{"meta":{"query_hash":"2b75d06c8cbe","filters":{"venue":"Geoscience Data Journal"},"cohort_total":23,"direct_labels_cover":0,"predictions_cover":23,"exported":23,"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/2b75d06c8cbe","api":"https://metacan.xera.ac/api/v1/cohort?venue=Geoscience+Data+Journal"},"results":[{"id":"W1926356829","doi":"10.1002/gdj3.25","title":"The International Surface Pressure Databank version 2","year":2015,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","cited_by":194,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Oak Ridge National Laboratory; Biological and Environmental Research; Office of Science; FP7 Space; Climate Program Office; Universidade do Porto; U.S. Department of Energy; European Commission; National Oceanic and Atmospheric Administration; Sight Research UK; University of East Anglia; National Energy Research Scientific Computing Center; Natural Environment Research Council; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Metadata; Computer science; Data assimilation; Download; Environmental science; Meteorology; Climatology; Database; Geography; Geology; World Wide Web","score_opus":0.07476526700861204,"score_gpt":0.29293629256322806,"score_spread":0.21817102555461604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1926356829","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":[0.93406826,0.00041791552,0.014939228,0.019250818,0.007352013,0.00029410917,0.0018410116,0.00006149105,0.021775162],"genre_scores_gemma":[0.9695734,0.0011143923,0.018330753,0.001564002,0.00061988115,0.000002430486,0.00054570567,0.000021306942,0.0082281595],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984179,0.000059041005,0.0001653286,0.00027894406,0.00081901747,0.00025975358],"domain_scores_gemma":[0.99886996,0.000081752514,0.00009111694,0.00073578936,0.00002292008,0.00019843981],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0034433643,0.00006861183,0.000052291285,0.000009671045,0.00054543087,0.0003598719,0.0030454495,0.000025227842,0.0009399528],"category_scores_gemma":[0.0005723365,0.000043262644,0.000016008406,0.00013731248,0.0003097127,0.0018390286,0.0023255055,0.00020566823,0.00052406517],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086983695,0.00014024551,0.07019359,0.0000018516571,0.000017455548,0.00001944702,0.0005636065,0.022401223,0.0040832018,0.00034939242,0.8903723,0.011770704],"study_design_scores_gemma":[0.00018202318,0.000018601819,0.0030459613,0.0000040440314,0.0000075312355,0.0001065986,0.0002104701,0.09959434,0.000030530355,0.000743678,0.8959872,0.00006902302],"about_ca_topic_score_codex":0.00027798585,"about_ca_topic_score_gemma":0.000103782804,"teacher_disagreement_score":0.07719312,"about_ca_system_score_codex":0.00007982172,"about_ca_system_score_gemma":0.00006562103,"threshold_uncertainty_score":0.9999733},"labels":[],"label_agreement":null},{"id":"W1983439209","doi":"10.1002/gdj3.11","title":"Historical climate observations in Canada: 18th and 19th century daily temperature from the St. Lawrence Valley, Quebec","year":2014,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Observatoire de Paris, Université de Recherche Paris Sciences et Lettres; University of South Carolina; McGill University","keywords":"Centennial; Climatology; Sampling frame; Climate change; Environmental science; Metadata; Consistency (knowledge bases); Physical geography; Geography; History; Archaeology; Geology; Demography; Mathematics; Computer science","score_opus":0.03214757967623939,"score_gpt":0.2173994340000008,"score_spread":0.1852518543237614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983439209","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":[0.9910266,0.0019207128,0.000041813164,0.003946051,0.00089308474,0.00006925297,0.0020295908,0.000011413225,0.000061524835],"genre_scores_gemma":[0.9944092,0.002015072,0.0017312279,0.0010704779,0.0002670995,5.3069186e-7,0.00041868456,0.0000043525542,0.00008335811],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99798167,0.00025348365,0.0003149525,0.00038940713,0.00055823126,0.00050223403],"domain_scores_gemma":[0.99823356,0.0008075583,0.00014770443,0.00054176786,0.000038296017,0.00023110786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001269919,0.00014068403,0.0001765246,0.000054586486,0.00069265394,0.00038721526,0.0014449658,0.000041818377,0.00020231097],"category_scores_gemma":[0.00066297624,0.00008932378,0.000016477066,0.00036062518,0.00013108659,0.00090761116,0.00014729657,0.00047966107,0.000010281307],"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":[0.000020811203,0.000007213615,0.9771893,0.000003089992,0.0000027252195,0.000027950882,0.00012174492,0.0001805936,0.00005336164,0.000024092153,0.0076190745,0.014750041],"study_design_scores_gemma":[0.0001427569,0.000018931269,0.90346175,0.000037599493,0.0000059475847,0.00006482228,0.00031846444,0.0050068987,8.967478e-7,0.000045744946,0.090780154,0.000116056755],"about_ca_topic_score_codex":0.96881545,"about_ca_topic_score_gemma":0.99741715,"teacher_disagreement_score":0.08316108,"about_ca_system_score_codex":0.00012482092,"about_ca_system_score_gemma":0.001294564,"threshold_uncertainty_score":0.5327404},"labels":[],"label_agreement":null},{"id":"W2925793299","doi":"10.1002/gdj3.67","title":"Long‐term weather, streamflow, and water chemistry datasets for hydrological modelling applications at the upper La Salle River watershed in Manitoba, Canada","year":2019,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Streamflow; Environmental science; Precipitation; Watershed; Hydrology (agriculture); Relative humidity; Drainage basin; Wind speed; Automatic weather station; Water year; STREAMS; Atmospheric