartography; Artificial intelligence; World Wide Web; Geography","score_opus":0.03520013781570731,"score_gpt":0.29360843846254775,"score_spread":0.25840830064684045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076647913","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012326678,0.0005414909,0.99716514,0.00041212075,0.00020194637,0.0002455602,0.000092934635,0.00002266402,0.00008549081],"genre_scores_gemma":[0.055396475,0.00019260898,0.9432051,0.00022239223,0.0000789008,0.000014863748,0.00020524385,0.000010111536,0.0006742835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991707,0.00005494356,0.00016066086,0.00025819376,0.000109896035,0.00024556572],"domain_scores_gemma":[0.99899566,0.00015676422,0.0000709165,0.0006876912,0.000024587804,0.00006438997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000808295,0.000105617284,0.00013079467,0.000038734248,0.00007626554,0.000054300155,0.0011233852,0.000040239713,0.000012351699],"category_scores_gemma":[0.0000121338835,0.00007109671,0.000039745544,0.00011743801,0.0000616478,0.00077718415,0.00062869897,0.00007141958,0.0000036490187],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012525077,0.00009117707,0.00021008385,0.000049463328,0.000035769397,2.4636697e-7,0.0010016991,0.000021700207,0.00019798825,0.004358779,0.000669308,0.9933513],"study_design_scores_gemma":[0.00035831006,0.00003594214,0.0030018303,0.000011571181,0.000029743711,0.0000034003604,0.000028073282,0.94311094,0.00010471589,0.00038374003,0.052831426,0.000100315076],"about_ca_topic_score_codex":0.000084334446,"about_ca_topic_score_gemma":0.000020317788,"teacher_disagreement_score":0.99325097,"about_ca_system_score_codex":0.000005846789,"about_ca_system_score_gemma":0.00000773633,"threshold_uncertainty_score":0.28992394},"labels":[],"label_agreement":null},{"id":"W4389824878","doi":"10.1016/j.geog.2023.11.002","title":"Assessment of the performance of the TOPGNSS and ANN-MB antennas for ionospheric measurements using low-cost u-blox GNSS receivers","year":2023,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"GNSS positioning and interference","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 New Brunswick","funders":"","keywords":"GNSS applications; Ionosphere; Geodesy; Geology; Remote sensing; Telecommunications; Computer science; Geophysics; Global Positioning System","score_opus":0.022772664537605347,"score_gpt":0.2459432410049769,"score_spread":0.22317057646737154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389824878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99766195,0.00005592826,0.0012514844,0.00002565963,0.00031617016,0.00022249778,0.00003761629,0.000020396375,0.00040828346],"genre_scores_gemma":[0.9993908,0.00015153422,0.00032112104,0.000015164374,0.000011151195,0.000010005331,0.000005220655,0.000010798062,0.00008422357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994168,0.000017966959,0.00016884442,0.00010902668,0.00012155726,0.00016580532],"domain_scores_gemma":[0.999637,0.0000381396,0.00006105647,0.00017105702,0.000069054,0.0000236609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017616693,0.0000983149,0.0001264553,0.00002274318,0.0001435604,0.000017236824,0.00013018228,0.000048383798,0.0000023937898],"category_scores_gemma":[0.000016005626,0.00006922467,0.000045050354,0.00020186412,0.00008652655,0.00007731912,0.000059702572,0.000094215895,2.604476e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032349606,0.000054351924,0.3672932,0.0012078313,0.00017726261,2.5645375e-7,0.00095176836,0.5716281,0.049274523,0.0005081762,0.0001242795,0.008747904],"study_design_scores_gemma":[0.00018860296,0.000020761156,0.26203966,0.000229288,0.000025023724,0.000002271717,0.00013397128,0.7358098,0.0014063352,0.00003775831,0.000035709236,0.00007079388],"about_ca_topic_score_codex":0.000033493074,"about_ca_topic_score_gemma":0.000025529687,"teacher_disagreement_score":0.16418172,"about_ca_system_score_codex":0.000035878456,"about_ca_system_score_gemma":0.000022202916,"threshold_uncertainty_score":0.28229},"labels":[],"label_agreement":null},{"id":"W4402852857","doi":"10.1016/j.geog.2024.08.003","title":"Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal","year":2024,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","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":"Wilfrid Laurier University","funders":"University Grants Commission- Nepal; Institut de Recherche pour le Développement","keywords":"Permafrost; Geology; Distribution (mathematics); Computer science; Physical geography; Machine learning; Artificial