{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":18,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":18,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"9aad27aca936","filters":{"venue":"Big Earth Data"}},"results":[{"id":"W2990370456","doi":"10.1080/20964471.2019.1690404","title":"A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing","year":2019,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":102,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Wetland; Scale (ratio); Field (mathematics); Remote sensing; Geography; Big data; Satellite imagery; Computer science; Environmental science; Cartography; Environmental resource management; Data mining; Ecology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07683894088236176,"gpt":0.2760697334722478,"spread":0.1992307925898861,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006241718,0.0001396546,0.0001955202,0.00004763145,0.00008766341,0.00006531311,0.001265466,0.00006811599,0.00005225382],"category_scores_gemma":[0.00003624902,0.0001170697,0.00001572436,0.0002519374,0.00001380319,0.0008716425,0.0006504181,0.00005105331,0.0002080257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001520307,"about_ca_system_score_gemma":0.00006280353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008925711,"about_ca_topic_score_gemma":0.008990373,"domain_scores_codex":[0.9983023,0.00004159081,0.0003072668,0.0008166957,0.0002844702,0.0002476903],"domain_scores_gemma":[0.997456,0.00001242939,0.0001551209,0.00223437,0.0000265944,0.0001154728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004235641,0.0005722074,0.2456633,0.001321783,0.00004005079,6.912823e-7,0.0005880966,0.0004488387,0.4885697,0.00004052295,0.002570411,0.2597608],"study_design_scores_gemma":[0.001629655,0.0002018696,0.0846776,0.00009640209,0.00004846856,0.000002863945,0.00009134385,0.6540134,0.007404257,0.00004348035,0.2513552,0.000435534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948142,0.00006670264,0.0016039,0.0002751174,0.0001407493,0.001562939,0.001382633,0.00004848959,0.0001053172],"genre_scores_gemma":[0.9776288,0.00001335481,0.009343411,0.0001264775,0.0004528871,0.00009318332,0.01219206,0.00002691599,0.0001229014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6535645,"threshold_uncertainty_score":0.5016839,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4242225916","doi":"10.1080/20964471.2021.1909822","title":"Number and nest-site selection of breeding black-necked cranes over the past 40 years in the Longbao Wetland Nature Reserve, Qinghai, China","year":2021,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Natural Resources Canada","funders":"Science and Technology Project of State Grid; National Natural Science Foundation of China","keywords":"Wetland; Habitat; Marsh; Endangered species; Nest (protein structural motif); Sanjiang Plain; Ecology; Nature reserve; IUCN Red List; Population; Geography; China; Biology; Archaeology","retraction":null,"screen_n_in":null,"score":{"opus":0.02114586210234691,"gpt":0.2599949221995837,"spread":0.2388490600972368,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003682427,0.00007255466,0.0000856449,0.000009727342,0.00008753927,0.00003511696,0.0003252054,0.0001061642,0.0008216188],"category_scores_gemma":[0.00005798188,0.00004656335,0.00001529418,0.000244335,0.0001892077,0.0002190221,0.000380147,0.0002957813,0.00006854302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006939043,"about_ca_system_score_gemma":0.000008121368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003374167,"about_ca_topic_score_gemma":0.0171375,"domain_scores_codex":[0.9992198,0.0001070287,0.0001099664,0.0002466812,0.0001657425,0.000150735],"domain_scores_gemma":[0.9994565,0.00006771916,0.00004889878,0.0004007647,0.000004575763,0.00002155472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001557504,0.00003790934,0.9944581,0.00000518032,0.000004085456,0.00002363181,0.0003621163,0.0000107403,0.001271064,0.000004483121,0.002637917,0.001169199],"study_design_scores_gemma":[0.0002189338,0.00001594667,0.9946154,0.00001276978,0.00001900658,0.00005537179,0.00009486648,0.0001599394,0.0001695899,0.00003154412,0.004546632,0.00005999959],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986875,0.00004330486,0.000003311904,0.0006038395,0.00007717928,0.0001014586,0.00004474895,0.000005991466,0.0004326409],"genre_scores_gemma":[0.9988436,0.00004554904,0.0000764897,0.000213809,0.00006429379,0.000001548716,0.00007344221,0.000005512601,0.0006757912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01680008,"threshold_uncertainty_score":0.9563125,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401721583","doi":"10.1080/20964471.2024.2386091","title":"Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model","year":2024,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deforestation (computer science); Land cover; Global change; Urbanization; Infill; Sustainability; Environmental change; Land use, land-use change and forestry; Land use; Environmental resource management; Climate change; Process (computing); Computer science; Cellular automaton; Geography; Environmental science; Artificial intelligence; Civil engineering; Engineering; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.08496265188208524,"gpt":0.284025435694942,"spread":0.1990627838128567,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002214253,0.0001236767,0.0001229516,0.00001776264,0.0001533559,0.0003294028,0.0001601524,0.00006220103,0.00004262494],"category_scores_gemma":[0.00002164536,0.00009345969,0.00001424859,0.0001860369,0.00002323292,0.000954524,0.0008327607,0.0001021545,0.00005045754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001363588,"about_ca_system_score_gemma":0.000006525886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004583938,"about_ca_topic_score_gemma":0.004535639,"domain_scores_codex":[0.9989682,0.00003937959,0.0001199353,0.000454821,0.0001677747,0.000249925],"domain_scores_gemma":[0.999586,0.00003860584,0.00003009596,0.0002524343,0.000002973012,0.00008990533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001043482,0.00001385064,0.9805114,0.00009938136,0.00001771538,0.00002662223,0.00016841,0.001237241,0.000109534,0.000001674165,0.00001277691,0.01779094],"study_design_scores_gemma":[0.0001786023,0.00001654937,0.1104004,0.0000765264,0.00002963278,0.00004484954,0.00001831402,0.8851529,0.000004318908,0.00001156349,0.003938471,0.0001278477],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980831,0.0007533005,0.0005371575,0.00004577006,0.00008255265,0.0001504231,0.0001297077,0.000109781,0.0001082762],"genre_scores_gemma":[0.9968561,0.0003087827,0.002580312,0.00006232729,0.00008907686,0.000003733326,0.00007190042,0.00001214059,0.00001562905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8839157,"threshold_uncertainty_score":0.6929573,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3086024170","doi":"10.1080/20964471.2020.1810492","title":"A review of the use of geosocial media data in agent-based models for studying urban systems","year":2020,"lang":"en","type":"review","venue":"Big Earth Data","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Context (archaeology); Data science; Social media; World Wide Web; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.6293673011771451,"gpt":0.4280650620631288,"spread":0.2013022391140163,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003778551,0.0001987175,0.001561842,0.00009924498,0.0001734245,0.00006219519,0.0036571,0.0001564748,0.00002438576],"category_scores_gemma":[0.004715992,0.0001461463,0.0002722182,0.001086625,0.0002297047,0.0002773105,0.0006394805,0.0002059658,0.000003863624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000484865,"about_ca_system_score_gemma":0.0027676,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01021119,"about_ca_topic_score_gemma":0.03072632,"domain_scores_codex":[0.9957581,0.001634419,0.001115779,0.0006264015,0.0006438763,0.0002214012],"domain_scores_gemma":[0.9943424,0.001528274,0.0009119457,0.002981592,0.0001573818,0.0000784233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004232248,0.0001325326,0.00003817898,0.1378586,0.0002100286,0.000001044413,0.0008091698,0.00005337594,2.50127e-8,0.0004784494,0.009487644,0.8509267],"study_design_scores_gemma":[0.00008957592,0.000006092578,0.000003745151,0.05231019,0.001033067,4.102571e-8,0.000113712,0.004570577,1.122414e-8,0.00000627507,0.9417414,0.0001253336],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001033054,0.9809758,0.001095495,0.0001641941,0.0002060616,0.002329792,0.01519596,0.00001148034,0.00002016324],"genre_scores_gemma":[0.0001148312,0.9914736,0.0001407321,0.0001171015,0.0003146927,0.00007851372,0.007714262,0.00001711459,0.00002914135],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9322537,"threshold_uncertainty_score":0.9963799,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408756748","doi":"10.1080/20964471.2025.2479430","title":"Temporal relationships between agricultural and meteorological drought over the Oum Er Rbia River, Morrocco","year":2025,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Agriculture; Environmental