sciences; Meteorology; Geography","score_opus":0.018617518274399412,"score_gpt":0.22140074986354374,"score_spread":0.20278323158914432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925793299","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":[0.99407315,0.00004017399,0.0034204363,0.0015127263,0.000052946983,0.00030320385,0.0003677932,0.000004887719,0.00022466776],"genre_scores_gemma":[0.9974811,0.00017433238,0.00038176918,0.00057801424,0.000036845027,0.00003289088,0.00061971025,0.000006492128,0.00068881176],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987415,0.000040409654,0.00017011416,0.00042303017,0.00021654172,0.0004083819],"domain_scores_gemma":[0.9993422,0.00006209367,0.000046239336,0.00047201524,0.0000036953527,0.000073735966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007344742,0.00012013645,0.00011305282,0.0000135415985,0.00050435896,0.000057485762,0.0008827378,0.00004697183,0.00053232326],"category_scores_gemma":[0.0000071055715,0.000065524575,0.000015550826,0.000052345695,0.00039548942,0.0003616713,0.0014247998,0.00018485205,0.00005690484],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010063603,0.00011010234,0.95893514,0.00003002506,0.000039249197,0.00005035849,0.0005079598,0.020105952,0.0027001444,0.000011784973,0.014430303,0.002978362],"study_design_scores_gemma":[0.002972892,0.00012320366,0.4034989,0.000040806783,0.00012629079,0.00054333464,0.00065808784,0.114832506,0.0015306645,0.0017832556,0.47287077,0.0010193192],"about_ca_topic_score_codex":0.01705589,"about_ca_topic_score_gemma":0.056241695,"teacher_disagreement_score":0.55543625,"about_ca_system_score_codex":0.00008908968,"about_ca_system_score_gemma":0.00001425585,"threshold_uncertainty_score":0.9894896},"labels":[],"label_agreement":null},{"id":"W2940481073","doi":"10.1002/gdj3.62","title":"From books to bytes: A new data rescue tool","year":2019,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; McGill University","keywords":"Metadata; Computer science; Data mapping; Open data; World Wide Web; Traceability; Linked data; Data science; Schema (genetic algorithms); Unstructured data; Context (archaeology); Information retrieval; Database; Data mining; Semantic Web; Software engineering; Big data","score_opus":0.0669314535514649,"score_gpt":0.2863183443244633,"score_spread":0.2193868907729984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940481073","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":[0.030063717,0.00027003442,0.96130776,0.005144536,0.0017115376,0.0001492139,0.0006239506,0.00006420759,0.00066501205],"genre_scores_gemma":[0.080416985,0.000088540146,0.905214,0.003974387,0.0026556184,0.0000013159943,0.000523938,0.000027414144,0.007097772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99712163,0.000055124372,0.00043060773,0.0010566975,0.00077647076,0.00055948715],"domain_scores_gemma":[0.99411446,0.00005603527,0.00017147292,0.0051749297,0.00007406108,0.0004090308],"candidate_categories":["scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.0016396841,0.00015969847,0.00023684434,0.00014869226,0.0003457922,0.0020351387,0.015147928,0.00003929845,0.00024457753],"category_scores_gemma":[0.00045165906,0.00012728247,0.000042419404,0.00073991687,0.000026228463,0.00555841,0.008269971,0.0003081394,0.0008157278],"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":[0.000020672838,0.000057561803,0.0019749275,0.0000045280876,0.0000539899,0.00008842229,0.0011480517,0.00055625336,0.0033237669,0.0016508921,0.23420185,0.7569191],"study_design_scores_gemma":[0.00033054186,0.00012966603,0.0034574224,0.000095963056,0.0000232868,0.00020263,0.00048443815,0.51357746,0.00006802092,0.0011640915,0.48005274,0.0004137218],"about_ca_topic_score_codex":0.001278007,"about_ca_topic_score_gemma":0.00016694964,"teacher_disagreement_score":0.75650537,"about_ca_system_score_codex":0.00003361679,"about_ca_system_score_gemma":0.00048580975,"threshold_uncertainty_score":0.9999623},"labels":[],"label_agreement":null},{"id":"W2950726882","doi":"10.1002/gdj3.69","title":"Hydrometeorological measurements in peatland‐dominated, discontinuous permafrost at Scotty Creek, Northwest Territories, Canada","year":2019,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Peat; Permafrost; Environmental science; Hydrology (agriculture); Plateau (mathematics); Wetland; Snow; Precipitation; Structural basin; Hydrometeorology; Mire; Water table; Physical geography; Geology; Groundwater; Geography; Ecology; Geomorphology; Meteorology","score_opus":0.04920288572504611,"score_gpt":0.2400239059705159,"score_spread":0.1908210202454698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950726882","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":[0.9621563,0.00045823585,0.000002818506,0.00038654252,0.0019024463,0.00015980095,0.03432817,0.000008306111,0.000597382],"genre_scores_gemma":[0.9771323,0.00018860988,0.00006365148,0.00044780684,0.00027574162,6.0561786e-7,0.021537919,0.000004622681,0.00034871072],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99706024,0.00013685264,0.00043306916,0.00057053985,0.0009746264,0.0008246531],"domain_scores_gemma":[0.99868155,0.00011422748,0.00017824046,0.00060334924,0.000056896122,0.00036572036],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012939646,0.00021034497,0.00030664264,0.0001020771,0.00045392624,0.00039677054,0.0016292818,0.000074733995,0.010924549],"category_scores_gemma":[0.00013191598,0.00016402744,0.000036762365,0.00040859723,0.00014880941,0.0010763968,0.00022163677,0.00040778087,0.00014620938],"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":[0.000046495577,0.000019717325,0.99529016,0.000006635624,0.0000047559024,0.00017269321,0.000078683755,0.00005565264,0.0003057809,1.6514494e-7,0.0024427187,0.0015765204],"study_design_scores_gemma":[0.00057822786,0.00011666502,0.9682774,0.000034714296,0.000008902199,0.0006600021,0.00025256292,0.001689405,0.000010950484,0.000008757887,0.028115736,0.00024670057],"about_ca_topic_score_codex":0.92964685,"about_ca_topic_score_gemma":0.99904937,"teacher_disagreement_score":0.069402516,"about_ca_system_score_codex":0.00008680309,"about_ca_system_score_gemma":0.0004401139,"threshold_uncertainty_score":0.9899796},"labels":[],"label_agreement":null},{"id":"W3003434013","doi":"10.1002/gdj3.88","title":"A cross‐checked global monthly weather station database for precipitation covering the period 1901–2010","year":2020,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weather station; Environmental science; Precipitation; Database; Elevation (ballistics); Automatic weather station; Meteorology; Anomaly (physics); Climatology; Climate change; Geography; Computer