{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":234,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":234,"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","author_layer_release":"2026-06-26"},"query_hash":"77435b757413","filters":{"venue":"USGS DOI Tool Production Environment"}},"results":[{"id":"W6968024900","doi":"10.5066/p9j6quf6","title":"North American Breeding Bird Survey Dataset 1966 - 2019, version 2019.0","year":2020,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":53,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Breeding bird survey; Identification (biology); Citizen science; Census; Survey methodology; Survey data collection; Species identification; Aerial survey","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02390093179800859,"gpt":0.2386157576180695,"spread":0.2147148258200609,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007039352,0.001145817,0.0008980022,0.002148646,0.0005661198,0.001103934,0.001751453,0.0007213778,0.04274604],"category_scores_gemma":[0.003649564,0.0004461329,0.0006210171,0.005456709,0.0002127303,0.0009874438,0.0009297166,0.0009141497,0.04640377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005571,"about_ca_system_score_gemma":0.001559592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08742966,"about_ca_topic_score_gemma":0.1432222,"domain_scores_codex":[0.9994001,0.00008667912,0.00008926071,0.0002003121,0.0001408779,0.00008281848],"domain_scores_gemma":[0.9986235,0.0002265484,0.0001680418,0.0001986621,0.0006572736,0.0001259575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000030392,0.00001116668,0.002518542,0.0002982312,0.00002306002,0.00001894732,0.00001604285,0.0001698609,0.00005220668,0.0002376208,0.9941559,0.002467997],"study_design_scores_gemma":[0.000165966,0.00001507876,0.02660129,0.0003144499,0.00003339702,0.00007240229,0.0001225818,0.0007158711,0.0001699136,0.0008363122,0.97092,0.00003281077],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001765262,0.00003904007,0.00005698734,0.00002651879,0.00001518233,0.000006447337,0.9991668,0.00007258645,0.0004399873],"genre_scores_gemma":[0.0005431293,0.00003575372,0.000227629,0.00002779684,0.000006468358,0.0000550325,0.998585,0.00002295694,0.0004963482],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08742966,"threshold_uncertainty_score":0.1738415,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892132674","doi":"10.5066/f7c53hzn","title":"North American Breeding Bird Survey Dataset 1966 - 2015, version 2015.1","year":2016,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":32,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Breeding bird survey; Identification (biology); Sample (material); Census; Survey methodology; Geographic coordinate system; Location data","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02309471513332779,"gpt":0.2598452975934075,"spread":0.2367505824600797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009073482,0.001094776,0.00087617,0.002514215,0.0006523013,0.001249767,0.001807562,0.0006440203,0.03318623],"category_scores_gemma":[0.005027317,0.0004891528,0.0006284886,0.005655407,0.0002231154,0.001187493,0.001181482,0.0009920473,0.03813839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001352982,"about_ca_system_score_gemma":0.002298854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1084655,"about_ca_topic_score_gemma":0.1949271,"domain_scores_codex":[0.9991636,0.0001261111,0.0001329068,0.0002604748,0.0002192129,0.00009775809],"domain_scores_gemma":[0.9979544,0.0002849128,0.0002242538,0.0003176259,0.00105774,0.0001611476],"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":[0.00002178737,0.00001093749,0.002754752,0.00020783,0.00002120673,0.00001240056,0.0000198986,0.0001506019,0.0000445652,0.0002686967,0.9940746,0.002412677],"study_design_scores_gemma":[0.00008819601,0.000009927067,0.02395574,0.0002614842,0.00002906014,0.00005157254,0.0001268327,0.0005353791,0.0001859123,0.0008398476,0.9738837,0.00003244587],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002444195,0.00004208213,0.0001087106,0.00003510799,0.00002170825,0.00001089364,0.9987671,0.0001338812,0.0006361254],"genre_scores_gemma":[0.000522971,0.00003089643,0.0003192177,0.00002725612,0.000005988809,0.00007206243,0.9984292,0.00003046233,0.0005618676],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1084655,"threshold_uncertainty_score":0.2156683,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6911372068","doi":"10.5066/p9h5qeac","title":"Updated Compilation of VS30 Data for the United States","year":2020,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":24,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Metadata; Geological survey; Context (archaeology); Field (mathematics)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.05895140705457529,"gpt":0.2740696332464194,"spread":0.2151182261918441,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001300699,0.0007858921,0.0004963557,0.009607696,0.0005194499,0.001496177,0.0008958776,0.0004275996,0.0187519],"category_scores_gemma":[0.006751472,0.0004557341,0.0004893878,0.01590428,0.0002177715,0.001501974,0.001245165,0.0007904017,0.0161965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001549015,"about_ca_system_score_gemma":0.003647248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08998458,"about_ca_topic_score_gemma":0.05602675,"domain_scores_codex":[0.9983714,0.0001712873,0.0003173791,0.0002283142,0.0007611174,0.0001504293],"domain_scores_gemma":[0.990238,0.0007142997,0.001076509,0.000818273,0.006776189,0.0003767898],"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":[0.0001318229,0.00005032384,0.02183335,0.0006580304,0.00004948736,0.00009194001,0.0003505536,0.0009054557,0.0008150985,0.002552761,0.9111482,0.06141296],"study_design_scores_gemma":[0.00001411975,0.00001663663,0.05530564,0.0002897264,0.00002007568,0.00005653663,0.0003281207,0.0002793264,0.0005014404,0.0005778313,0.9425815,0.00002896782],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006894899,0.0005036189,0.001798093,0.0002886408,0.0002536967,0.00008279805,0.9696688,0.001220937,0.01928851],"genre_scores_gemma":[0.01256802,0.0007547548,0.005391585,0.0002367906,0.00009031651,0.0003385649,0.9726935,0.0005306007,0.007395818],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08998458,"threshold_uncertainty_score":0.1789216,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948931641","doi":"10.5066/p9sjxui1","title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across Western North America from 1985-2023","year":2024,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Rangeland; Biome; Geolocation; Land cover; Compositing; Ancillary data; Projection (relational algebra); Range (aeronautics); Component (thermodynamics)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01523950548793939,"gpt":0.2856732306095439,"spread":0.2704337251216045,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003616921,0.0003373538,0.0001841133,0.0009007427,0.0001868764,0.0003107559,0.0003251242,0.000147516,0.002673186],"category_scores_gemma":[0.0005835084,0.000117387,0.0002197901,0.002418797,0.0000821255,0.0003005292,0.0003279859,0.0002649894,0.001018776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009572575,"about_ca_system_score_gemma":0.0009552949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2580209,"about_ca_topic_score_gemma":0.3584617,"domain_scores_codex":[0.9998492,0.00001519933,0.00001125329,0.00004465065,0.00005938865,0.00002041156],"domain_scores_gemma":[0.9994337,0.00002830492,0.00007675605,0.00004544788,0.0003704996,0.00004526107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003730187,0.0001684013,0.5343754,0.0004481651,0.0003167693,0.0003093401,0.0004782211,0.01257359,0.00309276,0.0008902994,0.3674235,0.0795505],"study_design_scores_gemma":[0.00002059474,0.00002545659,0.9497681,0.00005183663,0.00003940838,0.00004270973,0.0001959057,0.007358192,0.0006451686,0.0001681735,0.04166768,0.00001691342],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2592754,0.0003030677,0.00160967,0.0002528495,0.00004743399,0.00009758606,0.7311844,0.0006095681,0.006620009],"genre_scores_gemma":[0.2691498,0.0003184199,0.00600261,0.0001244224,0.00003075981,0.0002732617,0.7196479,0.00006374213,0.004389039],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2580209,"threshold_uncertainty_score":0.5130382,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892215734","doi":"10.5066/f7w0944j","title":"North American Breeding Bird Survey Dataset 1966 - 2016, version 2016.0","year":2018,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Breeding bird survey; Identification (biology); Census; Sample (material); Survey methodology; Breeding pair","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02387777601786979,"gpt":0.246251388679382,"spread":0.2223736126615122,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006748121,0.001166561,0.0009119952,0.002313248,0.0005551838,0.001066244,0.001662024,0.0005980183,0.03260525],"category_scores_gemma":[0.003964213,0.0004391606,0.0006135318,0.005382367,0.0002061072,0.0009334658,0.0009081915,0.0009268849,0.03773545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016766,"about_ca_system_score_gemma":0.001835464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09355478,"about_ca_topic_score_gemma":0.1758902,"domain_scores_codex":[0.9994137,0.00008151984,0.00008930638,0.0001833969,0.0001560579,0.00007588533],"domain_scores_gemma":[0.9983227,0.0002417098,0.0002144488,0.0002407462,0.0008349832,0.0001454344],"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":[0.00003624232,0.00001572097,0.004530327,0.0002890467,0.00002803018,0.00002010241,0.00002344197,0.0002154781,0.00005573558,0.0002494096,0.9915932,0.002943371],"study_design_scores_gemma":[0.0001580968,0.00001498644,0.03726155,0.0003387388,0.00003937459,0.00007354949,0.0001495729,0.000856074,0.0002304558,0.0008498321,0.9599931,0.00003466038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00028149,0.00003753794,0.00007679364,0.00002417058,0.00001438897,0.000008623045,0.9989302,0.00009646689,0.0005302627],"genre_scores_gemma":[0.0006321406,0.00003529909,0.0002580275,0.00001950793,0.000005999038,0.00005833182,0.9984212,0.00002445481,0.0005450243],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09355478,"threshold_uncertainty_score":0.1860205,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948549530","doi":"10.5066/p9y8kzj9","title":"Location data for whooping cranes of the Aransas-Wood Buffalo Population, 2009-2018","year":2020,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Wildlife; Location data; Fish <Actinopterygii>; National park; Aerial survey; Grus (genus)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.04578438088840116,"gpt":0.2517500584800088,"spread":0.2059656775916077,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004134962,0.0007673323,0.000660985,0.003149106,0.0005454971,0.0008458884,0.001086131,0.0004331279,0.0323582],"category_scores_gemma":[0.002574282,0.0004100806,0.000393679,0.006135405,0.0001626162,0.0006450478,0.0009315459,0.0006885736,0.01983797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226622,"about_ca_system_score_gemma":0.001902654,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.146768,"about_ca_topic_score_gemma":0.1961888,"domain_scores_codex":[0.9995302,0.00003154809,0.00007490505,0.0001312662,0.0001458363,0.00008625942],"domain_scores_gemma":[0.9986089,0.0001298735,0.000282799,0.000163993,0.0006803101,0.0001340595],"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":[0.0001011865,0.00003721979,0.02158342,0.0007234938,0.0000492358,0.00008765972,0.0001237947,0.0004161129,0.0002356391,0.0007091251,0.9678409,0.008092213],"study_design_scores_gemma":[0.0001766625,0.00002581396,0.1219664,0.0005455027,0.00004595815,0.0001464474,0.0005394521,0.0004742421,0.0004733449,0.0003779299,0.8751904,0.00003783271],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009823751,0.00004267809,0.00003682858,0.00002009049,0.0000132942,0.00001100431,0.9978451,0.00004243498,0.001006123],"genre_scores_gemma":[0.003081824,0.00009974602,0.0002587886,0.00002113623,0.000007180131,0.0001315887,0.9949428,0.0000212376,0.001435614],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.853232,"threshold_uncertainty_score":0.2918274,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892361582","doi":"10.5066/p9phpk4f","title":"CONUS404: Four-kilometer long-term regional hydroclimate reanalysis over the conterminous United States (ver. 3.0, June 2026)","year":2023,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Weather Research and Forecasting Model; Forcing (mathematics); Climate model; Raw data; Water resources; Climate change; Hydrology (agriculture); Representation (politics)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03416817495034349,"gpt":0.2591256073269353,"spread":0.2249574323765919,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007896,0.001009704,0.001018854,0.00171842,0.0005671697,0.001204514,0.002181527,0.0007418154,0.01909294],"category_scores_gemma":[0.001686486,0.0006273698,0.0006134444,0.005747287,0.0002582481,0.001191767,0.0007232388,0.001260549,0.01030451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353432,"about_ca_system_score_gemma":0.003735697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1741109,"about_ca_topic_score_gemma":0.1525311,"domain_scores_codex":[0.9996142,0.00005594075,0.00003756698,0.00008708135,0.0001426433,0.00006247532],"domain_scores_gemma":[0.9990335,0.00007102411,0.0001087875,0.0001571201,0.0005266743,0.0001030812],"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":[0.0001039016,0.00002939497,0.006657084,0.0003466104,0.00008994286,0.00005130687,0.00005087266,0.004513296,0.0003540282,0.001365861,0.9795813,0.006856321],"study_design_scores_gemma":[0.0003355618,0.00002019284,0.04969645,0.0001987706,0.00004480763,0.00004984466,0.0001697958,0.009756735,0.0009557999,0.001893981,0.9367989,0.00007919798],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001857084,0.0000822731,0.0004364788,0.00008756398,0.00007531129,0.00002472023,0.9949192,0.0005621166,0.001955176],"genre_scores_gemma":[0.007678342,0.0001118485,0.001903472,0.00006227671,0.00002819303,0.0001505583,0.988918,0.0002426628,0.000904643],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1741109,"threshold_uncertainty_score":0.346195,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948292165","doi":"10.5066/p9xbbbuu","title":"Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2018 - February 2019","year":2021,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Radiometric dating; Alluvium; Terrain; Ridge; Background radiation; Radon; Altitude (triangle); Aerial survey","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01065522918870259,"gpt":0.2002948804106075,"spread":0.1896396512219049,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001456349,0.0002478453,0.00009609215,0.00134726,0.0009527389,0.0003661361,0.0003218339,0.0002501808,0.00194925],"category_scores_gemma":[0.0003384315,0.0001516437,0.00007688685,0.001216768,0.0001551159,0.0002470032,0.0005967382,0.0002418041,0.0007234887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001253276,"about_ca_system_score_gemma":0.001780374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2575774,"about_ca_topic_score_gemma":0.5964376,"domain_scores_codex":[0.9998454,0.000009907439,0.000006911378,0.00003799333,0.00007496576,0.00002480272],"domain_scores_gemma":[0.9996158,0.0000104599,0.00004388188,0.00001539866,0.0002637296,0.00005072685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001748902,0.0002440984,0.8288409,0.0002212441,0.00006914822,0.001972947,0.005231575,0.001178551,0.04531461,0.0003411039,0.01733635,0.09907466],"study_design_scores_gemma":[0.000005503608,0.0000429227,0.9683248,0.00002733526,0.000006903163,0.0001554915,0.001447503,0.000579045,0.0009103381,0.00003204544,0.02846064,0.000007281152],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9690777,0.0002893106,0.001090229,0.0003677442,0.00002299464,0.0001817391,0.01423722,0.0001136747,0.01461937],"genre_scores_gemma":[0.9476693,0.0004948066,0.007035998,0.0002159333,0.00005346906,0.0002909208,0.0220283,0.0000334492,0.02217784],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.2575774,"threshold_uncertainty_score":0.5121562,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892193405","doi":"10.5066/f7251h3w","title":"Seismogenic Landslides, Debris Flows, and Outburst Floods in the Western United States and Canada from 1977 to 2017","year":2017,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Event (particle physics); Table (database); Debris; Data file; Quality (philosophy)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01444674010519881,"gpt":0.2325919012122104,"spread":0.2181451611070116,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003989493,0.0006644622,0.0004097477,0.007115012,0.002249425,0.001693291,0.001017052,0.0002909273,0.00414299],"category_scores_gemma":[0.002305092,0.000283472,0.000368684,0.01242226,0.0005444335,0.0007551548,0.001390031,0.0006866909,0.00103981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01563985,"about_ca_system_score_gemma":0.03257037,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9943016,"about_ca_topic_score_gemma":0.9970408,"domain_scores_codex":[0.9993413,0.000018057,0.0000631681,0.00007741555,0.0003041084,0.0001959451],"domain_scores_gemma":[0.9966936,0.0001166311,0.0002578873,0.00007114743,0.002547411,0.0003134284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000224588,0.00007083984,0.5931549,0.0010523,0.000264228,0.001021999,0.004080548,0.001659923,0.0005748618,0.001929917,0.3402351,0.05573076],"study_design_scores_gemma":[0.00001700628,0.00001211875,0.8523456,0.0005487517,0.00009622097,0.0001951086,0.008758994,0.0005730976,0.0005178752,0.0001829389,0.1367016,0.00005077854],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1804405,0.002239564,0.0004805368,0.0007928212,0.0002112809,0.000121075,0.7909205,0.0003733107,0.02442043],"genre_scores_gemma":[0.3140981,0.00650884,0.001194214,0.0004245816,0.000130475,0.0001737057,0.6555744,0.0001942936,0.02170141],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01563985,"threshold_uncertainty_score":0.1134756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948236776","doi":"10.5066/p9hqkkfo","title":"Chronic Wasting Disease distribution in the United States by state and county (ver. 3.0, June 2025)","year":2024,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Chronic wasting disease; Distribution (mathematics); Wasting; Disease; Wildlife disease; Disease surveillance; Wildlife; Disease control","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.009062151116936923,"gpt":0.2263690242229316,"spread":0.2173068731059947,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007354285,0.00101067,0.001087813,0.003086022,0.0005688973,0.001822146,0.001359657,0.001072103,0.07074777],"category_scores_gemma":[0.005203182,0.0006137149,0.0008787488,0.007970726,0.0002205208,0.001168015,0.001467146,0.001148929,0.03339947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001263978,"about_ca_system_score_gemma":0.002227932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08355373,"about_ca_topic_score_gemma":0.08894052,"domain_scores_codex":[0.9993141,0.00007822563,0.0001503865,0.0001848347,0.0001415411,0.0001310442],"domain_scores_gemma":[0.9983422,0.0003365926,0.0003089457,0.0002191654,0.0006211685,0.0001720374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00005468727,0.00001352855,0.004184111,0.0009422473,0.00005018147,0.00002633126,0.00003568985,0.0002365961,0.00006520296,0.0004185805,0.9914815,0.002491403],"study_design_scores_gemma":[0.0003962028,0.00002903904,0.04967505,0.001734794,0.00009535047,0.0001364338,0.0002965069,0.0005671745,0.0002604474,0.001242391,0.9455101,0.0000564866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001257224,0.0000406465,0.00001578424,0.00002913695,0.000009595279,0.000005751693,0.999384,0.00003622705,0.0003531318],"genre_scores_gemma":[0.001063928,0.000110111,0.000145544,0.00006061215,0.000007972344,0.00009544727,0.997897,0.00002909817,0.0005903036],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08355373,"threshold_uncertainty_score":0.2366748,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6910858532","doi":"10.5066/p9yyvv7r","title":"Selenium and mercury in the Kootenai River, Montana and Idaho, 2018-2019","year":2019,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Tributary; Mercury (programming language); Selenium; Surface runoff; Hydrology (agriculture); Water quality; Fish <Actinopterygii>","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01209757078014122,"gpt":0.2140707065717739,"spread":0.2019731357916326,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002385845,0.0004426731,0.0003864679,0.002585404,0.0005239455,0.001026614,0.0007599309,0.0003676784,0.007407174],"category_scores_gemma":[0.0008448685,0.0002263517,0.0003575318,0.005989396,0.0002115057,0.0004114749,0.001043962,0.0004861692,0.003649605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002239484,"about_ca_system_score_gemma":0.00273982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4087683,"about_ca_topic_score_gemma":0.5224063,"domain_scores_codex":[0.9998112,0.00001562413,0.00002107436,0.00005231813,0.00005974781,0.00004007737],"domain_scores_gemma":[0.9995494,0.00004390048,0.00007015036,0.00004185348,0.0002379177,0.00005660194],"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":[0.0002924838,0.0001193072,0.199204,0.001548468,0.0002107365,0.0003132234,0.0007246027,0.001361222,0.001045058,0.001251756,0.7736567,0.02027236],"study_design_scores_gemma":[0.0001170368,0.00002231766,0.4956168,0.0005070806,0.00008048348,0.0001404529,0.002287416,0.001152652,0.0009369102,0.0005051124,0.4985857,0.00004820159],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01433433,0.0001411328,0.00004305761,0.000280051,0.0000269074,0.00001701626,0.982098,0.0001302397,0.002929284],"genre_scores_gemma":[0.01838844,0.0002701443,0.000343931,0.0001217198,0.0000169722,0.0001371529,0.9769902,0.00003718328,0.003694359],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4087683,"threshold_uncertainty_score":0.8127779,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6929493253","doi":"10.5066/p9wwv93s","title":"U-Pb Isotopic Data and Ages of Zircon and Titanite from Rocks from the Yukon-Tanana Upland, Alaska","year":2020,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Zircon; Titanite; Igneous rock; Sedimentary rock; Uranium; Thorium; Apatite; Mineral; Table (database)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02341033350196595,"gpt":0.2321491903888196,"spread":0.2087388568868536,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002105983,0.0002069996,0.0001942637,0.002970211,0.0005467229,0.0004818079,0.0003033448,0.0001342097,0.003898702],"category_scores_gemma":[0.0005382716,0.0001669368,0.0001756094,0.003348531,0.0001506872,0.0002716191,0.0003377853,0.00009634447,0.001278958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006945242,"about_ca_system_score_gemma":0.0009375043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1089789,"about_ca_topic_score_gemma":0.269875,"domain_scores_codex":[0.9998029,0.000006440868,0.00005510945,0.00004621434,0.00007356267,0.00001589876],"domain_scores_gemma":[0.999279,0.00008045842,0.0001352166,0.00009427386,0.0003695544,0.00004164919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000434765,0.00006697713,0.8802402,0.0003490532,0.0001802195,0.000367187,0.001114687,0.001716615,0.01644955,0.000293196,0.009750416,0.08903716],"study_design_scores_gemma":[0.00001875072,0.00004583362,0.9553724,0.00004943745,0.00007236798,0.0002141756,0.0007317004,0.0003976153,0.005464128,0.0001220121,0.03748892,0.00002256256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.7962077,0.0004125519,0.000891686,0.00003130463,0.00002241976,0.00004284838,0.1911721,0.0002236495,0.01099573],"genre_scores_gemma":[0.6442134,0.0008281458,0.00425325,0.00004472198,0.00001155675,0.0001102839,0.3389288,0.00009945204,0.01151031],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1089789,"threshold_uncertainty_score":0.2166891,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6929665040","doi":"10.5066/p970gdd5","title":"National-Scale Geophysical, Geologic, and Mineral Resource Data and Grids for the United States, Canada, and Australia: Data in Support of the Tri-National Critical Minerals Mapping Initiative (ver 1.1, March 2025)","year":2025,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Mathematics Education and Teaching Techniques","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Prospectivity mapping; Geological survey; Mineral exploration; Geographic information system; Resource (disambiguation); Raster data; Raster graphics; Decision support system; Data