science; Agronomy; Geography; Biology; Archaeology","retraction":null,"screen_n_in":null,"score":{"opus":0.05451557219594132,"gpt":0.2654117746073948,"spread":0.2108962024114535,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005294078,0.0001170266,0.0001530112,0.00002338435,0.0004243053,0.00004052046,0.0005561031,0.0001353942,0.0004941233],"category_scores_gemma":[0.0001497216,0.00006547177,0.00003704521,0.0003706292,0.0003992289,0.0002978831,0.0009684301,0.0002937332,0.0002933696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001035924,"about_ca_system_score_gemma":0.000007242052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004218927,"about_ca_topic_score_gemma":0.001525982,"domain_scores_codex":[0.9988264,0.0002299098,0.0001740304,0.0004035289,0.0001608882,0.0002052841],"domain_scores_gemma":[0.9990314,0.0002096253,0.00005238748,0.0006425871,0.000003939713,0.00006005376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006994756,0.00001671201,0.9779826,0.000001359583,0.00004691498,0.000002856196,0.00008114842,0.0000446708,0.00004563786,0.0001788561,0.0188796,0.002712672],"study_design_scores_gemma":[0.0001582151,0.00001245587,0.9311389,0.000002793159,0.000113542,0.000002148963,0.00002867707,0.0006071153,0.00001871991,0.001036395,0.06679532,0.00008570106],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905347,0.000219128,0.0004004145,0.002535874,0.00007161029,0.0001191482,0.0001131663,0.00003284403,0.005973129],"genre_scores_gemma":[0.9954121,0.00003116439,0.0006026023,0.0003882515,0.00006813543,0.000004367122,0.0003791708,0.000002709774,0.003111552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04791572,"threshold_uncertainty_score":0.5410304,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3191318732","doi":"10.1080/20964471.2021.1940733","title":"Earth observation and geospatial big data management and engagement of stakeholders in Hungary to support the SDGs","year":2021,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Hungarian Social, Economic and Educational Studies","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Magyar Tudományos Akadémia; Innovation, Science and Economic Development Canada; National Aeronautics and Space Administration","keywords":"Geospatial analysis; Sustainable development; Sustainability; Geographic information system; Earth observation; Earth system science; Environmental resource management; Big data; Political science; Business; Environmental planning; Geography; Remote sensing; Engineering; Computer science; Environmental science","retraction":null,"screen_n_in":null,"score":{"opus":0.4856162755270305,"gpt":0.3579257214257885,"spread":0.1276905541012419,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001560466,0.00006821723,0.0001239411,0.00003290405,0.0003496747,0.00007861704,0.0004291648,0.00002833857,0.00006852581],"category_scores_gemma":[0.0002605667,0.00006346066,0.000007900206,0.0001776164,0.000144099,0.0002144543,0.001089588,0.0000697639,0.000008454947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001273811,"about_ca_system_score_gemma":0.0002102789,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0029779,"about_ca_topic_score_gemma":0.0600025,"domain_scores_codex":[0.9989134,0.0001653055,0.0002065169,0.0003497376,0.0001881374,0.0001769084],"domain_scores_gemma":[0.9991235,0.0001380019,0.00006328254,0.0005829523,0.00003304701,0.0000591946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002332565,0.0001391401,0.1809573,0.00007300904,0.000164746,0.00001093174,0.03006767,0.000006729712,0.00002196548,0.007992379,0.05115239,0.7293904],"study_design_scores_gemma":[0.0001598847,0.00001029322,0.5126912,0.00001348934,0.00001744267,1.785695e-7,0.02166629,0.00001658421,0.000006646783,0.0001451434,0.4652077,0.00006517533],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750413,0.0007128685,0.0001780183,0.01423365,0.0007854779,0.0005891406,0.0009500635,0.0000151563,0.00749438],"genre_scores_gemma":[0.9896489,0.002787992,0.001929482,0.001051181,0.0004973963,0.00001603886,0.001091144,0.00000842372,0.002969488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7293253,"threshold_uncertainty_score":0.95715,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3190473853","doi":"10.1080/20964471.2021.1946290","title":"Capturing the value of biosurveillance “big data” through natural capital accounting","year":2021,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Guelph; University of Victoria","funders":"","keywords":"Biodiversity; Natural capital; Big data; Underpinning; Environmental resource management; Pace; Scale (ratio); Business; Ecosystem services; Ecosystem; Ecology; Economics; Geography; Computer