science; Geology","score_opus":0.0693379893752232,"score_gpt":0.3128615558650594,"score_spread":0.24352356648983622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003434013","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":[0.7846761,0.000032864697,0.20355582,0.0046152542,0.0004452958,0.00043867025,0.005811967,0.00002531135,0.00039873717],"genre_scores_gemma":[0.9652063,0.000050120078,0.031552725,0.0019765894,0.0002695513,0.00002669898,0.0008105489,0.000014144117,0.000093273215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842775,0.00006463187,0.00027419912,0.000432282,0.000470796,0.000330348],"domain_scores_gemma":[0.99907136,0.00007783184,0.0001585562,0.00048596825,0.000025063198,0.00018123155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00149847,0.00011091493,0.00009478975,0.000009081644,0.0007332745,0.00048553632,0.0011883696,0.000034610057,0.00061335467],"category_scores_gemma":[0.00088078965,0.0000792042,0.00003825483,0.00026196733,0.00030797237,0.002326279,0.0006113382,0.0001622062,0.00007957847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019124238,0.0010051213,0.31969586,0.00020136533,0.00010163239,0.00005889351,0.036132704,0.33239704,0.101820245,0.001850408,0.060051586,0.14477271],"study_design_scores_gemma":[0.0009677819,0.00018965625,0.106895365,0.000020293552,0.000040492272,0.00005692367,0.0013357714,0.8464949,0.000108646236,0.0015975889,0.04200595,0.00028660896],"about_ca_topic_score_codex":0.00031380562,"about_ca_topic_score_gemma":0.00020681675,"teacher_disagreement_score":0.51409787,"about_ca_system_score_codex":0.00012583725,"about_ca_system_score_gemma":0.00006787533,"threshold_uncertainty_score":0.6715803},"labels":[],"label_agreement":null},{"id":"W3011687737","doi":"10.1002/gdj3.89","title":"A database of Holocene temperature records for north‐eastern North America and the north‐western Atlantic","year":2020,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Holocene; Peat; Radiocarbon dating; Geology; Temperature record; Context (archaeology); Sea surface temperature; Oceanography; Climatology; Climate change; Dinocyst; Physical geography; Holocene climatic optimum; Geography; Archaeology; Paleontology; Palynology; Pollen; Ecology","score_opus":0.04654905777149093,"score_gpt":0.26677851233039757,"score_spread":0.22022945455890663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011687737","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":[0.9914153,0.00077228073,0.0005820953,0.0041632936,0.00019856919,0.00022962748,0.0026071086,0.000007881947,0.000023833269],"genre_scores_gemma":[0.9943784,0.0010800321,0.0009511146,0.0014098174,0.00017168457,0.0000013086566,0.0019685472,0.0000025793784,0.000036547888],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99823076,0.00022558884,0.00032790197,0.00041672846,0.00032719597,0.00047184195],"domain_scores_gemma":[0.99845606,0.00048101103,0.0002127361,0.00045925388,0.00010107999,0.00028987965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006163326,0.00014328457,0.0003012832,0.00006577593,0.0005817479,0.00012126577,0.0015711997,0.000047822952,0.00016331689],"category_scores_gemma":[0.0005864731,0.00008455144,0.000042441174,0.00046233163,0.0012041386,0.0006087437,0.00019665381,0.0005158542,0.000054995176],"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":[0.00032711923,0.000009663402,0.9955711,0.000035375404,0.000017773147,0.00004383803,0.00032242917,0.000073975534,0.0000013400163,0.0000011384975,0.00088172226,0.002714542],"study_design_scores_gemma":[0.0008599965,0.00023896777,0.9792413,0.000014964079,0.000035997033,0.00047111127,0.00017917127,0.015746407,0.0000010619627,0.000014124467,0.0030824938,0.0001143953],"about_ca_topic_score_codex":0.00038697643,"about_ca_topic_score_gemma":0.013174924,"teacher_disagreement_score":0.016329773,"about_ca_system_score_codex":5.258803e-7,"about_ca_system_score_gemma":0.00017814782,"threshold_uncertainty_score":0.7351917},"labels":[],"label_agreement":null},{"id":"W4200404100","doi":"10.1002/gdj3.142","title":"Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1): Multivariate bias‐corrected climate model outputs for terrestrial modelling and attribution studies in North America","year":2021,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University; Environment and Climate Change Canada","funders":"","keywords":"Climatology; Quantile; Multivariate statistics; Environmental science; Climate model; Shortwave; Shortwave radiation; Consistency (knowledge bases); Precipitation; Meteorology; Climate change; Econometrics; Statistics; Geography; Computer science; Mathematics; Radiation; Geology; Radiative transfer","score_opus":0.1881434693944444,"score_gpt":0.33235105337640586,"score_spread":0.14420758398196146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200404100","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":[0.82669675,0.00016872522,0.11888432,0.00058423996,0.00048955326,0.0002967465,0.05285745,0.0000141451,0.000008049546],"genre_scores_gemma":[0.9304929,0.0037794567,0.032505643,0.0006683766,0.00011802634,0.00001841915,0.0323615,0.000022066257,0.000033620934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975612,0.00014979114,0.00043292827,0.0006983583,0.00033688874,0.0008208088],"domain_scores_gemma":[0.998743,0.00017845629,0.00017263799,0.0005257846,0.000040122526,0.0003400088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015006555,0.0001844379,0.0002696491,0.000116922645,0.0008382799,0.00015588565,0.00055518036,0.000071150884,0.00005418237],"category_scores_gemma":[0.0006481855,0.00016939765,0.00003111541,0.0005188952,0.0001992068,0.0013779057,0.00090686145,0.00026564187,0.000014727404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032345223,0.00032316995,0.04030925,0.00005730612,0.00003315479,0.00014292277,0.0026872521,0.93663704,0.0016051386,0.00003979664,0.008813652,0.009027884],"study_design_scores_gemma":[0.0010132127,0.000039572995,0.0022903723,0.000049524468,0.00003435306,0.000050240502,0.0008238103,0.98505,0.00001918692,0.00013139547,0.010287299,0.00021103122],"about_ca_topic_score_codex":0.031107247,"about_ca_topic_score_gemma":0.33847815,"teacher_disagreement_score":0.3073709,"about_ca_system_score_codex":0.00045636442,"about_ca_system_score_gemma":0.0002651223,"threshold_uncertainty_score":0.9753447},"labels":[],"label_agreement":null},{"id":"W4383700617","doi":"10.1002/gdj3.190","title":"A data screening approach to confirming a target mineral is chlorite using <scp>EPMA</scp> and <scp>LA‐ICPMS</scp> data","year":2023,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Chlorite; Electron microprobe; Mineral; Spurious relationship; Mineralogy; Chemistry; Analytical Chemistry (journal); Computer science; Materials science; Environmental chemistry; Metallurgy; Machine