intelligence; Mathematics; Geography; Oceanography","score_opus":0.048226579957107274,"score_gpt":0.25886761597624214,"score_spread":0.21064103601913486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402852857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98570496,0.0032557154,0.002372183,0.0007848649,0.00048619966,0.00013861942,0.006264384,0.00006158603,0.00093150703],"genre_scores_gemma":[0.9764089,0.0006723166,0.00012683708,0.00022892377,0.00016159796,0.00000238575,0.021885816,0.000008328424,0.0005048921],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987818,0.000061606814,0.00020927619,0.00036619062,0.00014782742,0.0004332957],"domain_scores_gemma":[0.9996007,0.00010695117,0.000023408544,0.00009854466,0.000020221834,0.0001501898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003469033,0.00017803685,0.00017088934,0.00010193838,0.00023683798,0.00057450053,0.00009168116,0.00010171017,0.0007477789],"category_scores_gemma":[0.000011655833,0.00016267941,0.00004277435,0.00025591694,0.000041495063,0.0004839287,0.000035310364,0.00032123897,0.00014254104],"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":[0.000086747656,0.00003188062,0.5400411,0.00032273153,0.00002918928,0.00027643534,0.0032312488,0.3484235,0.000712159,0.0011331672,0.00072654977,0.104985304],"study_design_scores_gemma":[0.00011757232,0.000045549095,0.04596937,0.000084211264,0.000011794352,0.0000305281,0.00029594882,0.94499314,0.0000043889595,0.00078911905,0.0074355663,0.00022282229],"about_ca_topic_score_codex":0.006232712,"about_ca_topic_score_gemma":0.05962344,"teacher_disagreement_score":0.5965696,"about_ca_system_score_codex":0.000017589306,"about_ca_system_score_gemma":0.000032205608,"threshold_uncertainty_score":0.957536},"labels":[],"label_agreement":null},{"id":"W4409471616","doi":"10.1016/j.geog.2025.01.005","title":"Refining GNSS-based water storage estimation: Improved hydrological signal extraction using principal component analysis","year":2025,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","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":"Center for Strategic Research; Natural Science Foundation of Qinghai; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"GNSS applications; Principal component analysis; Computer science; Refining (metallurgy); Component (thermodynamics); SIGNAL (programming language); Extraction (chemistry); Estimation; Artificial intelligence; Telecommunications; Global Positioning System; Chemistry; Chromatography; Engineering","score_opus":0.019238684704254137,"score_gpt":0.25095154568889005,"score_spread":0.23171286098463592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409471616","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95715356,0.000049124672,0.041921705,0.00013011252,0.00014324473,0.00008720577,0.000037403523,0.00002070622,0.00045691498],"genre_scores_gemma":[0.9968154,0.0000024539531,0.0024214436,0.00017255287,0.000022564021,0.0000013004818,0.0004452284,0.000002045667,0.00011700523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886256,0.00007649406,0.00023838386,0.00031767704,0.00019544463,0.00030946248],"domain_scores_gemma":[0.99956733,0.00007302364,0.00007010431,0.00015635125,0.000050098974,0.00008308948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042867893,0.0001575335,0.00022822115,0.00017311238,0.0003769099,0.000108421,0.00009540541,0.000101149024,0.00023257177],"category_scores_gemma":[0.000012075053,0.00012087356,0.00009666225,0.0002978342,0.00006578635,0.00013641268,0.000018529587,0.00017972982,0.000011930947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008306804,0.000040126728,0.19137652,0.000023587314,0.00014259072,0.000005910094,0.000027159518,0.7989146,0.0014829215,0.00005212153,0.000001304396,0.007850072],"study_design_scores_gemma":[0.00024480044,0.000045746947,0.21132831,0.000008008855,0.00020794496,0.0000013696025,0.000015343567,0.787524,0.00007860436,0.00028210608,0.0001433526,0.00012040315],"about_ca_topic_score_codex":0.002149879,"about_ca_topic_score_gemma":0.0021175973,"teacher_disagreement_score":0.039661825,"about_ca_system_score_codex":0.000020067988,"about_ca_system_score_gemma":0.000035190413,"threshold_uncertainty_score":0.4929081},"labels":[],"label_agreement":null},{"id":"W4411330470","doi":"10.1016/j.geog.2025.03.004","title":"Evaluating Euler pole parameters for the north American terrestrial reference frame of 2022","year":2025,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","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":"Geological Survey of Canada; Natural Resources Canada","funders":"University of Nevada, Reno","keywords":"Reference frame; Euler's formula; Frame of reference; Geology; Frame (networking); Geodesy; Mathematics; Computer science; Mathematical analysis; Physics; Telecommunications; Classical mechanics","score_opus":0.0470136264655879,"score_gpt":0.29192892422242633,"score_spread":0.24491529775683843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411330470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975622,0.00014538814,0.0009216401,0.00025502604,0.00030832176,0.00027237824,0.0002118497,0.0000063299494,0.0003168365],"genre_scores_gemma":[0.9984224,0.000064649,0.0010466931,0.00017833647,0.000026363503,0.000005087772,0.0001249602,0.0000019052555,0.00012963527],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99913347,0.00005142731,0.00019236171,0.0002013489,0.00019095119,0.00023043016],"domain_scores_gemma":[0.9989956,0.00059965404,0.00012013306,0.00019615325,0.000047007536,0.00004142043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037835602,0.00010658725,0.0001694178,0.000042639324,0.00022742615,0.000044352822,0.00018746167,0.00002838413,0.000021112588],"category_scores_gemma":[0.00016223978,0.00007567905,0.000053558375,0.000251515,0.00017076341,0.00005199289,0.000019588377,0.00012554321,0.00000326694],"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":[0.00019043376,0.000023157061,0.60418034,0.000032101794,0.00009221093,2.4472268e-7,0.00010920664,0.0162232,0.000045323646,0.000291348,0.00008187447,0.37873057],"study_design_scores_gemma":[0.00034074223,0.00019837456,0.7794848,0.000013177228,0.000059827245,3.178414e-7,0.00014656015,0.21676777,0.000007864306,0.0021771493,0.0007085444,0.000094880466],"about_ca_topic_score_codex":0.007183812,"about_ca_topic_score_gemma":0.014538638,"teacher_disagreement_score":0.37863567,"about_ca_system_score_codex":0.0000030893127,"about_ca_system_score_gemma":0.000061898405,"threshold_uncertainty_score":0.99942744},"labels":[],"label_agreement":null},{"id":"W4414445144","doi":"10.1016/j.geog.2025.06.003","title":"Sensitivity