science; Biology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1144254386284098,"gpt":0.278752375489586,"spread":0.1643269368611762,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003177325,0.0001014105,0.0001169791,0.000005478555,0.0001424385,0.00007239574,0.001286949,0.00003541269,0.003387437],"category_scores_gemma":[0.0002369289,0.00007486824,0.00002425378,0.0002391181,0.0001803304,0.0003910382,0.003171961,0.0001226845,0.0003883458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002955288,"about_ca_system_score_gemma":0.00002118708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001125135,"about_ca_topic_score_gemma":0.005255529,"domain_scores_codex":[0.9987617,0.00005173951,0.0001809087,0.0004274603,0.0003349965,0.000243183],"domain_scores_gemma":[0.997889,0.00006321873,0.00009260389,0.001911878,0.00001349026,0.0000298421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009099329,0.0006714239,0.5036729,0.0002517313,0.0002860141,0.000233713,0.004065071,0.0001278046,0.1099307,0.005685564,0.2204021,0.1545819],"study_design_scores_gemma":[0.0003068303,0.00000729928,0.641254,0.00002008882,0.00001637939,0.00003697136,0.002507936,0.0006792872,0.005354108,0.00004538941,0.3495634,0.0002082494],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840258,0.001161919,0.00007973648,0.001073235,0.0009630056,0.00009310927,0.004603823,0.00003195781,0.007967434],"genre_scores_gemma":[0.9950261,0.0002179352,0.0001581851,0.0003551946,0.0001606367,0.000001158068,0.003831505,0.00000786523,0.0002413903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1543737,"threshold_uncertainty_score":0.9975236,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408822890","doi":"10.1080/20964471.2025.2480446","title":"Exploring the concept of digital twins of wetlands for supporting ecosystem monitoring and management","year":2025,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wetland; Ecosystem; Environmental resource management; Ecosystem management; Environmental science; Ecosystem approach; Business; Computer science; Environmental planning; Ecology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.08711809671200893,"gpt":0.2713054242976198,"spread":0.1841873275856109,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002109416,0.00005633129,0.0001092109,0.00001782347,0.00006004906,0.00003091629,0.0003355993,0.00001102037,0.000008047883],"category_scores_gemma":[0.00001046646,0.0000367615,0.00001683311,0.000088744,0.00001072555,0.0003425812,0.0005844948,0.00001860887,0.000003037408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004617504,"about_ca_system_score_gemma":0.000002921247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005248977,"about_ca_topic_score_gemma":0.0002152781,"domain_scores_codex":[0.999399,0.000008389383,0.0002089762,0.0001667602,0.00009802906,0.0001188559],"domain_scores_gemma":[0.9994317,0.00005752713,0.00008690798,0.0004004667,0.00000402624,0.00001934806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003611998,0.00004981871,0.8576356,0.001091029,0.0001266081,0.000002917773,0.0007539533,0.0003159738,0.0005224898,0.0002437389,0.0003110323,0.1389107],"study_design_scores_gemma":[0.002363814,0.0001894049,0.7900459,0.001605765,0.0002685552,0.000006252697,0.006647674,0.01265543,0.0484666,0.0002263422,0.1370478,0.0004763828],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977959,0.00005059368,0.0001680124,0.00005290148,0.0001959527,0.0002213422,0.0002728375,0.000008277445,0.001234157],"genre_scores_gemma":[0.9996523,0.00006330164,0.0001372562,0.000004201573,0.00003487288,0.00001422042,0.00003756621,0.00000343974,0.00005286557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1384344,"threshold_uncertainty_score":0.149909,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2972980450","doi":"10.1080/20964471.2019.1658494","title":"On the isolatitude property of the rHEALPix Discrete Global Grid System","year":2019,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geodetic datum; Property (philosophy); Geospatial analysis; Computer science; Grid; Ring (chemistry); Identifier; Constant (computer programming); Topology (electrical circuits); Geodesy; Geology; Remote sensing; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04045379475602556,"gpt":0.2419366665017834,"spread":0.2014828717457579,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058773,0.0001207857,0.0001644484,0.00001065993,0.0001248817,0.0001541484,0.004398405,0.00003915392,0.00000495484],"category_scores_gemma":[0.0000562417,0.0000477194,0.00005123308,0.0003391819,0.00003799765,0.0001543352,0.001461477,0.0001117672,0.0002519326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001563387,"about_ca_system_score_gemma":0.0001117496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002489072,"about_ca_topic_score_gemma":0.00004949302,"domain_scores_codex":[0.9985138,0.0002247511,0.0002421094,0.000387228,0.0004099003,0.0002221675],"domain_scores_gemma":[0.9962036,0.0001017826,0.0001609517,0.003447826,0.00004383054,0.00004202365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000762752,0.0002237642,0.04870782,0.0008174194,0.0002792921,0.00001553053,0.0007228535,0.00614464,0.0003517245,0.7543067,0.1678954,0.02045851],"study_design_scores_gemma":[0.0008458344,0.0002761123,0.07866488,0.001489662,0.00002387696,0.00008790413,0.00009234731,0.6626738,0.0001367719,0.0003448252,0.2548652,0.0004987649],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4193264,0.0008304556,0.4339415,0.01360258,0.02416645,0.005321316,0.006478766,0.0009523556,0.09538019],"genre_scores_gemma":[0.9989195,0.000001286245,0.000297864,0.0001623929,0.0001178891,0.000002567901,0.00003810558,0.000004039918,0.0004563314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7539619,"threshold_uncertainty_score":0.8173403,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407571965","doi":"10.1080/20964471.2025.2454044","title":"The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine","year":2025,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"WSP (Canada); Natural Resources Canada","funders":"","keywords":"Wetland; Cloud computing; Earth (classical element); Remote sensing; Environmental science; Environmental resource management; Geography; Earth science; Geology; Computer science; Ecology; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.03825234132487187,"gpt":0.2276457762230669,"spread":0.189393434898195,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006897047,0.0000840193,0.00009190457,0.000008992502,0.0003760169,0.00007642634,0.0004174697,0.00002189214,0.000002712773],"category_scores_gemma":[0.0001522857,0.00003727362,0.00001205863,0.0001701345,0.0003774839,0.00007044571,0.0003128082,0.0001554212,6.837251e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001063357,"about_ca_system_score_gemma":0.00008661796,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.202482,"about_ca_topic_score_gemma":0.786311,"domain_scores_codex":[0.999135,0.0001317404,0.0001724215,0.0002230076,0.0002034179,0.0001343952],"domain_scores_gemma":[0.9978186,0.001238784,0.00008652997,0.00082246,0.0000062974,0.00002735203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001780759,0.0002272821,0.3521956,0.0001018727,0.0002831462,0.00002894225,0.01496741,0.01709477,0.005648313,0.008730645,0.07042424,0.5285171],"study_design_scores_gemma":[0.0007200035,0.00001015657,0.780557,0.00006900937,0.00002511892,0.000001882573,0.000698483,0.1894808,0.0005671894,0.001389622,0.02640035,0.00008039649],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838855,0.0004258895,0.001227979,0.01228985,0.000096225,0.0005267636,0.0002384525,0.00001097732,0.00129839],"genre_scores_gemma":[0.9990877,0.0001092137,0.0002328004,0.0002400906,0.00003208913,0.000003731974,0.0001406774,0.000003971964,0.0001497083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5838289,"threshold_uncertainty_score":0.8028287,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3154310963","doi":"10.1080/20964471.2021.1899578","title":"Clustering spatio-temporal bi-partite graphs for finding crowdsourcing communities in IoMT networks","year":2021,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Crowdsourcing; Computer science; Data science; Premise; The Internet; Graph; World Wide Web; Representation (politics); Human–computer interaction; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.1434081926192701,"gpt":0.3445993672764515,"spread":0.2011911746571814,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001704725,0.00008435887,0.0001667925,0.0001247682,0.0007556021,0.0002440662,0.0004758224,0.00007476752,0.0002184002],"category_scores_gemma":[0.0003403419,0.00009706426,0.00005461214,0.0005770106,0.0001408924,0.000292922,0.0002173407,0.0001409367,0.000006470612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000262109,"about_ca_system_score_gemma":0.0001904038,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02187728,"about_ca_topic_score_gemma":0.7653305,"domain_scores_codex":[0.9986769,0.0003455847,0.0002644919,0.0002290565,0.0001779789,0.0003060067],"domain_scores_gemma":[0.9986525,0.0004264043,0.0000774292,0.0006960952,0.00007912416,0.00006849357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008211994,0.0003069803,0.6684633,0.000247632,0.000166137,0.00002759358,0.04816342,0.06853176,0.00004810021,0.004677326,0.002759226,0.2065264],"study_design_scores_gemma":[0.0008984891,0.00003450575,0.03186298,0.0004200971,0.00008787004,0.000001009745,0.03618833,0.689103,0.0000725317,0.001200386,0.2395594,0.0005714113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8326197,0.001176858,0.1608128,0.001888823,0.0006120547,0.0005557408,0.0003694464,0.0001423437,0.001822266],"genre_scores_gemma":[0.9958273,0.0001132714,0.001030337,0.0001791661,0.000208292,0.00001714282,0.002270169,0.000008734693,0.0003455622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7434532,"threshold_uncertainty_score":0.9846361,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414690600","doi":"10.1080/20964471.2025.2564525","title":"A transformer-based multi-feature fusion method for detecting traffic events using Twitter data","year":2025,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Event (particle physics); Social media; Sensor fusion; Deep learning; Semantics (computer