learning","score_opus":0.14593132296104708,"score_gpt":0.3154254745135261,"score_spread":0.16949415155247902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383700617","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":[0.06317715,0.0004974094,0.9274023,0.001620169,0.00078357034,0.00032200123,0.003192132,0.00023262092,0.0027726148],"genre_scores_gemma":[0.035864934,0.00031875508,0.9514535,0.002304098,0.0014761061,0.000010209638,0.004044818,0.000034893088,0.004492653],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99405813,0.00019022965,0.0006869307,0.002426894,0.0011652716,0.0014725362],"domain_scores_gemma":[0.99220663,0.0007248071,0.00042721283,0.0057023745,0.00019068072,0.0007482883],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0072522257,0.0004091764,0.00045343756,0.0004665452,0.0016729076,0.0034001255,0.017122444,0.00015786674,0.000009390463],"category_scores_gemma":[0.0060218344,0.0003899689,0.000042685842,0.002629486,0.00040695834,0.0062346617,0.026322786,0.0008743555,0.0000383686],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010589729,0.00041899958,0.014552636,0.00039542935,0.00027206916,0.0016061473,0.010168235,0.006422718,0.04028031,0.0015319206,0.85977733,0.064563625],"study_design_scores_gemma":[0.00026298806,0.000023283546,0.0017521878,0.000093080045,0.000020287349,0.001850351,0.0011434773,0.6455783,0.000104061124,0.00033395714,0.34873492,0.00010310449],"about_ca_topic_score_codex":0.00034165918,"about_ca_topic_score_gemma":0.000017298475,"teacher_disagreement_score":0.6391556,"about_ca_system_score_codex":0.000031408454,"about_ca_system_score_gemma":0.00049177004,"threshold_uncertainty_score":0.9998552},"labels":[],"label_agreement":null},{"id":"W4386475389","doi":"10.1002/gdj3.217","title":"A 5‐km gridded product development of daily temperature and precipitation for Bangladesh, Nepal, and Pakistan from 1981 to 2016","year":2023,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation","funders":"Climate Extremes; International Centre for Integrated Mountain Development; Asia-Pacific Network for Global Change Research","keywords":"Precipitation; Climatology; Context (archaeology); Environmental science; Climate change; Meteorology; Product (mathematics); Geography; Geology; Mathematics","score_opus":0.04145543883636125,"score_gpt":0.2996842861833527,"score_spread":0.2582288473469914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386475389","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":[0.9955389,0.00005155988,0.001843073,0.0010527304,0.00015653942,0.00028603125,0.0010435049,0.000011576408,0.000016115826],"genre_scores_gemma":[0.8240958,0.00033068322,0.17363898,0.00038019643,0.00018253649,0.00004124833,0.0009336958,0.000019406349,0.0003774628],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99872446,0.000031570187,0.0002687415,0.00044274176,0.0002873161,0.00024519413],"domain_scores_gemma":[0.99932534,0.00009804945,0.000089864996,0.00030648254,0.000020007019,0.00016024364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00145242,0.00009172544,0.00011752239,0.000054906366,0.0003628385,0.00013911599,0.00040078975,0.000031787848,0.000066812885],"category_scores_gemma":[0.00026693236,0.0000724474,0.00001053482,0.0002946835,0.00015108145,0.00070491596,0.0005303972,0.00008775673,0.000014610868],"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":[0.00022706416,0.00020578169,0.036964715,0.00008740668,0.00002928072,0.00000762749,0.028326787,0.0005402079,0.7388447,0.00007198504,0.04881478,0.14587967],"study_design_scores_gemma":[0.0015135229,0.00030454103,0.85461366,0.0002433737,0.0000435363,0.00006071663,0.00400913,0.017070869,0.0057962793,0.004305069,0.11137954,0.00065974],"about_ca_topic_score_codex":0.0001125728,"about_ca_topic_score_gemma":0.00023238779,"teacher_disagreement_score":0.81764895,"about_ca_system_score_codex":0.000045622517,"about_ca_system_score_gemma":0.00008293736,"threshold_uncertainty_score":0.2954319},"labels":[],"label_agreement":null},{"id":"W4390841449","doi":"10.1002/gdj3.236","title":"Digitizing observations from the 1861–1875 Met Office Daily Weather Reports using citizen scientist volunteers","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Cambridge; Met Office; National Centre for Atmospheric Science; Natural Environment Research Council; Sight Research UK","keywords":"Storm; Environmental science; Climatology; High pressure; Meteorology; Geography; Geology; Engineering","score_opus":0.09349251172344224,"score_gpt":0.29623864033703734,"score_spread":0.2027461286135951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390841449","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":[0.9607238,0.00024459153,0.03424154,0.0006710598,0.0017636481,0.00015220669,0.0008345768,0.000056640838,0.0013119426],"genre_scores_gemma":[0.97262293,0.00007564204,0.025458425,0.00069005723,0.0002760936,0.000003649061,0.00019658044,0.000023681903,0.00065291487],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973104,0.00012552126,0.00045790145,0.0007557582,0.00086272,0.00048770828],"domain_scores_gemma":[0.9981823,0.00032433894,0.00013399038,0.0011529981,0.000022980039,0.00018340221],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0037432862,0.00015852106,0.00013709984,0.000050001297,0.0013323945,0.0017436214,0.0014244567,0.000052680014,0.001848014],"category_scores_gemma":[0.0009705195,0.000110230234,0.00006984633,0.00087244494,0.0006823291,0.0029509692,0.0010780239,0.0004112538,0.00014620503],"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":[0.00004433635,0.000564564,0.5905237,0.000052832176,0.00020492137,0.002056503,0.01073993,0.04845731,0.19428498,0.0045675114,0.11140229,0.037101083],"study_design_scores_gemma":[0.00021137577,0.00004260049,0.07738607,0.0003209449,0.00018634115,0.0026347819,0.0019551034,0.44814843,0.00010601421,0.010991429,0.45739037,0.00062655605],"about_ca_topic_score_codex":0.0037797918,"about_ca_topic_score_gemma":0.0006149733,"teacher_disagreement_score":0.5131377,"about_ca_system_score_codex":0.00023784784,"about_ca_system_score_gemma":0.00020424562,"threshold_uncertainty_score":0.99996775},"labels":[],"label_agreement":null},{"id":"W4391607635","doi":"10.1002/gdj3.241","title":"Bias‐adjusted and downscaled humidex projections for heat preparedness and adaptation in Canada","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Research Council Canada; Environment and Climate Change Canada; University of Waterloo; Government of