of glacial isostatic adjustment observations on 3D Earths with lateral viscosity variations: A perspective from the Forward problem","year":2025,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","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":"Post-glacial rebound; Perturbation (astronomy); Computation; Sensitivity (control systems); Inverse problem; Perspective (graphical)","score_opus":0.011598072981133922,"score_gpt":0.21005331250665474,"score_spread":0.19845523952552083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414445144","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9928831,0.0000711487,0.003139035,0.0011888279,0.000103271384,0.00038548035,0.0006170882,0.000010311199,0.0016017265],"genre_scores_gemma":[0.9978912,0.000023424034,0.0013329224,0.0004105583,0.000027121565,0.0000031317902,0.00019252043,0.0000018287219,0.00011728066],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991671,0.00011890851,0.00013686108,0.0002145159,0.00019752493,0.0001651221],"domain_scores_gemma":[0.9992893,0.0003134634,0.00007730473,0.00016397455,0.00011938894,0.00003661325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024713002,0.00011958036,0.00015034335,0.000032365955,0.00027420055,0.000044651788,0.000067154404,0.000038809932,0.000026911457],"category_scores_gemma":[0.000037293456,0.00008002757,0.00003529407,0.00020497301,0.00008418311,0.000108321,0.000014825926,0.00012457243,0.000006014761],"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":[0.00031031153,0.00016830591,0.933853,0.0000341906,0.000294082,0.000003310446,0.0023059538,0.026914086,0.000058939993,0.021293929,0.0000629733,0.014700951],"study_design_scores_gemma":[0.0003483753,0.000121272344,0.90221393,0.000056952802,0.0000691765,5.510869e-7,0.00028146405,0.078441665,0.000005001764,0.018302822,0.00007447083,0.00008431381],"about_ca_topic_score_codex":0.02316784,"about_ca_topic_score_gemma":0.05712605,"teacher_disagreement_score":0.05152758,"about_ca_system_score_codex":0.000011337749,"about_ca_system_score_gemma":0.000082007595,"threshold_uncertainty_score":0.983337},"labels":[],"label_agreement":null},{"id":"W881913845","doi":"10.1016/j.geog.2015.07.002","title":"Water storage changes in North America retrieved from GRACE gravity and GPS data","year":2015,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","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; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Environmental science; Climate change; Water storage; Global warming; Water resources; Snow; Physical geography; Hydrology (agriculture); Sea level; Global change; Peninsula; Climatology; Oceanography; Geography; Meteorology; Geology; Ecology","score_opus":0.03872572364871909,"score_gpt":0.21856940866747954,"score_spread":0.17984368501876044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W881913845","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973351,0.00041469574,0.000102466176,0.00036522915,0.00017296153,0.00012663376,0.0013268637,0.000012636442,0.00014344159],"genre_scores_gemma":[0.9945081,0.00022019556,0.00028352047,0.00024900035,0.0000509822,6.4116733e-7,0.00462068,0.0000037633429,0.00006312905],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988249,0.000061115825,0.00013368858,0.00041425723,0.0002368051,0.00032923455],"domain_scores_gemma":[0.9992858,0.000049286074,0.00004537675,0.00038046346,0.00003493834,0.00020414552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032536764,0.00015622958,0.00020961647,0.000032047803,0.00009934233,0.00008885144,0.00023636482,0.000062685525,0.000045862187],"category_scores_gemma":[0.00004141109,0.000120016164,0.000011186624,0.00015474936,0.000102427555,0.00024290409,0.00010431417,0.00017132788,0.00003658983],"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":[0.00006796748,0.000024695111,0.9729321,0.000010922992,0.00001652312,0.000017981518,0.0008000444,0.00042563165,0.000024447654,0.0000062648837,0.00009381905,0.025579583],"study_design_scores_gemma":[0.00049720966,0.00009116422,0.9405072,0.000008403188,0.000019598541,0.0000024504284,0.00027355214,0.049573842,0.0000062233935,0.0028389895,0.005954558,0.00022684438],"about_ca_topic_score_codex":0.027646024,"about_ca_topic_score_gemma":0.25995323,"teacher_disagreement_score":0.23230721,"about_ca_system_score_codex":0.0000046517766,"about_ca_system_score_gemma":0.000022558994,"threshold_uncertainty_score":0.97882897},"labels":[],"label_agreement":null}]}