science); Data modeling; Data integration","retraction":null,"screen_n_in":null,"score":{"opus":0.1205570432661833,"gpt":0.3507193102120783,"spread":0.2301622669458949,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004891602,0.000171829,0.0001686056,0.0002057063,0.0001139712,0.00004675458,0.0008381659,0.0001135732,0.000005443362],"category_scores_gemma":[0.00005574184,0.0001706947,0.0000402785,0.0002630324,0.00001015427,0.0002743242,0.0001552219,0.000172593,0.000002332398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001895713,"about_ca_system_score_gemma":0.00003318577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001399902,"about_ca_topic_score_gemma":0.00024202,"domain_scores_codex":[0.9989587,0.00003349022,0.0002032432,0.0004282379,0.0001208202,0.0002554763],"domain_scores_gemma":[0.9985319,0.00006267798,0.00002674083,0.001314809,0.00001928037,0.00004463416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004347933,0.00007106108,0.00004065614,0.0005170419,0.0001254085,0.000002856754,0.00006320468,0.01390643,0.01505446,0.000007516574,0.03973904,0.9304289],"study_design_scores_gemma":[0.0008060325,0.00001213765,0.0003713947,0.0001410764,0.00008651939,0.000001194962,0.00002971096,0.8458338,0.001946995,0.00000206954,0.1506275,0.0001416144],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006114395,0.000185717,0.989319,0.0001610383,0.0005611034,0.0006573353,0.000780154,0.002132292,0.00008895677],"genre_scores_gemma":[0.4694322,0.00005192148,0.5278386,0.0002968278,0.00009737497,0.00002816863,0.00209834,0.00004026216,0.0001162357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9302872,"threshold_uncertainty_score":0.6960728,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3151620880","doi":"10.1080/20964471.2021.1898780","title":"Analytics of big geosocial media and crowdsourced data","year":2021,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crowdsourcing; Social media; Big data; Analytics; Data science; World Wide Web; Computer science; Internet privacy; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.194622147457639,"gpt":0.3503448106989038,"spread":0.1557226632412647,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001194868,0.00006614884,0.0001809796,0.00005394687,0.0002803555,0.00008816492,0.0008669048,0.00007591007,0.0003717673],"category_scores_gemma":[0.002513193,0.00006904028,0.00002461522,0.0005009017,0.0003711742,0.0001732452,0.0005941739,0.00008690335,0.0000142646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007192209,"about_ca_system_score_gemma":0.0006331911,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006765719,"about_ca_topic_score_gemma":0.230629,"domain_scores_codex":[0.998608,0.0002290819,0.0002177039,0.0003974027,0.0003765604,0.0001712425],"domain_scores_gemma":[0.9978074,0.0003248647,0.00008490421,0.001537896,0.000136264,0.0001087031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003548411,0.0005047641,0.07065753,0.0001658635,0.0005174648,0.00004638428,0.0208037,0.00006079397,0.0006118529,0.009123615,0.01626304,0.8812095],"study_design_scores_gemma":[0.0009838957,0.00002906971,0.1272603,0.00008799719,0.0007019528,0.000001537656,0.01481073,0.01307731,0.0003152674,0.002360501,0.8397873,0.0005841],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9256601,0.004827925,0.01277682,0.01609726,0.00139918,0.0005478054,0.01801649,0.0002080124,0.02046636],"genre_scores_gemma":[0.9941891,0.0004272181,0.0003974921,0.0001393717,0.0006156276,6.32295e-7,0.0036259,0.000005343535,0.0005992566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8806254,"threshold_uncertainty_score":0.9998483,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4327936214","doi":"10.1080/20964471.2023.2187659","title":"Publishing Eurac Research data on the GEOSS Platform","year":2023,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Università degli Studi della Basilicata; Università degli Studi di Firenze; European Geosciences Union; European Commission; European Space Agency; Newlife the Charity for Disabled Children; Innovation, Science and Economic Development Canada; National Science Foundation","keywords":"Computer science; Data publishing; Publishing; Process (computing); Data science; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.9209924926776032,"gpt":0.5550802865390005,"spread":0.3659122061386026,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaresearch","open_science"],"category_scores_codex":[0.0849622,0.0001170861,0.000147107,0.000668027,0.001092462,0.01090985,0.025717,0.00004814897,0.000428095],"category_scores_gemma":[0.06685261,0.00006582239,0.00002467434,0.005395511,0.0002055386,0.003298764,0.0347476,0.0005061554,0.0156449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000976587,"about_ca_system_score_gemma":0.0001490785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003577023,"about_ca_topic_score_gemma":0.0007837939,"domain_scores_codex":[0.9914261,0.000428576,0.0005021638,0.001945863,0.005002641,0.0006946907],"domain_scores_gemma":[0.9674096,0.00894193,0.0001350011,0.02306619,0.0002885052,0.0001588343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005641202,0.00001310917,0.0002106429,0.000001301653,0.000005246055,0.00001476277,0.00006123731,0.00002167072,0.000003444703,0.002076253,0.6934443,0.3041424],"study_design_scores_gemma":[0.00009828858,0.00001461595,0.01775539,0.00002193343,0.000002329245,0.000001761611,0.001069225,0.08093874,0.000008255164,0.004269913,0.8957356,0.0000839124],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4991708,0.000398712,0.005227195,0.2471233,0.02119403,0.002449181,0.03449813,0.001574775,0.1883638],"genre_scores_gemma":[0.9127455,0.00009952269,0.001011664,0.002353449,0.002084131,0.00001403001,0.01834361,0.00004419414,0.06330388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4135747,"threshold_uncertainty_score":0.9901169,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403315861","doi":"10.1080/20964471.2024.2412379","title":"Enhanced oceanic fog nowcasting through satellite-based recurrent neural networks","year":2024,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Nowcasting; Satellite; Computer science; Remote sensing; Artificial neural network; Meteorology; Climatology; Artificial intelligence; Environmental science; Geology; Geography; Engineering; Aerospace engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.124822144985353,"gpt":0.288855103010857,"spread":0.164032958025504,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003350466,0.000159389,0.0001636789,0.00004416804,0.0001737455,0.0002167086,0.0004945179,0.00006958022,0.001841948],"category_scores_gemma":[0.0001259241,0.000117675,0.00004944195,0.0003995603,0.00005956945,0.0003605298,0.0000561527,0.0002469761,0.0003958002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001854646,"about_ca_system_score_gemma":0.0000421428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008389592,"about_ca_topic_score_gemma":0.0004050754,"domain_scores_codex":[0.9984638,0.0001120703,0.0002717874,0.0005456945,0.0002177047,0.0003889251],"domain_scores_gemma":[0.9986416,0.0005725141,0.00004209748,0.0006069966,0.00001995624,0.0001169028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004622593,0.00002196666,0.009013134,0.00005050181,0.00002574466,0.00003920074,0.00006956758,0.08710606,0.00003169592,0.0002213449,0.0006562496,0.9027183],"study_design_scores_gemma":[0.0001234615,0.0001036276,0.04526919,0.00003327559,0.00001978234,0.000002193364,0.000008502888,0.9199184,0.00001365015,0.000301495,0.03404194,0.0001644484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7284042,0.0445966,0.1666074,0.001577036,0.01017412,0.001219358,0.00336501,0.001192431,0.04286381],"genre_scores_gemma":[0.9937649,0.0001621439,0.00141137,0.0004367891,0.0005327793,6.738302e-7,0.003576508,0.00000529068,0.000109546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9025539,"threshold_uncertainty_score":0.9990705,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414296289","doi":"10.1080/20964471.2025.2558408","title":"Driver analysis of subarctic wildfire severity over a 35-year period","year":2025,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Subarctic climate; Vegetation (pathology); Period (music); Forcing (mathematics); Temperate climate; Ecosystem; Permafrost; Regression analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.01474098063143006,"gpt":0.2454174510718942,"spread":0.2306764704404642,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003529428,0.0001207613,0.0002807991,0.0001138407,0.00006881476,0.00003281048,0.000749918,0.0000657393,0.001410849],"category_scores_gemma":[0.0001251872,0.000112728,0.00007845392,0.00128877,0.0001257756,0.0002848299,0.0008824746,0.00009366786,0.0002650039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004223342,"about_ca_system_score_gemma":0.00002342623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003285785,"about_ca_topic_score_gemma":0.004978208,"domain_scores_codex":[0.9986724,0.0001010728,0.000217132,0.0004757962,0.0003201678,0.0002134222],"domain_scores_gemma":[0.9981571,0.00006338488,0.00008187975,0.001630532,0.000004116503,0.00006299914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001867726,0.00007936866,0.9805093,0.00003158208,0.0003028368,0.000008184836,0.0001111745,0.00005119554,0.001669465,0.000014807,0.004876548,0.01232686],"study_design_scores_gemma":[0.000215547,0.00001751964,0.9418104,0.00002422878,0.0003032389,8.434604e-7,0.00002236695,0.0347418,0.0001741962,0.000006561238,0.0225714,0.000111874],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956125,0.00005163703,0.0002055167,0.00009773333,0.0001731231,0.0001829951,0.0003821388,0.00003212668,0.003262209],"genre_scores_gemma":[0.9983587,0.00001291378,0.0004093527,0.0001173785,0.00001690855,0.000004107112,0.0001953098,0.000007096121,0.0008782615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03869888,"threshold_uncertainty_score":0.999502,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4416247708","doi":"10.1080/20964471.2025.2574174","title":"Towards