Canada","keywords":"Preparedness; Adaptation (eye); Heat wave; Environmental science; Econometrics; Psychology; Climate change; Economics; Geology; Management; Oceanography","score_opus":0.10915301642765773,"score_gpt":0.2866985583005825,"score_spread":0.17754554187292476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391607635","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":[0.9907034,0.0001315527,0.007419464,0.0006127674,0.00030556516,0.00025013732,0.00047986113,0.000009408019,0.00008782923],"genre_scores_gemma":[0.99660933,0.00018979127,0.0029457777,0.0000892946,0.000028593391,0.000013968186,0.000051923962,0.0000043808336,0.00006696816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990034,0.000036141555,0.00020613498,0.00034436505,0.00019575583,0.0002141731],"domain_scores_gemma":[0.99955595,0.00013253259,0.000024692814,0.0001781144,0.000007257765,0.000101482314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009721043,0.00006887309,0.00007925031,0.000047697835,0.0002407416,0.0001862938,0.00021170612,0.000021524209,0.00008058484],"category_scores_gemma":[0.00018946057,0.00005635245,0.000007964149,0.00027022712,0.0001247862,0.00097250997,0.00021363456,0.00011275634,0.0000013251199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003472829,0.00045996855,0.36010316,0.00069869007,0.00006273124,0.00022037153,0.036349084,0.06337678,0.029224172,0.001068832,0.045777775,0.46231115],"study_design_scores_gemma":[0.00020230906,0.00003413261,0.021725457,0.00005003867,0.000013214561,0.00024502014,0.0020532236,0.9670318,0.000011447641,0.0006182746,0.007905739,0.00010934854],"about_ca_topic_score_codex":0.58572966,"about_ca_topic_score_gemma":0.9116053,"teacher_disagreement_score":0.903655,"about_ca_system_score_codex":0.00032944328,"about_ca_system_score_gemma":0.00039874762,"threshold_uncertainty_score":0.417029},"labels":[],"label_agreement":null},{"id":"W4392195682","doi":"10.1002/gdj3.243","title":"Preface to the special issue on “Old records for new knowledge”","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Geology and Paleoclimatology Research","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":"McGill University","funders":"","keywords":"Data science; History; Computer science","score_opus":0.07622722623413447,"score_gpt":0.3417585862537743,"score_spread":0.26553136001963984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392195682","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3812676,0.016040968,0.020586705,0.31453335,0.0990923,0.0034342885,0.0064677447,0.00027974247,0.15829732],"genre_scores_gemma":[0.37821618,0.003338532,0.026708076,0.014057595,0.17653841,0.000018210025,0.0019204717,0.000042242835,0.39916027],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984572,0.000121249715,0.00018451721,0.00041651234,0.00026651396,0.00055403786],"domain_scores_gemma":[0.9985128,0.0006456346,0.00002950423,0.00047288198,0.000040730487,0.00029844564],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002356734,0.0001077404,0.000117956726,0.00014613172,0.00077137287,0.0003928207,0.0019597856,0.000067221175,0.0064971973],"category_scores_gemma":[0.0005732018,0.000062124855,0.000040166145,0.0004813045,0.00016827896,0.0004341265,0.000093683026,0.0004908931,0.0065058037],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000630663,0.000008833388,0.11999033,0.000008511538,0.00000947742,0.000028890126,0.00039951183,0.00015863583,0.0000011159053,0.00014225276,0.7039095,0.17527987],"study_design_scores_gemma":[0.00008942791,0.00028849416,0.054040436,0.00003169112,0.00000898087,0.00042134794,0.00010842474,0.007791995,0.0000066382167,0.0009676028,0.93615603,0.00008895124],"about_ca_topic_score_codex":0.00012933339,"about_ca_topic_score_gemma":0.0068756137,"teacher_disagreement_score":0.30047575,"about_ca_system_score_codex":0.0000023810128,"about_ca_system_score_gemma":0.0004598488,"threshold_uncertainty_score":0.994411},"labels":[],"label_agreement":null},{"id":"W4399532353","doi":"10.1002/gdj3.258","title":"Geochemistry of forty‐one eclogitic and pyroxenitic mantle xenoliths from the Central Slave Craton, Canada (Ekati Diamond Mine)","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Geological and Geochemical Analysis","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":true,"ca_institutions":"Hudbay Minerals (Canada)","funders":"Deutsche Forschungsgemeinschaft","keywords":"Kimberlite; Eclogite; Xenolith; Geology; Geochemistry; Craton; Metasomatism; Mantle (geology); Lithosphere; Peridotite; Subduction; Petrology; Tectonics; Paleontology","score_opus":0.020463986463528545,"score_gpt":0.19732308857297626,"score_spread":0.17685910210944772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399532353","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":[0.98788005,0.005257035,0.00020621784,0.0022021092,0.00023863211,0.000043569064,0.0036710221,0.000008167548,0.0004932011],"genre_scores_gemma":[0.99799126,0.0003505134,0.00043993807,0.00034907306,0.00019995977,2.3709171e-7,0.0005773473,0.0000014456186,0.00009020889],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983449,0.00004455169,0.00030985824,0.00036292057,0.0004802493,0.000457533],"domain_scores_gemma":[0.9988122,0.000450704,0.00008274265,0.00034198133,0.00004591782,0.00026646213],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035071475,0.00013078225,0.00019349312,0.000024239785,0.00025027094,0.00022841313,0.00092907174,0.00004256856,0.006217869],"category_scores_gemma":[0.00033149577,0.00007308235,0.000042948424,0.00031717267,0.0003938908,0.0003079699,0.00012153072,0.0002886282,0.0000138700725],"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":[0.000023058124,0.00003636961,0.9467385,0.000057975267,0.00012514241,0.0003464638,0.00019232383,0.00014745106,0.0025427176,0.000040161704,0.0136294635,0.036120366],"study_design_scores_gemma":[0.00015246103,0.000049434457,0.9359692,0.00009046068,0.00016856892,0.00023902142,0.0005976116,0.041749753,0.001276552,0.013953203,0.0055035376,0.0002502283],"about_ca_topic_score_codex":0.43522212,"about_ca_topic_score_gemma":0.46693134,"teacher_disagreement_score":0.041602302,"about_ca_system_score_codex":0.000007375637,"about_ca_system_score_gemma":0.00035225012,"threshold_uncertainty_score":0.9946906},"labels":[],"label_agreement":null},{"id":"W4400569816","doi":"10.1002/gdj3.257","title":"Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS‐M6)","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Pacific