management","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.1130027123047123,"gpt":0.3695167620784021,"spread":0.2565140497736899,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001147684,0.001022635,0.0008114348,0.004559437,0.001626854,0.001843742,0.002359314,0.0006586635,0.02196323],"category_scores_gemma":[0.006433328,0.0008504628,0.0006377137,0.01291465,0.0004293486,0.001161764,0.001677895,0.001657232,0.01268292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01225249,"about_ca_system_score_gemma":0.04718761,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9668058,"about_ca_topic_score_gemma":0.9678892,"domain_scores_codex":[0.9985279,0.00004805251,0.00009002211,0.0001290196,0.0009584833,0.0002465546],"domain_scores_gemma":[0.9896235,0.0002294943,0.0003883441,0.0005267391,0.008489977,0.0007418856],"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":[0.00006123901,0.00004207009,0.006813447,0.0002049886,0.00003691605,0.00003434993,0.0001633987,0.0008924645,0.0002340451,0.001422241,0.9816279,0.008467004],"study_design_scores_gemma":[0.00008888532,0.00001235396,0.09653606,0.00022874,0.00003302844,0.00003941603,0.0006560624,0.001924052,0.001242911,0.000766171,0.8983905,0.00008180647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00128598,0.00004355265,0.0002366649,0.0001246811,0.00003734137,0.00006043326,0.9943401,0.0002665696,0.003604583],"genre_scores_gemma":[0.003361373,0.0001171169,0.001805534,0.00005420842,0.000006268334,0.0001122279,0.9907879,0.0001206245,0.003634684],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03319424,"threshold_uncertainty_score":0.08889848,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6929573848","doi":"10.5066/p9e44ctq","title":"Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020","year":2021,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Sperm and Testicular Function","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Radiometric dating; Alluvium; Basement; Terrain; Alluvial plain; Loess; Block (permutation group theory); Background radiation","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01127705364609602,"gpt":0.2186955354215223,"spread":0.2074184817754262,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001280338,0.0001858332,0.0000713357,0.001236491,0.0008708552,0.0003677711,0.0002887732,0.0002203432,0.002385417],"category_scores_gemma":[0.0002826373,0.0001404225,0.00006754057,0.001079076,0.0001391497,0.0001866458,0.0004752401,0.0001944976,0.0008332002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001587207,"about_ca_system_score_gemma":0.001902253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3624263,"about_ca_topic_score_gemma":0.733269,"domain_scores_codex":[0.9998775,0.000008962837,0.000005543582,0.00002611407,0.00006156089,0.00002038241],"domain_scores_gemma":[0.9996138,0.00001162375,0.00004322702,0.00001424923,0.0002608525,0.00005624216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001864091,0.0002008605,0.853584,0.0001512268,0.00006177608,0.001371883,0.003125769,0.001385914,0.03567785,0.000280859,0.01628077,0.08769261],"study_design_scores_gemma":[0.000003621956,0.00003199818,0.9783961,0.00001645876,0.000004942253,0.0001120857,0.0009876927,0.0005236164,0.0007681759,0.00002134999,0.01912801,0.000005919329],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.962998,0.0002689108,0.0008005212,0.0004096003,0.00001628439,0.0001385779,0.01608561,0.00009664727,0.01918593],"genre_scores_gemma":[0.9388334,0.0004218405,0.005297573,0.0001962647,0.00003062625,0.0001854331,0.0217423,0.00002443713,0.03326809],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.3624263,"threshold_uncertainty_score":0.7206334,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6929925981","doi":"10.5066/f71r6nk8","title":"North American Breeding Bird Survey Dataset 1966 - 2015, version 2015.0","year":2016,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Breeding bird survey; Identification (biology); Census; Sample (material); Survey methodology; Breeding pair","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02309471513332779,"gpt":0.2598452975934075,"spread":0.2367505824600797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007083339,0.001093671,0.0008867173,0.002247858,0.0005200137,0.001020421,0.001626107,0.0005774114,0.02657189],"category_scores_gemma":[0.003667947,0.0004441373,0.000630839,0.004895058,0.0001971205,0.000884477,0.0009165027,0.0008424151,0.02916484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001070464,"about_ca_system_score_gemma":0.001794742,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1021062,"about_ca_topic_score_gemma":0.1916236,"domain_scores_codex":[0.9993955,0.00008913542,0.00009411795,0.0001898788,0.0001546415,0.00007678342],"domain_scores_gemma":[0.9984754,0.0002126835,0.0002035382,0.0002152802,0.0007631865,0.0001298986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003905661,0.00001781233,0.005923825,0.0003324448,0.00003943879,0.00002214767,0.00002652757,0.000284947,0.00006735871,0.0002984113,0.9896502,0.003297785],"study_design_scores_gemma":[0.0001672201,0.0000184551,0.04958889,0.0003840844,0.00005272241,0.0000870333,0.000168248,0.001157289,0.0002743509,0.0009582136,0.9471003,0.00004316365],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003548005,0.00004671864,0.00009322869,0.00002424177,0.00001392061,0.00001003089,0.9988225,0.0001024462,0.0005320325],"genre_scores_gemma":[0.0007709877,0.00003661491,0.0002951017,0.00002190834,0.000005434375,0.0000589181,0.9983026,0.00002129603,0.0004871001],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8978938,"threshold_uncertainty_score":0.2030237,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6967043805","doi":"10.5066/p9csm0kn","title":"Pacific Walrus Coastal Haulout Occurrences Interpreted from Satellite Imagery","year":2022,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Satellite imagery; Satellite; Polygon (computer graphics); Synthetic aperture radar; Geospatial analysis; Earth observation; Table (database); Terrain","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01277130136049385,"gpt":0.2252718134176153,"spread":0.2125005120571214,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000249218,0.0008371,0.0003885523,0.002851109,0.0004223271,0.0008222328,0.0009018411,0.0004519614,0.01833067],"category_scores_gemma":[0.001134479,0.0002915312,0.0003734019,0.004666442,0.0001942124,0.0005305745,0.0009244758,0.000581095,0.01403825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007011727,"about_ca_system_score_gemma":0.001080496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08818868,"about_ca_topic_score_gemma":0.1276707,"domain_scores_codex":[0.9997262,0.00002512773,0.00002332087,0.00008944427,0.00008367351,0.00005218582],"domain_scores_gemma":[0.9994742,0.00008071771,0.00008179562,0.0001151006,0.0001880839,0.00006003113],"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":[0.0001064064,0.000045039,0.01203445,0.0005830961,0.0000555774,0.0001262118,0.0002115275,0.001230316,0.000613139,0.0005332513,0.971639,0.012822],"study_design_scores_gemma":[0.0001025714,0.0000210178,0.08596954,0.0003199801,0.00003598656,0.0001175611,0.0006342268,0.001790445,0.001075522,0.0005189456,0.9093741,0.00004016417],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002679861,0.00003616501,0.00007223211,0.00002172911,0.00001304162,0.00001206853,0.9956698,0.0002339171,0.001261077],"genre_scores_gemma":[0.002410406,0.00003323987,0.0003246856,0.000009433704,0.00000381725,0.00004193919,0.9965125,0.00004496158,0.0006190876],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08818868,"threshold_uncertainty_score":0.1753507,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892229163","doi":"10.5066/p97epu2f","title":"Thermal Data and Navigation for T-3 (Fletcher's) Ice Island Arctic Ocean Heat Flow Studies, 1963-73 (ver. 1.1 December 2022)","year":2019,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Heat flow; Arctic; Ridge; Sea ice; Table (database); Arctic ice pack; The arctic","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.04121266560423327,"gpt":0.2872552730079942,"spread":0.246042607403761,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005745257,0.0008246577,0.0005292451,0.002403956,0.0005370933,0.0007705733,0.0008473195,0.0004561805,0.02564734],"category_scores_gemma":[0.001485546,0.0004301961,0.0004102468,0.005459477,0.000179629,0.0008314546,0.0007084531,0.0009291166,0.02110154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001401411,"about_ca_system_score_gemma":0.002884662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2349126,"about_ca_topic_score_gemma":0.3058589,"domain_scores_codex":[0.9995934,0.00002549878,0.00004059241,0.00008615076,0.0001933867,0.00006094479],"domain_scores_gemma":[0.9989435,0.00006118648,0.0001199583,0.0001633841,0.0006183029,0.00009377244],"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":[0.00009419562,0.00004996674,0.01753448,0.0003261703,0.00002993709,0.00004415757,0.0001116395,0.0009789789,0.0006331716,0.0007895354,0.96013,0.01927775],"study_design_scores_gemma":[0.00008044644,0.00001835957,0.0844337,0.0002353086,0.00001639603,0.00003368247,0.0001867945,0.0005874652,0.0007856833,0.0003234804,0.9132702,0.00002849519],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001077406,0.00004362479,0.0001738759,0.0000267646,0.00004037725,0.00002427372,0.9955711,0.0001504272,0.002892271],"genre_scores_gemma":[0.00366913,0.00006796367,0.001335753,0.00002260094,0.00001160529,0.0001408735,0.9924415,0.00009130267,0.002219295],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2349126,"threshold_uncertainty_score":0.4670905,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6911116875","doi":"10.5066/p9ygdhiq","title":"A bathymetric terrain model of multibeam sonar data collected between 2005 and 2018 along the Queen Charlotte Fault System in the Eastern Gulf of Alaska from Cross Sound, Alaska to Queen Charlotte Sound, Canada","year":2022,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Bathymetry; Terrain; Submarine pipeline; Queen (butterfly); Sonar; Bathymetric chart; Fault (geology); Shapefile","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02910102412349064,"gpt":0.2458604701363484,"spread":0.2167594460128578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000273251,0.0008909543,0.0005040598,0.00246375,0.0006566319,0.0009008346,0.001542824,0.0005563757,0.0232917],"category_scores_gemma":[0.001220484,0.0005216791,0.0005909202,0.007196581,0.0002292626,0.0005287117,0.0006127283,0.0008513968,0.01658288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003453166,"about_ca_system_score_gemma":0.006190871,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7825389,"about_ca_topic_score_gemma":0.8885748,"domain_scores_codex":[0.9997594,0.00001462259,0.00001863511,0.00005818406,0.0000884495,0.0000608206],"domain_scores_gemma":[0.999055,0.00006749757,0.00005620696,0.0001120194,0.000620502,0.0000888061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009391255,0.00007589735,0.01783615,0.0004840084,0.00008016045,0.0001195525,0.0002580482,0.009919284,0.0004131786,0.001333852,0.9578694,0.01151661],"study_design_scores_gemma":[0.0001948845,0.00001718527,0.08592517,0.0004077226,0.00004845394,0.0001113,0.0008683287,0.009199885,0.0006913268,0.0009920925,0.9014655,0.00007813893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001420214,0.00002030191,0.0001400072,0.00002402175,0.00001059046,0.00001223118,0.9972894,0.0001798112,0.0009034279],"genre_scores_gemma":[0.003599423,0.00004586936,0.0006106415,0.0000095164,0.000002067251,0.00004122595,0.9947861,0.00004708968,0.0008580417],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2174611,"threshold_uncertainty_score":0.4374835,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892170809","doi":"10.5066/p9r1l6q7","title":"North American Bird Banding Program Dataset 1960-2020 retrieved 2020-06-26","year":2021,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Data quality; Geological survey; Quality (philosophy); Historical record","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01562393414034987,"gpt":0.2527229487749654,"spread":0.2370990146346156,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006440081,0.001097084,0.0008054828,0.003301462,0.0006154877,0.001226528,0.001402161,0.0006592557,0.05641747],"category_scores_gemma":[0.003390163,0.0005143988,0.0006415659,0.006987511,0.0001806787,0.001062007,0.0009820655,0.0009411142,0.06762467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173367,"about_ca_system_score_gemma":0.002092082,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1082009,"about_ca_topic_score_gemma":0.1349931,"domain_scores_codex":[0.9991781,0.00007999576,0.000110441,0.0002715513,0.0002554207,0.00010453],"domain_scores_gemma":[0.9976048,0.0001880373,0.000301477,0.0003396729,0.00137155,0.0001943549],"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":[0.00003449383,0.00001424385,0.00260362,0.0001928335,0.00002125337,0.00001427135,0.00001379171,0.0001260806,0.00006455381,0.0003054506,0.9932479,0.00336162],"study_design_scores_gemma":[0.0000752581,0.00001355396,0.02881134,0.0002456301,0.00002454956,0.00004418932,0.0001140392,0.0003782597,0.0001697862,0.0004874551,0.9696085,0.00002745448],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000229571,0.00003946243,0.00005453455,0.00003201881,0.00002479019,0.000009335296,0.9983125,0.00009170521,0.001206149],"genre_scores_gemma":[0.0005087778,0.00003792343,0.0002224756,0.00002417577,0.000008612906,0.00005930472,0.9980906,0.00002358338,0.001024436],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8917991,"threshold_uncertainty_score":0.2151421,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892206485","doi":"10.5066/p9o75ydv","title":"North