a Global Ground-Based Earth Observatory (GGBEO): Leveraging existing systems and networks","year":2025,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"Division of Chemistry; Academy of Finland; Horizon 2020 Framework Programme; National Aeronautics and Space Administration; Analyses et Expérimentations pour les Ecosystèmes; Battelle; College of Computing; European Commission; Helsingin Yliopisto; Goddard Space Flight Center; CERN; National Science Foundation","keywords":"Earth system science; Interoperability; Earth observation; Leverage (statistics); Observatory; Global climate; Data center; Sustainability; Global network","retraction":null,"screen_n_in":null,"score":{"opus":0.09041919056152246,"gpt":0.2712797539566265,"spread":0.180860563395104,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005319773,0.0002004768,0.0002599702,0.00001311692,0.0003118974,0.0003296493,0.0005270828,0.00009543782,0.000120662],"category_scores_gemma":[0.00007329462,0.0001778203,0.00003022579,0.0004221629,0.00009433131,0.000348784,0.0001369009,0.0001575889,0.00004261467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004429178,"about_ca_system_score_gemma":0.0002079819,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009040822,"about_ca_topic_score_gemma":0.003019253,"domain_scores_codex":[0.9983525,0.0001111483,0.0002912848,0.0005452315,0.0002428073,0.0004570874],"domain_scores_gemma":[0.9988716,0.000116789,0.00009208505,0.0007433905,0.00003416148,0.0001420382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004652285,0.00001427655,0.7822937,0.0001303916,0.00004779393,0.00005568404,0.0000234374,0.008891092,0.000001978337,0.0002520118,0.00145473,0.2067884],"study_design_scores_gemma":[0.0003104896,0.00002743486,0.5419676,0.0001156261,0.00002841779,0.000009283978,0.0001278739,0.4117331,7.946484e-7,0.00003722159,0.04547612,0.0001660255],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9183623,0.01832168,0.02652113,0.000411321,0.002800604,0.0005022589,0.001203183,0.000321504,0.03155609],"genre_scores_gemma":[0.9949191,0.0001672547,0.002520802,0.0008757667,0.0002498456,0.000001452535,0.000792328,0.000004771224,0.0004687181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.402842,"threshold_uncertainty_score":0.9975581,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411738446","doi":"10.1080/20964471.2025.2518763","title":"Spatial sample weighted machine learning for multitemporal land cover change modeling with imbalanced datasets","year":2025,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Land cover; Sample (material); Change detection; Cover (algebra); Computer science; Artificial intelligence; Pattern recognition (psychology); Remote sensing; Geography; Machine learning; Land use; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05220717917520037,"gpt":0.2617149815049951,"spread":0.2095078023297947,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001767531,0.0001181877,0.0001482253,0.00002603163,0.0001680127,0.00005734571,0.0003980909,0.00004189262,0.0002317375],"category_scores_gemma":[0.0000191665,0.00008293521,0.00001399687,0.0001167276,0.00000854601,0.0003857298,0.0004430513,0.00007235137,0.00008350716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001061072,"about_ca_system_score_gemma":0.00001027032,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01858875,"about_ca_topic_score_gemma":0.05281052,"domain_scores_codex":[0.9990869,0.00002610258,0.0001363865,0.0003835818,0.0001395902,0.0002274626],"domain_scores_gemma":[0.999294,0.00005044749,0.00005312356,0.0005446489,0.000005051998,0.000052715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003529965,0.00008103355,0.9760509,0.0001472103,0.00005735286,0.000007326105,0.0001218073,0.006576689,0.0001607613,0.000005184767,0.001059835,0.01537891],"study_design_scores_gemma":[0.0009845252,0.00004398111,0.01169326,0.00005448725,0.00002335774,0.000001047637,0.000005288254,0.8928452,0.00006096504,0.00001583264,0.09414469,0.0001273497],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8982085,0.0002126633,0.08631487,0.0002847342,0.0002454704,0.0007945757,0.01357588,0.00009803079,0.000265297],"genre_scores_gemma":[0.9743301,0.00004035638,0.002949022,0.0002160257,0.0001032434,0.00002629307,0.0222733,0.0000110588,0.00005061252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9643576,"threshold_uncertainty_score":0.9879466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}