Institute for Climate Solutions; University of Victoria","funders":"Environment and Climate Change Canada","keywords":"Multivariate statistics; Climatology; Environmental science; Geography; Geology; Mathematics; Statistics","score_opus":0.04848706006360344,"score_gpt":0.30205759133191556,"score_spread":0.25357053126831214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400569816","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":[0.82749045,0.0006588775,0.10568498,0.02031915,0.010090381,0.0023988204,0.022928962,0.00035612143,0.010072234],"genre_scores_gemma":[0.9741553,0.00037633986,0.02224069,0.0015450999,0.00045564733,0.000029886003,0.00044397748,0.000039444792,0.00071362144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749595,0.000056599536,0.00035898562,0.0007015612,0.00039895836,0.0009879231],"domain_scores_gemma":[0.998407,0.0001462486,0.00006429991,0.0007089148,0.000018071878,0.0006554823],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029264938,0.00017576882,0.00016510657,0.000120656834,0.00096504344,0.0007410664,0.0015120171,0.000084492785,0.0026855515],"category_scores_gemma":[0.00031798295,0.00014078009,0.00007390778,0.0004196443,0.00028945197,0.0017198131,0.0005785031,0.00033008467,0.0005881486],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004357626,0.0009414515,0.056958918,0.00060925924,0.0002196716,0.0012001061,0.009924948,0.050325368,0.09981853,0.011233449,0.339427,0.42890555],"study_design_scores_gemma":[0.00037870085,0.00007606346,0.005569272,0.00008758393,0.00004402848,0.00035841748,0.00012273158,0.6205401,0.000035653542,0.0023181962,0.3701245,0.00034474937],"about_ca_topic_score_codex":0.043811604,"about_ca_topic_score_gemma":0.084115244,"teacher_disagreement_score":0.57021475,"about_ca_system_score_codex":0.00039924055,"about_ca_system_score_gemma":0.00030236624,"threshold_uncertainty_score":0.9982261},"labels":[],"label_agreement":null},{"id":"W4402314430","doi":"10.1002/gdj3.272","title":"The Irish drought impacts database: A 287‐year database of drought impacts derived from newspaper archives","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"European Research Council; Irish Research Council","keywords":"Newspaper; Irish; Database; Geography; Computer science; Business; Advertising; Linguistics","score_opus":0.020927866531312227,"score_gpt":0.2794423225507011,"score_spread":0.2585144560193889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402314430","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":[0.95534885,0.0062128236,0.02173886,0.0047734934,0.0013127686,0.00027961438,0.006140384,0.00008019469,0.0041130288],"genre_scores_gemma":[0.9780815,0.006993868,0.011639844,0.0007518973,0.0005362403,0.000005786778,0.001250906,0.00003486122,0.00070505805],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99627286,0.0003231672,0.00064190937,0.0008799816,0.0009688128,0.0009132609],"domain_scores_gemma":[0.9964515,0.00074262096,0.00027193376,0.0020016148,0.000014139957,0.0005182114],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002068883,0.0003015602,0.00032374947,0.0001271223,0.001055167,0.0005875338,0.0034259793,0.00008496996,0.004414297],"category_scores_gemma":[0.00081294513,0.00019156204,0.0001460041,0.001043111,0.0016624999,0.0036518078,0.0022636803,0.0008015409,0.0006096925],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049789995,0.0005781576,0.09007766,0.000080687096,0.0006907921,0.0015197142,0.0048786975,0.0007233329,0.559082,0.00035251872,0.27498844,0.066530116],"study_design_scores_gemma":[0.0025432373,0.0005502644,0.35642767,0.0010988574,0.0013734996,0.0019948308,0.0030169217,0.17462547,0.012284075,0.007011614,0.43682107,0.0022524924],"about_ca_topic_score_codex":0.0021793807,"about_ca_topic_score_gemma":0.0017804274,"teacher_disagreement_score":0.54679793,"about_ca_system_score_codex":0.00007382219,"about_ca_system_score_gemma":0.0002496666,"threshold_uncertainty_score":0.9964958},"labels":[],"label_agreement":null},{"id":"W4402765811","doi":"10.1002/gdj3.267","title":"Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","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":"Canmore Museum and Geoscience Centre; University of Saskatchewan; University of Calgary","funders":"Regione Abruzzo","keywords":"Series (stratigraphy); Precipitation; Geography; Geology; Meteorology","score_opus":0.0496837898305277,"score_gpt":0.27948428019645605,"score_spread":0.22980049036592834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402765811","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":[0.9823449,0.000046365007,0.006378382,0.0024325128,0.0013885627,0.00016755128,0.006984935,0.000013946744,0.00024280818],"genre_scores_gemma":[0.9920131,0.000072474846,0.0065076463,0.00020720813,0.00011185344,0.0000012253388,0.0009280932,0.0000065242357,0.00015186587],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.998317,0.000077165954,0.00028641763,0.0004578159,0.0005945354,0.00026706577],"domain_scores_gemma":[0.9986341,0.000059467027,0.00008053989,0.0010999235,0.000008021443,0.000117937234],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00095245725,0.00009127247,0.00009565803,0.000028901712,0.00027478917,0.00027959122,0.0024570765,0.000028854414,0.0009694972],"category_scores_gemma":[0.00017375719,0.00006318611,0.000025187566,0.00034980886,0.00030850378,0.0032297669,0.002713695,0.00016936148,0.000120953795],"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":[0.0002253688,0.0006374813,0.27212277,0.00007031395,0.00008335997,0.000025886806,0.011729615,0.011392857,0.3068069,0.0015286707,0.2581835,0.13719329],"study_design_scores_gemma":[0.00025407234,0.00012427277,0.78729546,0.00017778609,0.00005929653,0.00015041155,0.0008427876,0.10824712,0.0006842212,0.0033829985,0.098501064,0.00028049323],"about_ca_topic_score_codex":0.001780887,"about_ca_topic_score_gemma":0.00067603216,"teacher_disagreement_score":0.5151727,"about_ca_system_score_codex":0.000115017945,"about_ca_system_score_gemma":0.00007044604,"threshold_uncertainty_score":0.99994373},"labels":[],"label_agreement":null},{"id":"W4402933071","doi":"10.1002/gdj3.261","title":"Automation of historical weather data rescue","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Environment and Climate Change Canada","keywords":"Automation; Computer science; Aeronautics; Systems