American Bat Monitoring Program (NABat) Master Sample and Grid-Based Sampling Frame","year":2018,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Sampling (signal processing); Sampling frame; Foraging; Sample (material); Frame (networking); Sampling design; Stratified sampling; Scale (ratio)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03487529282059017,"gpt":0.2728012121573322,"spread":0.2379259193367421,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001817993,0.0007459888,0.0007987208,0.003160311,0.0008475246,0.001113674,0.002155665,0.0007483271,0.05020574],"category_scores_gemma":[0.007882504,0.0005503005,0.0005873344,0.00613861,0.0002984901,0.0007565705,0.001269608,0.001269646,0.02317242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001613814,"about_ca_system_score_gemma":0.00287711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0974657,"about_ca_topic_score_gemma":0.1370495,"domain_scores_codex":[0.9989781,0.0002267921,0.0001483018,0.0003117404,0.0002141501,0.0001209497],"domain_scores_gemma":[0.9969128,0.0005967676,0.0002758819,0.0005879847,0.001465717,0.0001609325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00007418894,0.00003849973,0.008808646,0.0003328828,0.00003591476,0.00004734477,0.00008955673,0.001088407,0.0001234671,0.001850424,0.9768764,0.01063419],"study_design_scores_gemma":[0.0001636485,0.00001741515,0.02643589,0.0002952783,0.00003263314,0.00005894486,0.0002599197,0.001677096,0.0002839569,0.001858956,0.9688846,0.00003166416],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001032265,0.00004948324,0.0008606024,0.00009293596,0.00002976861,0.0001507999,0.9957842,0.0001856353,0.001814258],"genre_scores_gemma":[0.003796178,0.0000726801,0.004004142,0.00007458375,0.00001604995,0.00156392,0.9881762,0.0001075142,0.002188749],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0974657,"threshold_uncertainty_score":0.1937968,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6910977005","doi":"10.5066/f7ft8k74","title":"Map of whooping crane migration corridor","year":2018,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Wildlife refuge; Endangered species; Wildlife; Population; National park; Wildlife conservation; Easement; Aerial survey; Wildlife management","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01462434778024586,"gpt":0.2282975131170871,"spread":0.2136731653368412,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003097934,0.0009102724,0.0004610858,0.003651506,0.0005654352,0.001098618,0.0008567093,0.0004259841,0.06240122],"category_scores_gemma":[0.001995649,0.0003439657,0.0003693161,0.007480814,0.0001941195,0.0006687858,0.000855559,0.0007975544,0.0320331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008692995,"about_ca_system_score_gemma":0.002018077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1502244,"about_ca_topic_score_gemma":0.2316242,"domain_scores_codex":[0.9997335,0.00003290683,0.00003534153,0.00008014534,0.00006705589,0.00005089754],"domain_scores_gemma":[0.9990363,0.0001346194,0.0001212522,0.0001072918,0.0004990683,0.0001014239],"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":[0.00003377792,0.00001560076,0.004360549,0.0003773536,0.00001766021,0.0000420312,0.00007189584,0.0003701439,0.00008819815,0.0004573406,0.9898595,0.004305968],"study_design_scores_gemma":[0.00009666981,0.00001029257,0.03252086,0.0003729839,0.00001711826,0.00007246741,0.0003881368,0.000527518,0.0001771716,0.0003990023,0.9653956,0.00002223382],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004476231,0.00004229386,0.00003959508,0.00003629466,0.00001025481,0.00001117439,0.9978784,0.00008236332,0.001452022],"genre_scores_gemma":[0.001416369,0.00009207956,0.00027672,0.00001631189,0.000003662509,0.0000638249,0.9968946,0.0000309971,0.001205379],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1502244,"threshold_uncertainty_score":0.2987,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6947959507","doi":"10.5066/f7q52nxk","title":"Simulation of Groundwater Flow, and Analysis of Projected Water Use for the Rush Springs Aquifer, Western Oklahoma","year":2018,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Aquifer; MODFLOW; Groundwater; Hydrology (agriculture); Groundwater flow; Inflow; Groundwater model; Spring (device); Aquifer test; Outflow","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02776437360045957,"gpt":0.2553291240344994,"spread":0.2275647504340398,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002107697,0.0004084697,0.0002260368,0.0004076079,0.0009085831,0.0005758938,0.0004711705,0.0005161338,0.002241793],"category_scores_gemma":[0.0005164507,0.0002541278,0.0004010048,0.0006514647,0.0003404013,0.0003625985,0.0003348961,0.0003637312,0.0001555924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003725969,"about_ca_system_score_gemma":0.001887131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3287631,"about_ca_topic_score_gemma":0.3052019,"domain_scores_codex":[0.999904,0.00002082942,0.000005887296,0.00002582022,0.00001496433,0.00002847802],"domain_scores_gemma":[0.9998544,0.00005192669,0.00001420958,0.00001270446,0.00004629904,0.00002050518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001651755,0.0001224916,0.02819266,0.0000471223,0.00003916446,0.0002340296,0.0001644454,0.9587773,0.003031268,0.001244572,0.00212792,0.005853913],"study_design_scores_gemma":[0.0001133176,0.00009437936,0.02877897,0.00001289824,0.00002905649,0.00002009188,0.0003679699,0.9658777,0.002183281,0.0004364474,0.002057372,0.00002852535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9917675,0.00003427691,0.0008546495,0.0001378317,0.00001099164,0.00003940322,0.003658949,0.0001668147,0.003329629],"genre_scores_gemma":[0.994928,0.00004495924,0.001700673,0.00001568879,0.000002955761,0.0000594527,0.001637452,0.00001344989,0.001597345],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.3287631,"threshold_uncertainty_score":0.653699,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2791359955","doi":"10.5066/f7f18wv0","title":"Peregrine Falcon (Falco peregrinus) mtDNA and Microsatellite Genetic Data, Alaska, Canada and Russia, 1880-2012","year":2017,"lang":"en","type":"article","venue":"USGS DOI Tool Production Environment","topic":"Ecology and biodiversity studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Subspecies; Peregrinus; Microsatellite; Geography; Zoology; Mitochondrial DNA; Biology; Genetics; Gene","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01614445388422041,"gpt":0.198143322314695,"spread":0.1819988684304746,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002088098,0.0002396384,0.0002072873,0.003130653,0.001030569,0.0003208995,0.0003604185,0.0001554373,0.003231759],"category_scores_gemma":[0.0005435388,0.0002003341,0.0001373328,0.00327176,0.0002147814,0.0002078502,0.0003579891,0.0002199318,0.0005773057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002387727,"about_ca_system_score_gemma":0.005133274,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8844594,"about_ca_topic_score_gemma":0.9583804,"domain_scores_codex":[0.9998048,0.00001120921,0.00001910214,0.00004849923,0.0000734073,0.000043076],"domain_scores_gemma":[0.9991974,0.0000474076,0.0001533492,0.00003441482,0.0004575677,0.0001099805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002904928,0.00006584773,0.8836623,0.0003866154,0.000207589,0.0005304856,0.002819853,0.001608188,0.002561103,0.0006768995,0.04872749,0.05846311],"study_design_scores_gemma":[0.000005548576,0.00001274271,0.9712389,0.00006389238,0.00004220182,0.00013937,0.001215396,0.000163328,0.0002633412,0.00004891726,0.02679587,0.00001057766],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.722828,0.001239829,0.0005889114,0.0001159948,0.00004194129,0.00008325039,0.2558763,0.0001123899,0.01911333],"genre_scores_gemma":[0.6974705,0.001427202,0.00367027,0.00007725478,0.00001887716,0.0002277067,0.2792439,0.00003891667,0.01782535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1155406,"threshold_uncertainty_score":0.232442,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892198939","doi":"10.5066/f7td9w8z","title":"Alaska Land Carbon Assessment Data","year":2017,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Primary production; Vegetation (pathology); Carbon cycle; Ecosystem; Soil carbon; Climate change; Carbon sequestration; Ecosystem respiration; Terrestrial ecosystem; Carbon sink","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.04171326351498404,"gpt":0.3092992357392769,"spread":0.2675859722242929,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008945202,0.0008162421,0.0004554802,0.005083369,0.0006704068,0.001140231,0.0008195395,0.0003691192,0.04879689],"category_scores_gemma":[0.002043829,0.0002387346,0.0003117417,0.007962255,0.0001186143,0.001304807,0.0006542348,0.000697023,0.022224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001115229,"about_ca_system_score_gemma":0.002519613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07969645,"about_ca_topic_score_gemma":0.07816644,"domain_scores_codex":[0.9993767,0.0000893035,0.00007517519,0.0001340814,0.0002664565,0.00005819275],"domain_scores_gemma":[0.9977036,0.0001627753,0.0002004024,0.0001975647,0.00160776,0.0001278379],"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":[0.00009634325,0.00005818584,0.02056356,0.000589296,0.00009439955,0.0001397435,0.000152473,0.002293728,0.0003059202,0.005194618,0.9059473,0.06456445],"study_design_scores_gemma":[0.00001997852,0.00001559596,0.03517063,0.0002361935,0.00004411309,0.00007324437,0.0002861225,0.0009246545,0.00044322,0.002192214,0.9605608,0.00003328818],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004172741,0.0006749608,0.0007369138,0.000245638,0.0002152939,0.00007407485,0.9214934,0.0004023018,0.07198475],"genre_scores_gemma":[0.01967769,0.001076059,0.004442889,0.0001824834,0.00006392459,0.0003758698,0.9429604,0.0001500101,0.03107074],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07969645,"threshold_uncertainty_score":0.1632419,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2739452558","doi":"10.5066/f7nc5zfm","title":"Active Layer Data from the Yukon River Basin in Alaska and Canada","year":2017,"lang":"en","type":"article","venue":"USGS DOI Tool Production Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Permafrost; Active layer; Structural basin; Watershed; Hydrology (agriculture); Drainage basin; Environmental science; Geology; Climate change; Layer (electronics); Geography; Oceanography; Geomorphology; Cartography","authors":[{"name":"Nicole M. Herman‐Mercer","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0572692223078977,"gpt":0.226635612559944,"spread":0.1693663902520463,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001378598,0.0002306171,0.0002140455,0.001693748,0.001050362,0.0007175075,0.0003993951,0.0002626008,0.002322101],"category_scores_gemma":[0.0004868685,0.0001504337,0.0001788009,0.00520924,0.0001972632,0.0003212858,0.000402013,0.0001933652,0.0004827535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00424366,"about_ca_system_score_gemma":0.006563501,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9717377,"about_ca_topic_score_gemma":0.9891,"domain_scores_codex":[0.9998242,0.00000646201,0.00001928549,0.00003996892,0.00007637706,0.00003362964],"domain_scores_gemma":[0.999201,0.00003836124,0.00004973805,0.0000388925,0.0005948516,0.00007714056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002537261,0.00009479425,0.8808084,0.0003617121,0.0002339506,0.0004577506,0.001543421,0.008552969,0.00276294,0.0007168953,0.04952719,0.05468628],"study_design_scores_gemma":[0.00002484618,0.000009204044,0.9652525,0.00006071877,0.00003870759,0.00005265748,0.001913799,0.003277193,0.0006624267,0.0001339767,0.02854149,0.000032562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.7646405,0.0006148128,0.0005541964,0.0001877206,0.00002388134,0.00006560417,0.2194619,0.0002516391,0.01419982],"genre_scores_gemma":[0.8107264,0.0008636644,0.00252958,0.0001017284,0.00000795802,0.00008673677,0.1766724,0.00004104284,0.008970607],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.02826232,"threshold_uncertainty_score":0.05685747,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2897376137","doi":"10.5066/f7w37tgk","title":"Continuous and optimized 3-arcsecond elevation model for United States east and west coasts","year":2017,"lang":"en","type":"article","venue":"USGS DOI Tool Production Environment","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Elevation (ballistics); North American Datum of 1927; Geodetic datum; Shapefile; Digital elevation