engineering; Data science; Engineering","score_opus":0.0765346719700735,"score_gpt":0.3237836979278009,"score_spread":0.24724902595772735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402933071","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":[0.0007184593,0.0007532933,0.99451077,0.0020636627,0.0010524826,0.000063462845,0.00017689368,0.00021274094,0.00044826072],"genre_scores_gemma":[0.10639212,0.00078255247,0.8899748,0.0003790829,0.00063477637,0.0000051715956,0.0002473943,0.000020710217,0.0015634111],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980728,0.00009174051,0.0003905811,0.00055000244,0.00065468665,0.00024017676],"domain_scores_gemma":[0.9974452,0.000089137706,0.000098049386,0.0021504448,0.00009944624,0.000117716576],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.002768541,0.00009396108,0.00013149837,0.00028820758,0.00015173049,0.00060930446,0.007041274,0.000048558548,0.00011131905],"category_scores_gemma":[0.00034866974,0.00007497044,0.000028415232,0.0007864323,0.00007869306,0.005623571,0.0021035078,0.00028738225,0.000102091886],"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":[0.0000017707199,0.00006141792,0.000076298435,0.000028115386,0.000010770484,0.00008205611,0.0002457883,0.0000016821856,0.0030641088,0.0038813038,0.19981368,0.792733],"study_design_scores_gemma":[0.0001286377,0.00012870142,0.0009883562,0.00029460236,0.000020682257,0.0016792964,0.000027444683,0.5786676,0.0014377241,0.009561802,0.40679005,0.00027512558],"about_ca_topic_score_codex":0.000057209818,"about_ca_topic_score_gemma":0.000007650876,"teacher_disagreement_score":0.7924579,"about_ca_system_score_codex":0.00014710175,"about_ca_system_score_gemma":0.0003288448,"threshold_uncertainty_score":0.9983311},"labels":[],"label_agreement":null},{"id":"W4405822678","doi":"10.1002/gdj3.288","title":"Assessment of Hydrologic Data Estimates From <scp>ERA5</scp> Reanalyses in Benin, West Africa","year":2024,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GDG Environnement","funders":"","keywords":"Environmental science","score_opus":0.06336229607818106,"score_gpt":0.32262898059305056,"score_spread":0.2592666845148695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405822678","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":[0.96148455,0.0024138624,0.018969808,0.0024251062,0.0010226273,0.000260378,0.0018081863,0.00008302736,0.011532426],"genre_scores_gemma":[0.98616207,0.0007919166,0.012239835,0.0001484605,0.000060781644,0.0000034306247,0.0003793095,0.000007779303,0.00020643813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975843,0.00010766957,0.000442575,0.00081017584,0.0005569402,0.0004983586],"domain_scores_gemma":[0.9981164,0.00042805722,0.00014624881,0.0011992567,0.0000068917457,0.00010318563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00231272,0.00017868797,0.00026960106,0.00016344264,0.00028898983,0.00018670327,0.0032684167,0.000057807916,0.0007010341],"category_scores_gemma":[0.00048260167,0.00013125024,0.000031129308,0.0006564493,0.0006590506,0.0019302067,0.0046306658,0.0003911532,0.00017414492],"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":[0.0000051948464,0.0002915195,0.9015183,0.000025307376,0.00015733358,0.0006309163,0.00094293425,0.008039005,0.0042363578,0.00015962365,0.07796188,0.006031626],"study_design_scores_gemma":[0.00023729663,0.00012610186,0.5855059,0.00008122065,0.00016256302,0.000052452313,0.00045014286,0.3620554,0.000070806076,0.0052692243,0.045856413,0.0001324445],"about_ca_topic_score_codex":0.0008866123,"about_ca_topic_score_gemma":0.00042180772,"teacher_disagreement_score":0.3540164,"about_ca_system_score_codex":0.000069987254,"about_ca_system_score_gemma":0.000042378913,"threshold_uncertainty_score":0.76758313},"labels":[],"label_agreement":null},{"id":"W4408520477","doi":"10.1002/gdj3.70001","title":"Data on Physical Properties of Sea Ice in the Northern Barents Sea and Adjacent Arctic Basin From the Nansen Legacy Project","year":2025,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"H2020 European Research Council; Framsenteret; Norges Forskningsråd","keywords":"Geology; Oceanography; Arctic; Structural basin; Sea ice; The arctic; Arctic ice pack; Canada Basin; Arctic sea ice decline; Climatology; Drift ice; Physical geography; Geomorphology; Geography","score_opus":0.05425882631006815,"score_gpt":0.2688700168398623,"score_spread":0.21461119052979413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408520477","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":[0.9928503,0.0003021422,0.00017345154,0.0038093238,0.0002385777,0.00020830863,0.0022654647,0.0000039891247,0.00014844659],"genre_scores_gemma":[0.9979079,0.00025255827,0.0002405447,0.0010160909,0.00011822592,4.969188e-7,0.0004388183,0.0000017979671,0.000023593937],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99824744,0.00029436836,0.00023592678,0.00036775373,0.0005578425,0.00029667097],"domain_scores_gemma":[0.9984326,0.0004134914,0.00011558043,0.0009496646,0.000042100877,0.0000466107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014985185,0.0001259977,0.00015633268,0.000052793028,0.00047891843,0.00030360871,0.002840522,0.000024910178,0.000025235675],"category_scores_gemma":[0.0005034707,0.000058256657,0.000020857171,0.0003889175,0.00043710344,0.0010357749,0.00029473627,0.00043908413,0.000008972093],"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":[0.00008395689,0.00009121176,0.9369327,0.000024477742,0.000019230996,0.000016862137,0.0018068299,0.00009743436,0.000014132113,0.000018922785,0.00083733525,0.06005692],"study_design_scores_gemma":[0.00030675088,0.00009018921,0.85983574,0.00031287715,0.000045908004,0.00006015788,0.007169336,0.12674886,0.000005258538,0.0002487707,0.0050607477,0.0001153904],"about_ca_topic_score_codex":0.019147933,"about_ca_topic_score_gemma":0.018228335,"teacher_disagreement_score":0.12665142,"about_ca_system_score_codex":0.000008372645,"about_ca_system_score_gemma":0.00040109243,"threshold_uncertainty_score":0.9996864},"labels":[],"label_agreement":null},{"id":"W4410321773","doi":"10.1002/gdj3.291","title":"Software to Enable Ocean Discoveries: A Case Study With <scp>ICESat</scp>‐2 and Argo","year":2025,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Environmental Monitoring and Data Management","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":"University of Toronto","funders":"Nuclear Safety and