model; Bathymetry; Geology; Seafloor spreading; Remote sensing; Sea level; Polygon (computer graphics); Vertical deflection; Geodesy; Physical geography; Oceanography; Geography; Metadata","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01582548660840681,"gpt":0.1946205682180894,"spread":0.1787950816096826,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002004786,0.0003312891,0.0002331894,0.0007406376,0.000275022,0.0007495364,0.0006955678,0.0003837402,0.007854328],"category_scores_gemma":[0.001171632,0.0002447228,0.0005367786,0.002723666,0.0001145498,0.0004418529,0.0003535859,0.0005307025,0.003785986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008606054,"about_ca_system_score_gemma":0.001372819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1695566,"about_ca_topic_score_gemma":0.196602,"domain_scores_codex":[0.999872,0.00002165536,0.00001434822,0.00004025786,0.00003415396,0.00001764236],"domain_scores_gemma":[0.9997076,0.00003500873,0.00001808722,0.00005134043,0.0001726106,0.00001527571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002331658,0.0001911386,0.06326863,0.0001345081,0.000132754,0.0002491875,0.0002283743,0.5963128,0.001346679,0.009149305,0.2330228,0.09573076],"study_design_scores_gemma":[0.0001535691,0.00003174007,0.07195678,0.00008620393,0.00004868591,0.00009339793,0.0004202602,0.7923604,0.001408701,0.004327537,0.1290437,0.0000690138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2508183,0.0004522178,0.09775122,0.00102485,0.0004376638,0.0003253537,0.573506,0.01430614,0.06137834],"genre_scores_gemma":[0.5935581,0.0004321079,0.09616938,0.0001445862,0.00005153716,0.0006142546,0.2951553,0.001417859,0.01245696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1695566,"threshold_uncertainty_score":0.3371394,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6930237156","doi":"10.5066/p92xvoof","title":"High-Resolution Aeromagnetic Survey of Mountain Pass, California","year":2018,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Aeromagnetic survey; Magnetometer; Magnetic survey; Azimuth; Elevation (ballistics); Aerial survey","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01648268436833203,"gpt":0.2241386756139225,"spread":0.2076559912455905,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003839717,0.0008440647,0.0006328066,0.00290868,0.000718105,0.001070334,0.001122654,0.0005009087,0.01024336],"category_scores_gemma":[0.001241626,0.0003150024,0.0003191214,0.005777469,0.0002152402,0.0004177299,0.0008174151,0.000746227,0.007660149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089284,"about_ca_system_score_gemma":0.002146424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3403797,"about_ca_topic_score_gemma":0.4549322,"domain_scores_codex":[0.9995992,0.00003171671,0.00002737556,0.0001438428,0.0001244479,0.00007345487],"domain_scores_gemma":[0.9991623,0.00006249997,0.0001099569,0.0001479553,0.0004112982,0.0001060238],"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":[0.00009500855,0.00005502098,0.02782587,0.0003787775,0.00008930224,0.0001264416,0.0001526247,0.00157948,0.000424609,0.000610984,0.9591687,0.009493292],"study_design_scores_gemma":[0.0001216,0.00001627883,0.14712,0.0002880293,0.00004350546,0.0000670116,0.0004286205,0.001309274,0.0004701571,0.0003545454,0.8497366,0.0000444342],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006775656,0.0002451643,0.0001712489,0.00009347279,0.00002802695,0.00001295489,0.988617,0.0004128709,0.003643605],"genre_scores_gemma":[0.008037844,0.0001527908,0.0005697138,0.00002091614,0.00001456305,0.00004006662,0.9896482,0.00007477638,0.001441189],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3403797,"threshold_uncertainty_score":0.676797,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6929749313","doi":"10.5066/f75b01nj","title":"Spatial data for estimating whooping crane migration corridor","year":2018,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Economic and Financial Impacts of Cancer","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Endangered species; Wildlife refuge; Wildlife; Population; National park; Wildlife conservation; Wildlife management; Grus (genus)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.05014034907448749,"gpt":0.2492561433082121,"spread":0.1991157942337247,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001082341,0.0009265754,0.0006471059,0.003330875,0.0004582906,0.001123949,0.001507101,0.0009386986,0.02184234],"category_scores_gemma":[0.006444645,0.0003909458,0.0007574784,0.005512868,0.0002756224,0.0006424715,0.001018474,0.001182101,0.01965481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009971581,"about_ca_system_score_gemma":0.001503682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07251309,"about_ca_topic_score_gemma":0.09663482,"domain_scores_codex":[0.9992847,0.000116305,0.00009489943,0.0001627802,0.0002282047,0.0001130988],"domain_scores_gemma":[0.9977501,0.0004482467,0.0003588669,0.0004503916,0.0007895449,0.0002028362],"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":[0.0000961471,0.00009941925,0.01634563,0.0003879683,0.00006909357,0.00006807013,0.00005716925,0.002533928,0.0001383874,0.001407112,0.9698498,0.008947395],"study_design_scores_gemma":[0.000314152,0.00003690577,0.07103319,0.000395154,0.00004696929,0.0001315189,0.0003737459,0.004395276,0.0004679267,0.001827978,0.9209237,0.00005355422],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001444818,0.00007285347,0.0002038269,0.00008567723,0.00001885899,0.00001809764,0.9972631,0.0001873936,0.0007053322],"genre_scores_gemma":[0.002949461,0.00006085122,0.0006471806,0.00002456329,0.000007590196,0.0001053991,0.9955376,0.00003610263,0.0006312674],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07251309,"threshold_uncertainty_score":0.1441821,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948801633","doi":"10.5066/p13kagu3","title":"Slope-Relief Threshold Landslide Susceptibility Models for the United States and Puerto Rico","year":2024,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Landslide; Digital elevation model; Terrain; Elevation (ballistics); Hazard; Geographic information system; Natural hazard; Digital mapping","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02459691898492632,"gpt":0.2477115044800922,"spread":0.2231145854951658,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000294299,0.0004105434,0.0002528848,0.0005994033,0.0002739431,0.0004946846,0.0008794567,0.0004360209,0.001945943],"category_scores_gemma":[0.0009933988,0.0001841596,0.0005418264,0.0005653212,0.000184484,0.0003423874,0.0004348518,0.0003278158,0.0002629368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295923,"about_ca_system_score_gemma":0.0007368901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2438059,"about_ca_topic_score_gemma":0.1632553,"domain_scores_codex":[0.9999305,0.0000189873,0.000003982581,0.00001853517,0.00001203854,0.00001596482],"domain_scores_gemma":[0.999816,0.00006770204,0.00002784518,0.00001838419,0.00005266022,0.00001742208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005433994,0.00004374047,0.02927529,0.00002671197,0.00005064001,0.0001023493,0.00007594741,0.9526968,0.0004078941,0.002171071,0.003263041,0.01183216],"study_design_scores_gemma":[0.00001143806,0.000005999759,0.0072307,0.000007966952,0.00001216408,0.00001824992,0.00004062806,0.990899,0.00007294329,0.000753222,0.0009407978,0.000006866357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9743822,0.0004974974,0.01133511,0.0009027865,0.00002681982,0.00006103786,0.003715281,0.0004875754,0.008591784],"genre_scores_gemma":[0.9926713,0.0001358325,0.004298571,0.0000431635,0.00000702333,0.00004406871,0.001489181,0.00003563076,0.001275309],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.2438059,"threshold_uncertainty_score":0.4847737,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6911031827","doi":"10.5066/p98q5f6r","title":"Genotypes and cluster definitions for a range-wide greater sage-grouse dataset collected 2005-2017 (ver 1.1, January 2023)","year":2022,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","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 Waterloo","funders":"","keywords":"Genetic diversity; Identification (biology); Microsatellite; Diversity (politics); Prioritization; Cluster (spacecraft); Range (aeronautics); Wildlife","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03387236236352184,"gpt":0.2357031742164261,"spread":0.2018308118529042,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001166081,0.0008616043,0.0005788057,0.002497219,0.0005335712,0.001062074,0.001435619,0.0009133853,0.04144764],"category_scores_gemma":[0.005307551,0.0004413762,0.0006126915,0.004321874,0.0002839838,0.0007584582,0.001387067,0.0009961203,0.02335935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008697494,"about_ca_system_score_gemma":0.001403405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03937114,"about_ca_topic_score_gemma":0.08966681,"domain_scores_codex":[0.9993716,0.0001084989,0.0001078972,0.0002010877,0.0001327159,0.00007821305],"domain_scores_gemma":[0.9982479,0.0005115999,0.0002738982,0.00038023,0.0004580102,0.0001283402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001090369,0.00004660216,0.01440952,0.0008045307,0.00007559518,0.00008935084,0.0002210786,0.0009971387,0.0005384811,0.001951381,0.9725035,0.008253878],"study_design_scores_gemma":[0.0002384956,0.00002911254,0.0577989,0.0004131667,0.00003963049,0.000140891,0.0003372186,0.0009557357,0.0004498607,0.002792584,0.9367561,0.00004835942],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000922484,0.00002956497,0.0002109005,0.00003847851,0.000009487929,0.00001978274,0.9980189,0.0001600309,0.0005904052],"genre_scores_gemma":[0.001560904,0.00002630137,0.001056926,0.00003430339,0.000003846356,0.000158841,0.9965418,0.00006616295,0.0005509131],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04144764,"threshold_uncertainty_score":0.1386561,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6967040740","doi":"10.5066/p9j6xkvs","title":"Quarterly sample results for per- and polyfluoroalkyl substances (PFAS) for locations in Campbell, Wisconsin, 2021-24 (ver. 2.0, March 2025)","year":2025,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Sampling (signal processing); Sample (material); Contamination; Certified reference materials; Groundwater; Certification","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01998174748341274,"gpt":0.2675433359211016,"spread":0.2475615884376889,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009286746,0.0007455098,0.00031413,0.002462341,0.0008673667,0.001085825,0.0007252089,0.0003409963,0.01717838],"category_scores_gemma":[0.001635543,0.0003871591,0.0003451655,0.002847939,0.0001183421,0.000366351,0.0003971957,0.0003081794,0.008753845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002020173,"about_ca_system_score_gemma":0.00229913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2197677,"about_ca_topic_score_gemma":0.3898792,"domain_scores_codex":[0.9986699,0.00006281387,0.0001068423,0.000220949,0.0008456614,0.00009403166],"domain_scores_gemma":[0.997977,0.0000850329,0.000281574,0.0001329978,0.001452509,0.00007097897],"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":[0.001376415,0.0002832941,0.2920935,0.001256594,0.0002112556,0.0004430117,0.001073783,0.001262625,0.06127369,0.0005493551,0.4151269,0.2250496],"study_design_scores_gemma":[0.00004508775,0.0001687728,0.5340046,0.0001570431,0.0001115625,0.0001461415,0.0005168023,0.0004658912,0.02815501,0.00006885494,0.4361036,0.00005665523],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1479055,0.0009531561,0.00493498,0.0002454121,0.000250833,0.0008099162,0.7887394,0.002145129,0.05401563],"genre_scores_gemma":[0.152087,0.001645948,0.02501683,0.0004048532,0.00007521727,0.001251959,0.7287536,0.0007892014,0.08997544],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2197677,"threshold_uncertainty_score":0.436977,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948766767","doi":"10.5066/p9d4ea9w","title":"Airborne electromagnetic, magnetic, and radiometric survey, Shellmound, Mississippi, March 2018 (ver. 2.0, March 2024)","year":2018,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Research Data Management Practices","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Radiometric dating; Terrain; Data processing; Magnetometer; Depth sounding; Thorium; Spectrometer; Background radiation","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03982334589554081,"gpt":0.2859321651403054,"spread":0.2461088192447646,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002867242,0.0003669618,0.000147399,0.001231178,0.0009402735,0.0005329913,0.000386862,0.0002433333,0.01032635],"category_scores_gemma":[0.0006737287,0.0002278751,0.00009403276,0.001660913,0.0001478413,0.0003443453,0.0005358405,0.0003297196,0.005980208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001585417,"about_ca_system_score_gemma":0.00309317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3971611,"about_ca_topic_score_gemma":0.6344525,"domain_scores_codex":[0.9997671,0.00001468313,0.00001168782,0.00005804346,0.0001231248,0.00002533845],"domain_scores_gemma":[0.9993491,0.0000160414,0.00004383847,0.00003887398,0.0005085869,0.00004354729],"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":[0.0003972473,0.0002153843,0.2581322,0.0005129746,0.000100067,0.0012179,0.003153005,0.001922193,0.03510486,0.001199115,0.4994614,0.1985836],"study_design_scores_gemma":[0.00003518594,0.00007054044,0.5351114,0.0001305131,0.00002127913,0.0002126414,0.001545051,0.001776204,0.003277757,0.0002226018,0.4575754,0.0000214415],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2932097,0.0007504319,0.009063543,0.0008598671,0.0002117337,0.0007227743,0.5989922,0.002063202,0.09412643],"genre_scores_gemma":[0.3020919,0.0008635622,0.02610623,0.0004861338,0.0001210185,0.001208308,0.5511048,0.0004655571,0.1175526],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3971611,"threshold_uncertainty_score":0.7896988,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6910958640","doi":"10.5066/p9449euf","title":"Cladophora biomass and supporting data collected in the Great Lakes, 2021","year":2023,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Cladophora; Transect; Biomass (ecology); Benthic zone; Water column; Aquatic plant; Abundance (ecology); Water quality","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03941019225779135,"gpt":0.2722547834613072,"spread":0.2328445912035158,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003569012,0.0006592519,0.0003617664,0.001775228,0.0005534213,0.0007695261,0.000517338,0.0003413144,0.007635021],"category_scores_gemma":[0.001568217,0.0003500456,0.0002880319,0.003217178,0.0001593519,0.0004318321,0.0008670449,0.0004495269,0.00640326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008156422,"about_ca_system_score_gemma":0.001662738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1214075,"about_ca_topic_score_gemma":0.2125888,"domain_scores_codex":[0.9996386,0.00002384038,0.00004778201,0.00009150227,0.0001417747,0.00005650926],"domain_scores_gemma":[0.9988939,0.00006634689,0.0002464595,0.0001289464,0.0005065998,0.0001577929],"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":[0.0002738913,0.00008433911,0.2284864,0.0008510137,0.0001656278,0.0002859494,0.000525117,0.001004254,0.002984004,0.0006120714,0.7406542,0.02407314],"study_design_scores_gemma":[0.00008172675,0.00003112713,0.6687632,0.0001328535,0.00004157524,0.000124864,0.0003918607,0.001346694,0.0008126529,0.0002015297,0.3280402,0.00003171449],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02746785,0.0001091796,0.0001177947,0.00008787623,0.00001825486,0.00004439705,0.9691199,0.0001015503,0.002933112],"genre_scores_gemma":[0.01828366,0.00007026398,0.0005271744,0.00004860073,0.00001226973,0.0001610028,0.9787815,0.0000191965,0.002096364],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1214075,"threshold_uncertainty_score":0.2414017,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948746253","doi":"10.5066/p13qpq5x","title":"Avian Point-Count Data from Boreal Alaska and Maps of Predicted Population Density for Lesser Yellowlegs, Olive-sided Flycatcher, and Rusty Blackbird, 2001-2020","year":2025,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Shapefile; Grid cell; Boreal; Population; Distance sampling; Population density; Breeding bird survey","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01854824800317922,"gpt":0.2629871185941039,"spread":0.2444388705909247,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006222267,0.0006106304,0.0004646172,0.001960704,0.0003864303,0.0006388251,0.0009443333,0.0004140885,0.02949821],"category_scores_gemma":[0.001839206,0.0004470687,0.0004648833,0.004078791,0.0001261233,0.0007315351,0.0004944036,0.0007152931,0.016009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001053196,"about_ca_system_score_gemma":0.00187026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2574433,"about_ca_topic_score_gemma":0.3498663,"domain_scores_codex":[0.999602,0.00003420783,0.00004331028,0.0001134152,0.0001728752,0.00003419394],"domain_scores_gemma":[0.9975715,0.0002442623,0.0002779732,0.0002031013,0.001531935,0.0001712229],"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":[0.0001063628,0.00008780512,0.05163785,0.0005241442,0.00008537532,0.00006829511,0.0001648933,0.00283172,0.0002831272,0.0006537665,0.9264401,0.01711657],"study_design_scores_gemma":[0.0001292402,0.00005859245,0.2276042,0.0004863305,0.00008378091,0.0001123808,0.0007743167,0.004445499,0.000713665,0.0007173214,0.7647848,0.00008998749],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002749897,0.00004730872,0.000234225,0.00003320566,0.00001854817,0.00001962814,0.9942949,0.0003206471,0.002281624],"genre_scores_gemma":[0.006637558,0.00007393765,0.00159905,0.00002589863,0.000006933142,0.0001162362,0.9894236,0.00006300137,0.002053844],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2574433,"threshold_uncertainty_score":0.5118896,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3192274046","doi":"10.5066/p9zgnrtb","title":"Habitat Selection Scenarios for Molting Waterfowl in the Goose Molting Area of the Teshekpuk Lake Special Area, for NPR-A Integrated Activity Plan/Environmental Impact Statement, 2020","year":2020,"lang":"en","type":"article","venue":"USGS DOI Tool Production Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Waterfowl; Shapefile; Goose; Geography; Fishery; Habitat; Shore; Polygon (computer graphics); Occupancy; Population; Ecology; Cartography; Environmental science; Engineering; Biology; Computer science","authors":[{"name":"Vijay P. Patil","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01622377866527768,"gpt":0.2153319218962619,"spread":0.1991081432309842,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000556197,0.000519572,0.0003176238,0.001129706,0.0003824594,0.0005406435,0.001063085,0.0009278326,0.008116659],"category_scores_gemma":[0.001666144,0.0002055136,0.0007158946,0.00143068,0.0001746607,0.0006799063,0.0004348899,0.0005388822,0.001592774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00187614,"about_ca_system_score_gemma":0.001097781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1157836,"about_ca_topic_score_gemma":0.2453829,"domain_scores_codex":[0.9997457,0.00005858873,0.00001907311,0.00007162979,0.0000443329,0.00006069236],"domain_scores_gemma":[0.9995641,0.0001551902,0.00003836325,0.00004445108,0.0001271812,0.00007073903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0008477969,0.0004163583,0.1735904,0.0008366769,0.0002974898,0.0008197077,0.0002761354,0.3513339,0.001002401,0.004553921,0.4443219,0.02170327],"study_design_scores_gemma":[0.0007284429,0.0003730691,0.2292103,0.000637213,0.0001838446,0.0006319703,0.003288588,0.519721,0.002064988,0.007938209,0.2350051,0.0002174004],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1311428,0.0002265338,0.0009643879,0.0005640977,0.00005925419,0.0001149604,0.8592904,0.0004484481,0.007189152],"genre_scores_gemma":[0.2620993,0.0001972345,0.005052576,0.0001331274,0.00001161008,0.0003693288,0.7290867,0.00006636776,0.002983789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1157836,"threshold_uncertainty_score":0.2302194,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892193651","doi":"10.5066/p14umzwu","title":"Mercury concentrations in amphibian tissues and egg masses, fish tissues and sediment in subarctic, freshwater systems near Churchill, Manitoba, 2015-2019","year":2024,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Churchill Northern Studies Centre","funders":"","keywords":"Amphibian; Mercury (programming language); Sediment; Biome; Methylmercury; Fish <Actinopterygii>","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01141903948157301,"gpt":0.2372578831500297,"spread":0.2258388436684567,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004642566,0.0008839133,0.0007441581,0.003423036,0.000754208,0.000807257,0.00184426,0.0005500936,0.02040208],"category_scores_gemma":[0.002380385,0.0005600565,0.0006087509,0.00894817,0.0002380857,0.0004428388,0.0009092344,0.0005578003,0.007743346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004858884,"about_ca_system_score_gemma":0.01006922,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8733081,"about_ca_topic_score_gemma":0.9127923,"domain_scores_codex":[0.9996359,0.00002097149,0.0000425152,0.00009374986,0.0001316399,0.00007510626],"domain_scores_gemma":[0.9977154,0.0001815615,0.0002483279,0.0001532611,0.001503288,0.0001982353],"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":[0.00009846246,0.00003103641,0.03204489,0.001426473,0.0001147091,0.00006261042,0.0001259061,0.0009198035,0.0003298903,0.0004688994,0.9594414,0.004935894],"study_design_scores_gemma":[0.0003064428,0.00003520056,0.387183,0.001356782,0.0001374404,0.00008556082,0.0008776024,0.001053447,0.001130029,0.0004247361,0.6073331,0.00007655643],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000763812,0.00002436067,0.0000177977,0.00003073199,0.000006638885,0.000005418868,0.9987448,0.00003646265,0.00036995],"genre_scores_gemma":[0.003272115,0.0000736357,0.0001937844,0.00004523483,0.000003865196,0.00005816876,0.9948605,0.00002036074,0.001472439],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1266919,"threshold_uncertainty_score":0.2548761,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2888574075","doi":"10.5066/f7dj5dxt","title":"Mapping Data of Polar Bear (Ursus maritimus) Maternal Den Habitat, Arctic Coastal Plain, Alaska","year":2018,"lang":"en","type":"article","venue":"USGS DOI Tool Production Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Shapefile; Interferometric synthetic aperture radar; Wildlife refuge; Digital elevation model; Terrain; Geography; Remote sensing; Arctic; Ursus maritimus; Cartography; Synthetic aperture radar; Habitat; Physical geography; Geology; Computer science; Ecology; Oceanography","authors":[{"name":"George M. Durner","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02751293145436473,"gpt":0.2114922168734093,"spread":0.1839792854190446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008728008,0.0005307017,0.0004498827,0.002920935,0.0007529584,0.0005776379,0.0008792718,0.0002867171,0.04550597],"category_scores_gemma":[0.002875968,0.0005609743,0.0003161266,0.004241863,0.0001650519,0.0006392786,0.0007806586,0.000357846,0.01440447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006325208,"about_ca_system_score_gemma":0.001845059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1258321,"about_ca_topic_score_gemma":0.1764735,"domain_scores_codex":[0.9994954,0.00006557025,0.0001050347,0.0001186167,0.00016513,0.00005021322],"domain_scores_gemma":[0.9969056,0.0005545409,0.0004199853,0.0005094854,0.00140944,0.0002008999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003095382,0.0002196765,0.2713077,0.00077115,0.0001693945,0.0003062118,0.001607806,0.002965597,0.002145223,0.001513754,0.6546617,0.06402232],"study_design_scores_gemma":[0.00009509608,0.00007421603,0.4350377,0.0002425136,0.0001104075,0.000217934,0.001985714,0.001142968,0.002707237,0.0006221948,0.557695,0.00006906982],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01692829,0.00004000136,0.001512244,0.00005980467,0.00003686834,0.0001117231,0.9669763,0.0009236088,0.01341127],"genre_scores_gemma":[0.03780819,0.0001201212,0.007792536,0.00004788546,0.00002026496,0.000669249,0.9419057,0.0003658931,0.01127025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1258321,"threshold_uncertainty_score":0.2501993,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892146267","doi":"10.5066/f7j96592","title":"Airborne electromagnetic and magnetic survey of Delaware Bay and surrounding regions of New Jersey and Delaware, 2022 (ver 2.0, April 2025)","year":2025,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Bay; Terrain; Hydrology (agriculture); Groundwater; Structural basin; Geological