Security Commission; National Aeronautics and Space Administration","keywords":"Argo; Software; Computer science; Geology; Oceanography; Operating system","score_opus":0.018818046523194538,"score_gpt":0.24466465811067548,"score_spread":0.22584661158748093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410321773","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":[0.994923,0.00026671402,0.003352515,0.00011075314,0.0003718763,0.00022564249,0.0005139942,0.00001905157,0.000216433],"genre_scores_gemma":[0.9941527,0.00008961862,0.004471884,0.0003712562,0.000083146006,7.574077e-7,0.00008412072,0.0000025179588,0.0007439966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99848545,0.00005477108,0.00018509143,0.0004990728,0.00039289022,0.00038274634],"domain_scores_gemma":[0.9989041,0.00013600773,0.00007085132,0.0006193858,0.000011843661,0.00025778287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008156597,0.00014188781,0.00013342721,0.00014055,0.0009054749,0.0007963657,0.00082797906,0.00001768357,0.00005147203],"category_scores_gemma":[0.00018747103,0.000098495635,0.000010049455,0.00040795695,0.00014839994,0.0016679949,0.0003625643,0.0001825639,0.000029602372],"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":[0.000009547062,0.00005370409,0.9687586,0.0000107835385,0.000017254775,0.0014904331,0.0005138232,0.00048364332,0.0000011700042,0.0000023865953,0.0033567215,0.02530192],"study_design_scores_gemma":[0.0006460783,0.0011600812,0.8513016,0.00010406183,0.00008379642,0.00297034,0.072666556,0.00089231663,0.000013408272,0.0000981445,0.06990132,0.00016231209],"about_ca_topic_score_codex":0.0050885547,"about_ca_topic_score_gemma":0.003517133,"teacher_disagreement_score":0.117457025,"about_ca_system_score_codex":0.000010262048,"about_ca_system_score_gemma":0.000063835476,"threshold_uncertainty_score":0.7692406},"labels":[],"label_agreement":null},{"id":"W4414667640","doi":"10.1002/gdj3.70034","title":"Integrated Global Radiosonde Archive Toolkit ( <scp>IGRAT</scp> ): A Python Library for Radiosonde Data Analysis","year":2025,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Climate variability and models","field":"Environmental Science","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":"Ministry of the Environment, Conservation and Parks; University of Toronto","funders":"","keywords":"Radiosonde; Python (programming language); Preprocessor; Software; Data set; Set (abstract data type)","score_opus":0.039508425013896814,"score_gpt":0.2930319486396074,"score_spread":0.2535235236257106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414667640","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":[0.18219854,0.0003878147,0.7725129,0.002045253,0.0008640218,0.00064050726,0.037136417,0.00012950817,0.0040850374],"genre_scores_gemma":[0.21009782,0.00316048,0.7201859,0.009250087,0.0008991879,0.000088916255,0.048135735,0.00010844587,0.008073485],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99628156,0.00023180648,0.000632415,0.0013956731,0.0006120215,0.00084649865],"domain_scores_gemma":[0.9958867,0.00052117935,0.00025407414,0.002934399,0.000019906947,0.00038377277],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0024373666,0.00029520033,0.00043065916,0.00021254111,0.00077521155,0.00083279685,0.006664898,0.00011143223,0.00042308576],"category_scores_gemma":[0.0018942781,0.00024656998,0.00016160104,0.003053393,0.0006308907,0.003567531,0.0041836193,0.00040862733,0.000050649836],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011106891,0.0006232859,0.19331764,0.000043094453,0.00052017794,0.00005757861,0.00047161247,0.007529574,0.00230344,0.0023492938,0.7559161,0.03675714],"study_design_scores_gemma":[0.0007090787,0.000097085474,0.031079272,0.000049433147,0.0006277756,0.00012798772,0.0005461923,0.5199965,0.00006077875,0.012959999,0.4335437,0.00020214482],"about_ca_topic_score_codex":0.00039315005,"about_ca_topic_score_gemma":0.000698417,"teacher_disagreement_score":0.51246697,"about_ca_system_score_codex":0.00019328651,"about_ca_system_score_gemma":0.00039339135,"threshold_uncertainty_score":0.9999986},"labels":[],"label_agreement":null},{"id":"W4416564429","doi":"10.1002/gdj3.70039","title":"Photovoltaic Power and Meteorological Datasets With Snow Detection From the Outdoor Solar Power Laboratories of the Finnish Meteorological Institute","year":2025,"lang":"en","type":"article","venue":"Geoscience Data Journal","topic":"Solar Radiation and Photovoltaics","field":"Computer 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":"Strategic Research Council; Academy of Finland; European Commission","keywords":"Photovoltaic system; Snow; Snow cover; Limiting; Irradiance; Data quality; Snow removal","score_opus":0.0182317902888854,"score_gpt":0.2536273044355106,"score_spread":0.23539551414662524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416564429","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":[0.62153846,0.00064417336,0.3699574,0.0030026366,0.0023261476,0.0003505315,0.002042585,0.000051457864,0.00008662324],"genre_scores_gemma":[0.98812556,0.00008748315,0.008204882,0.0034684443,0.000041121817,0.000005109829,0.00004703293,0.0000036617585,0.0000167128],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979613,0.00031075682,0.0003325581,0.00052104134,0.00056367443,0.000310716],"domain_scores_gemma":[0.9975957,0.00039744526,0.00026740055,0.0014655625,0.00016410906,0.00010975776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016458689,0.0001727961,0.0002083801,0.000066288594,0.0009604574,0.0006601581,0.0037087363,0.00009721112,0.000036511676],"category_scores_gemma":[0.0015553129,0.00008221132,0.0000390683,0.0012263602,0.000806129,0.0017839081,0.0014544133,0.0005838085,0.000003467158],"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":[0.001331013,0.0016205275,0.4803314,0.00005043269,0.0010989427,0.00040871187,0.008889187,0.0006209675,0.22113426,0.03636104,0.12269141,0.12546211],"study_design_scores_gemma":[0.0014582345,0.0005627367,0.63540405,0.00009157732,0.00011518945,0.0003802071,0.000518668,0.020052353,0.016168471,0.0063432944,0.31843045,0.00047474177],"about_ca_topic_score_codex":0.000193393,"about_ca_topic_score_gemma":0.00040487008,"teacher_disagreement_score":0.3665871,"about_ca_system_score_codex":0.000028161423,"about_ca_system_score_gemma":0.00037958252,"threshold_uncertainty_score":0.7387159},"labels":[],"label_agreement":null}]}