survey","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01753230752009764,"gpt":0.2351242640038756,"spread":0.217591956483778,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000293351,0.0007432611,0.0003808142,0.002072745,0.0004474453,0.0008781587,0.001139752,0.0005406677,0.01164029],"category_scores_gemma":[0.0008037366,0.0004075467,0.0003152364,0.004445633,0.0001816783,0.0005544752,0.0008005511,0.0004880578,0.01090619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235056,"about_ca_system_score_gemma":0.001833567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3446625,"about_ca_topic_score_gemma":0.5741832,"domain_scores_codex":[0.9996917,0.00002165472,0.00003010284,0.0000839474,0.0001101418,0.00006245186],"domain_scores_gemma":[0.9993156,0.00004993307,0.00008635933,0.0001327537,0.0003476357,0.00006777576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00009836673,0.00007236747,0.03340001,0.0004541341,0.00007577597,0.0001990735,0.0001803776,0.001618642,0.001370123,0.0004236707,0.9462925,0.01581507],"study_design_scores_gemma":[0.0001025872,0.00001792039,0.1810598,0.0001788949,0.00003632294,0.00008755585,0.0006444664,0.001474898,0.00122559,0.0002819774,0.8148366,0.00005333894],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004085259,0.00004841524,0.0001898375,0.00003663344,0.00001557943,0.00001476756,0.9932555,0.0002593935,0.00209463],"genre_scores_gemma":[0.004332534,0.00003233521,0.0005647021,0.00001869957,0.000003763866,0.00004160687,0.9938778,0.00003236911,0.00109618],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3446625,"threshold_uncertainty_score":0.6853127,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6966765229","doi":"10.5066/p92zoy4d","title":"U-Pb and 40Ar/39Ar Geochronologic Data for Selected Rocks from the Western Alaska Range, Alaska","year":2020,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Zircon; Igneous rock; Bedrock; Absolute dating; Radiometric dating; Geochronology; Isochron dating; Uranium; Isotope","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.0327028934063178,"gpt":0.243043223316587,"spread":0.2103403299102692,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000833365,0.00104878,0.0007097364,0.004690554,0.0006303471,0.0009916693,0.001376754,0.0006229503,0.04779999],"category_scores_gemma":[0.002679431,0.0006590265,0.0004671074,0.009896386,0.0002175127,0.0009745135,0.000961802,0.0007831309,0.03336316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001346719,"about_ca_system_score_gemma":0.002576314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08868564,"about_ca_topic_score_gemma":0.1031906,"domain_scores_codex":[0.9995191,0.0000478656,0.00008655496,0.0001414695,0.0001409998,0.00006406372],"domain_scores_gemma":[0.9981303,0.0003518625,0.0002853015,0.000373262,0.0007111662,0.0001481472],"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":[0.0001174355,0.00005678295,0.01202805,0.001374368,0.00008367666,0.00008552917,0.0002201529,0.001763583,0.0006424805,0.00166417,0.9650609,0.01690283],"study_design_scores_gemma":[0.00005531149,0.0000106118,0.03023094,0.0002843736,0.00003611228,0.00004772484,0.0002340581,0.0004230503,0.0007368422,0.001036861,0.9668702,0.00003385169],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003782231,0.00002145419,0.000066659,0.00001108672,0.000006800708,0.000003973152,0.9986701,0.0001054696,0.0007361747],"genre_scores_gemma":[0.001040578,0.00004933058,0.0004275413,0.000009098761,0.000002297539,0.00005235576,0.997361,0.00004798518,0.001009857],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08868564,"threshold_uncertainty_score":0.1763389,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948123510","doi":"10.5066/p9i5rl9c","title":"Mercury Concentrations and Isotopic Compositions in Sediment Cores from North American Lakes (Alaska, Minnesota, and Newfoundland)","year":2020,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Isotope; Sediment; Arctic; Watershed; Stable isotope ratio; Temperate climate; Sedimentary depositional environment","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01044515465012628,"gpt":0.2165304662478019,"spread":0.2060853115976756,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006447493,0.000847504,0.0005862857,0.002886417,0.001046776,0.001018738,0.001086356,0.0005935265,0.01048517],"category_scores_gemma":[0.001443799,0.0006518056,0.0003868075,0.006561865,0.00032926,0.0004679552,0.0008523656,0.0006994141,0.006542094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002796381,"about_ca_system_score_gemma":0.003274042,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4334614,"about_ca_topic_score_gemma":0.6434436,"domain_scores_codex":[0.9995399,0.00003373532,0.00004348853,0.0001454153,0.000133749,0.0001037977],"domain_scores_gemma":[0.9986298,0.0002207437,0.0002069882,0.0001966856,0.0006249804,0.0001207403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002390716,0.0001143796,0.07435069,0.001106637,0.0002030199,0.0001845798,0.0003616058,0.001467665,0.001471713,0.0007363764,0.9048211,0.01494328],"study_design_scores_gemma":[0.0002080177,0.00003387205,0.4332808,0.0003424416,0.0000961527,0.0001202828,0.0006088186,0.0007864953,0.001416445,0.0003959358,0.5626496,0.00006114531],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00473676,0.00004483113,0.00003305568,0.00002604124,0.000006350886,0.000006578683,0.9943179,0.00006311679,0.000765314],"genre_scores_gemma":[0.004424429,0.0000519161,0.0001818991,0.00001532215,0.000002876071,0.00004851251,0.9942444,0.00001536778,0.001015237],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5665386,"threshold_uncertainty_score":0.8618767,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948509981","doi":"10.5066/p9zvc4d5","title":"Denali Fault Zone Field, Microvein, and Microbeam Data, Kluane Ranges, Yukon, Canada","year":2023,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Research Data Management Practices","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Fault (geology); Geologic map; Fault scarp; Deformation (meteorology); Bedrock; Microbeam; Stereographic projection; Geologic hazards","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.04440548225385004,"gpt":0.2801242322845507,"spread":0.2357187500307007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004953638,0.001046254,0.0007139638,0.004966334,0.001561829,0.001572629,0.002122308,0.0006468463,0.02239395],"category_scores_gemma":[0.002821864,0.0005461247,0.000405941,0.01456091,0.0004198526,0.0006358941,0.0009759738,0.0007446944,0.01579141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008700451,"about_ca_system_score_gemma":0.02161877,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9432571,"about_ca_topic_score_gemma":0.9711525,"domain_scores_codex":[0.9994076,0.00002610018,0.00005845681,0.0001335391,0.0002053951,0.0001688762],"domain_scores_gemma":[0.9973916,0.000160915,0.0001537232,0.0002936751,0.001760693,0.0002393793],"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":[0.00006758614,0.00002570242,0.009525267,0.0003871537,0.00003491783,0.00006021988,0.0001378231,0.0006350157,0.0001725405,0.0007887462,0.983142,0.005022963],"study_design_scores_gemma":[0.0001532194,0.00001248949,0.07053706,0.0004094936,0.00004084895,0.00006513899,0.0008197522,0.001191028,0.0007083635,0.0006853295,0.9253159,0.00006131674],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005536398,0.00002357696,0.00003118083,0.0000223872,0.000003480924,0.000007200394,0.9986721,0.00008342975,0.0006029958],"genre_scores_gemma":[0.001318533,0.0000383631,0.0001981618,0.000009168748,9.999709e-7,0.00003086698,0.9974825,0.00002727027,0.0008942218],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05674291,"threshold_uncertainty_score":0.1141541,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6967273493","doi":"10.5066/p9c9jamy","title":"Water-quality and streamflow data for United States and Canadian sites in the Red River Basin and scripts for trend analysis - Data supporting water-quality trend analysis in the Red River of the North basin, 1970-2017","year":2020,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Trend analysis; Geological survey; Streamflow; Drainage basin; Water resources; Water quality; Climate change; Hydrology (agriculture); Environmental monitoring","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.09050651436646363,"gpt":0.3113413299980791,"spread":0.2208348156316155,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009323832,0.001188647,0.001032247,0.004685557,0.001521783,0.001842235,0.002117863,0.0006692184,0.02217826],"category_scores_gemma":[0.003966497,0.0007605097,0.001062729,0.01560915,0.0004478835,0.0010415,0.0012322,0.001440161,0.01554622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01234432,"about_ca_system_score_gemma":0.03607981,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9615272,"about_ca_topic_score_gemma":0.9748073,"domain_scores_codex":[0.9985715,0.00005359816,0.0001147885,0.0002709205,0.0007072781,0.0002818931],"domain_scores_gemma":[0.993322,0.0003255678,0.0003421294,0.0005055777,0.004972748,0.0005320447],"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":[0.00004092149,0.00002585749,0.007308707,0.0003453689,0.0000507059,0.0000234754,0.00008479266,0.0009593101,0.0001833913,0.0009269612,0.9834304,0.006620162],"study_design_scores_gemma":[0.0001316219,0.00001184161,0.09370026,0.0004786022,0.00005421095,0.00004603378,0.0004858423,0.002556445,0.001206541,0.001021061,0.9001982,0.0001094062],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003341277,0.00001740752,0.00007957101,0.00002490778,0.000008657889,0.00001455944,0.9984755,0.0002045055,0.000840762],"genre_scores_gemma":[0.001168352,0.00006271252,0.0006977208,0.00002169797,0.000003469448,0.00005165518,0.9970605,0.00006823369,0.0008657147],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03847277,"threshold_uncertainty_score":0.08956468,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6967728460","doi":"10.5066/p137vxon","title":"Great Lakes-Wide Dataset of Historical Coregonine Stocking Events","year":2025,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Stocking; Georeference; Data source; Quality (philosophy); Historical record; Geographic information system","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01939723200116174,"gpt":0.245422220914847,"spread":0.2260249889136853,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005886778,0.0005835854,0.0004515286,0.002564629,0.0004098672,0.0008657855,0.0009937249,0.0004998113,0.02661553],"category_scores_gemma":[0.002121025,0.0004016816,0.0003844159,0.005739853,0.0001882282,0.0004827687,0.001343913,0.000680981,0.01323102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008844682,"about_ca_system_score_gemma":0.002170581,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05681821,"about_ca_topic_score_gemma":0.08851352,"domain_scores_codex":[0.9996116,0.00006021656,0.00005573229,0.0001050639,0.0001117339,0.00005562391],"domain_scores_gemma":[0.9990608,0.0001813091,0.0001604376,0.0001558492,0.0003355883,0.0001059878],"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":[0.00008407632,0.00002905714,0.01026056,0.0007420286,0.00008330429,0.0001003691,0.0001934913,0.0009782275,0.000468188,0.001369025,0.977722,0.007969659],"study_design_scores_gemma":[0.0001155182,0.00001596505,0.06670096,0.000259181,0.00003979866,0.00007235834,0.0002368293,0.0006474427,0.0004203382,0.0006703214,0.9307882,0.00003310627],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001479863,0.00004844182,0.0001293511,0.00005235163,0.00001252432,0.00001250523,0.9971319,0.0000962667,0.001036788],"genre_scores_gemma":[0.002371505,0.00003967385,0.0005536812,0.00002790284,0.000005027286,0.0000852507,0.9959095,0.00002921753,0.0009783081],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9431818,"threshold_uncertainty_score":0.112975,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892142103","doi":"10.5066/f7vt1r1k","title":"Water-quality and streamflow datasets used for estimating loads considered for use in the 2002 Midcontinent nutrient SPARROW models, United States and Canada, 1970-2012","year":2018,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Sparrow; Watershed; Streamflow; Estimator; STREAMS; Hydrology (agriculture); Estimation","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.05199943039656791,"gpt":0.2676860841433645,"spread":0.2156866537467966,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007157916,0.001113083,0.0007779507,0.002952025,0.0007188288,0.000975041,0.002171315,0.0007587821,0.01596099],"category_scores_gemma":[0.002856048,0.0005241538,0.0005827525,0.009069082,0.0002655018,0.0006871973,0.0007769402,0.001221546,0.01148994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005346521,"about_ca_system_score_gemma":0.008580629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7372072,"about_ca_topic_score_gemma":0.7602259,"domain_scores_codex":[0.9994221,0.00003987815,0.00006667599,0.0001250127,0.0002546069,0.00009171123],"domain_scores_gemma":[0.9976767,0.0001935762,0.000221256,0.0002875437,0.001464931,0.0001561067],"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":[0.00005766064,0.00006601538,0.01005752,0.0003944744,0.00006509916,0.00004663894,0.00007379291,0.003879148,0.0001624312,0.001019322,0.978296,0.005881837],"study_design_scores_gemma":[0.0002947112,0.000017297,0.07775805,0.0004115153,0.00006179209,0.00006161244,0.0003727718,0.005782204,0.001076537,0.001502711,0.9125751,0.00008564512],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005278228,0.00001240038,0.00006969906,0.00001869328,0.000005775465,0.00001020821,0.9988751,0.0001096359,0.0003706971],"genre_scores_gemma":[0.001407948,0.00003214694,0.0003129161,0.00001002486,0.000001682013,0.00006072447,0.9977412,0.00002579941,0.0004075798],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2627928,"threshold_uncertainty_score":0.5286809,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6911081586","doi":"10.5066/p13tre2p","title":"Ring widths, scans of samples, and ancillary data from Sequoia sempervirens (coast redwood) trees and stumps at Fort Ross and Gualala, sampled for dendroseismology in 2021–2024","year":2025,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Sequoia; Dendrochronology; Table (database); Ring (chemistry); Tree (set theory); High resolution","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.04915495195706177,"gpt":0.2745924308824104,"spread":0.2254374789253487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002692039,0.0002778765,0.0001574489,0.001586856,0.0006778606,0.0003244928,0.0004602181,0.000232662,0.004912934],"category_scores_gemma":[0.0004478908,0.0002510257,0.0001581395,0.001308605,0.0002121591,0.0002271837,0.0003744266,0.0001870426,0.001424803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003906286,"about_ca_system_score_gemma":0.0002850787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03395231,"about_ca_topic_score_gemma":0.2988685,"domain_scores_codex":[0.9998429,0.00001079079,0.000009559221,0.00005359403,0.00005620314,0.00002688665],"domain_scores_gemma":[0.9994628,0.00004724176,0.0000836123,0.00008439692,0.0002360251,0.000085957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007948863,0.0002373145,0.744703,0.0003308429,0.0001098622,0.001437688,0.007160485,0.0008917666,0.109495,0.0003286968,0.01510209,0.1194083],"study_design_scores_gemma":[0.000003610565,0.00003104541,0.9890877,0.00001110331,0.00001294818,0.0001482053,0.0004777374,0.0001528913,0.003267131,0.00001580463,0.006785094,0.000006855624],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9697315,0.0001939312,0.001097465,0.00002622822,0.0000144101,0.00009319306,0.01747537,0.0002491765,0.01111875],"genre_scores_gemma":[0.9387569,0.0002201124,0.01487557,0.00005204464,0.00001977989,0.0002227357,0.03258936,0.0002347482,0.01302869],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.03395231,"threshold_uncertainty_score":0.06750935,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948636205","doi":"10.5066/p9hs0zkb","title":"County-Level Geographic Distributions for 47 Exotic Plant Species in Midwest USA and Central Canada, Compiled 2019","year":2021,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Distribution (mathematics); Geographic information system; Range (aeronautics); Plant species; Habitat; State (computer science)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03205637557431604,"gpt":0.2070330127673418,"spread":0.1749766371930258,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000399032,0.0008676032,0.0007801062,0.0044831,0.001628047,0.001661694,0.001457952,0.0005119194,0.01530985],"category_scores_gemma":[0.003064824,0.0004935363,0.0006268385,0.01479733,0.0003167765,0.0005567964,0.0009792332,0.0009256639,0.007771423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0103585,"about_ca_system_score_gemma":0.02302707,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9784982,"about_ca_topic_score_gemma":0.9881669,"domain_scores_codex":[0.999442,0.00002068765,0.00004623248,0.000144392,0.0001893977,0.0001572253],"domain_scores_gemma":[0.997442,0.0001591318,0.0001877659,0.0001594738,0.001827332,0.0002243388],"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":[0.00008322737,0.0000288,0.03947699,0.0007052062,0.0001381534,0.00009761598,0.0002606556,0.0009156754,0.0001853596,0.001137299,0.9488294,0.008141724],"study_design_scores_gemma":[0.0001292918,0.00001044919,0.2624022,0.0007414728,0.0001293612,0.0001236522,0.0008962957,0.0009921046,0.0004211104,0.0005927536,0.733504,0.00005722054],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001857956,0.0001209379,0.00004174688,0.00003811053,0.000007549726,0.000007595671,0.9967718,0.0000409463,0.001113407],"genre_scores_gemma":[0.006317137,0.0002588912,0.0003033316,0.00005086206,0.000004514755,0.00004724941,0.991107,0.00002687742,0.001884208],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02150184,"threshold_uncertainty_score":0.07515651,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6967682390","doi":"10.5066/p971jagf","title":"Geospatial Fabric for National Hydrologic Modeling, version 1.1","year":2020,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Geographic information system; Hydrography; Hydrological modelling; Spatial database; Spatial analysis; Hydrographic survey","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03059835273314112,"gpt":0.2428175614946874,"spread":0.2122192087615463,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001208304,0.001572821,0.0008680947,0.002825156,0.0006265527,0.002074459,0.001950789,0.0007937347,0.07340562],"category_scores_gemma":[0.005707899,0.0008893861,0.0007205486,0.008513014,0.0003050466,0.002122127,0.001735567,0.001806949,0.09098296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001509978,"about_ca_system_score_gemma":0.003020814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08629924,"about_ca_topic_score_gemma":0.08917595,"domain_scores_codex":[0.9993384,0.000114877,0.00008798194,0.0001551112,0.0001979188,0.0001056312],"domain_scores_gemma":[0.9981828,0.0003433618,0.0001690917,0.0004617002,0.0006838881,0.0001592401],"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":[0.00001629009,0.000007488238,0.0007568419,0.0001327203,0.000009507682,0.000008573484,0.00002445572,0.0003179034,0.00004265866,0.001102886,0.9955206,0.002060066],"study_design_scores_gemma":[0.00007468669,0.000003713279,0.003799574,0.0001384425,0.00001007181,0.00002085808,0.00008267094,0.0005691705,0.0001986091,0.002292079,0.9927898,0.00002028661],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009402967,0.00001763885,0.0001735327,0.00003876187,0.00001195215,0.000007579243,0.9979232,0.0005209207,0.001212366],"genre_scores_gemma":[0.0004546861,0.0000342237,0.0006847182,0.00002533,0.000004070819,0.00007201017,0.9977773,0.0003415468,0.0006060574],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08629924,"threshold_uncertainty_score":0.2455662,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6929951169","doi":"10.5066/p9xu3sqp","title":"Rainbow trout growth data and growth covariate data from Glen Canyon, Colorado River, Arizona, 2012-2021","year":2023,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"EcoMetrix","funders":"","keywords":"Rainbow trout; Tailwater; Fish measurement; Electrofishing; Biomass (ecology); Trout; Sampling (signal processing); Fishing","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.3211670617760715,"gpt":0.3677573308207615,"spread":0.04659026904468994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005256537,0.0004585054,0.0002571278,0.0007652434,0.0006310894,0.0003150264,0.0004785561,0.0002365863,0.00545711],"category_scores_gemma":[0.0008634605,0.0002565594,0.0002442882,0.001655206,0.0001850836,0.0002284849,0.0003399259,0.0003496552,0.001500013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001701616,"about_ca_system_score_gemma":0.001593393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4267005,"about_ca_topic_score_gemma":0.7173759,"domain_scores_codex":[0.9996971,0.00003283716,0.00001828758,0.00012782,0.0000907401,0.00003337871],"domain_scores_gemma":[0.9990387,0.00007655454,0.0001811483,0.0000809699,0.0005213489,0.0001012143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001782514,0.0001650354,0.925727,0.00009995795,0.0002004869,0.00009615273,0.0002365746,0.008004558,0.001804033,0.0002169484,0.05053278,0.01273818],"study_design_scores_gemma":[0.00002033997,0.00004874058,0.9833081,0.00001641662,0.00004229703,0.00003023271,0.0002048811,0.00228312,0.0002465126,0.00004859739,0.01373945,0.00001144477],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.7841228,0.0002687174,0.002910446,0.0001942581,0.0000361092,0.0001601231,0.2026034,0.0004520839,0.009252127],"genre_scores_gemma":[0.6938162,0.0003531593,0.005501987,0.000154629,0.00002767397,0.0007362338,0.281755,0.0001154497,0.0175397],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.4267005,"threshold_uncertainty_score":0.8484337,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6948288540","doi":"10.5066/p14xqywk","title":"16S rRNA whole-organism microbiome sequencing for larval insects, adult insects, and riparian spiders collected from Torch Lake and Gratiot Lake, Keweenaw Peninsula, Michigan, USA, July and October 2021","year":2025,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Peanut Plant Research Studies","field":"Agricultural and Biological 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":"McMaster University","funders":"","keywords":"Larva; Torch; Riparian zone; Microbiome; 16S ribosomal RNA; Genetic data","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01352325911120592,"gpt":0.2058027235150004,"spread":0.1922794644037945,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007115785,0.0007099102,0.0005041463,0.001796913,0.0006147267,0.0007889332,0.001001293,0.0007007956,0.02496429],"category_scores_gemma":[0.002086443,0.0003535603,0.0004111144,0.003131239,0.000186883,0.0004579653,0.001094118,0.0006411921,0.01454098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009278738,"about_ca_system_score_gemma":0.002072979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04638952,"about_ca_topic_score_gemma":0.1055813,"domain_scores_codex":[0.9995865,0.00005420236,0.00006269573,0.0001296456,0.00009159485,0.00007537304],"domain_scores_gemma":[0.9990733,0.0001842778,0.0001598232,0.0001323198,0.0003309115,0.0001194569],"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":[0.0002328032,0.00005009573,0.01661144,0.001730642,0.0001119265,0.0001243385,0.0001484755,0.0003931503,0.0013621,0.0006028988,0.9692152,0.009416833],"study_design_scores_gemma":[0.0003351944,0.00004854643,0.1048406,0.0007031764,0.0000901828,0.0001154161,0.0004170945,0.0004517574,0.001160933,0.0006325254,0.8911617,0.00004277602],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008956737,0.00003301731,0.00004005143,0.00002809635,0.000006017666,0.00001029202,0.9984648,0.00004267219,0.0004794856],"genre_scores_gemma":[0.001357125,0.00003315599,0.0002897859,0.00003173497,0.000002678937,0.0000831712,0.9976387,0.00001298946,0.0005506085],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04638952,"threshold_uncertainty_score":0.09223902,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6892255742","doi":"10.5066/p90eq1h7","title":"Historical shorelines and morphological metrics for barrier islands and spits along the north coast of Alaska between Cape Beaufort and the U.S.-Canadian border, 1947 to 2019","year":2021,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Cape; Shore; Beaufort scale; Barrier island; Beaufort sea; Suite","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01858032972872313,"gpt":0.2379163304989658,"spread":0.2193360007702427,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003630015,0.0006179214,0.000464081,0.004604232,0.0007423257,0.0008208464,0.0009128763,0.0003464922,0.01070336],"category_scores_gemma":[0.002087125,0.00038216,0.0004426372,0.009672874,0.0002421259,0.0005364477,0.0007730515,0.0005618385,0.005880943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00276415,"about_ca_system_score_gemma":0.003990629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.766897,"about_ca_topic_score_gemma":0.8819278,"domain_scores_codex":[0.9996232,0.00002073339,0.00004445846,0.00009801576,0.000141954,0.00007165148],"domain_scores_gemma":[0.9979479,0.0001134636,0.0002658677,0.0001797281,0.00132958,0.000163505],"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":[0.0001329386,0.00005643821,0.1960351,0.001146672,0.0001953385,0.0001853128,0.0008123341,0.002508622,0.0004634189,0.001507342,0.7796939,0.01726251],"study_design_scores_gemma":[0.00003671051,0.0000129836,0.6408116,0.0004467606,0.0000487645,0.00009498365,0.001251956,0.0006483104,0.0003408731,0.0002586705,0.3560016,0.00004681137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007134825,0.0001007071,0.00006217964,0.00002233424,0.00001031847,0.00001053482,0.9909603,0.00005664593,0.001642218],"genre_scores_gemma":[0.01019941,0.0001300804,0.0003556083,0.000009848142,0.000003723975,0.00005278079,0.9876564,0.00001960903,0.001572679],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.233103,"threshold_uncertainty_score":0.4689516,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}