{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":530,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":530,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"8ec81fbc78cc","filters":{"topic":"Precipitation Measurement and Analysis"}},"results":[{"id":"W2413554747","doi":"10.1175/bams-d-14-00283.1","title":"So, How Much of the Earth’s Surface Is Covered by Rain Gauges?","year":2016,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":731,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Hydrometeorology; Rain gauge; Precipitation; Environmental science; Snow; Meteorology; Gravimeter; Climatology; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01487556436915609,"gpt":0.204012039636775,"spread":0.1891364752676189,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008215848,0.0001351807,0.0003282116,0.000006143685,0.000182887,0.00001999674,0.0006775113,0.00005455601,0.004966336],"category_scores_gemma":[0.0003510191,0.00005403548,0.0005746076,0.0003158975,0.001274698,0.00002119396,0.0000593507,0.000112688,0.00004559061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004732779,"about_ca_system_score_gemma":0.00002808782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007437784,"about_ca_topic_score_gemma":0.00002744454,"domain_scores_codex":[0.9982358,0.0005028298,0.0002283462,0.0002363665,0.0005452179,0.0002513929],"domain_scores_gemma":[0.9984648,0.0005507317,0.0004997728,0.0003434296,0.00007793954,0.00006329303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009211584,0.00006254802,0.5687724,0.00001195621,0.0002647263,1.512462e-7,0.0002482944,0.0001053524,0.06358406,0.00003919073,0.3504787,0.01634048],"study_design_scores_gemma":[0.0007557544,0.0003618732,0.7560238,0.0000321407,0.0001519415,0.000001163273,0.0008091696,0.0003095651,0.03525496,0.0009979316,0.2049982,0.000303507],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9447433,0.0002661141,0.0001622126,0.0537256,0.00006267826,0.0001244148,0.000211656,0.00001397072,0.0006900055],"genre_scores_gemma":[0.9920993,0.0002005007,0.001366454,0.003780182,0.00002310226,7.723708e-7,0.000003598261,0.00000273904,0.002523294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1872514,"threshold_uncertainty_score":0.9959432,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1522265489","doi":"10.1175/bams-d-14-00174.1","title":"Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities","year":2015,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":657,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Quantitative precipitation estimation; Radar; Suite; Precipitation; Meteorology; Environmental science; Satellite; Numerical weather prediction; Computer science; Weather radar; Remote sensing; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.07750306661538607,"gpt":0.2985260489662824,"spread":0.2210229823508963,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001295798,0.0001737135,0.0003595663,0.00002355719,0.0002484187,0.00004897177,0.0003455299,0.00005583392,0.0007048],"category_scores_gemma":[0.002031116,0.0001085208,0.0003015438,0.0003302436,0.0008384891,0.00006927557,0.00003739561,0.0001869377,0.0001110515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001740306,"about_ca_system_score_gemma":0.00005691971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001930311,"about_ca_topic_score_gemma":0.0001284074,"domain_scores_codex":[0.9978,0.0007192139,0.0004125429,0.0002949702,0.0005222274,0.0002510366],"domain_scores_gemma":[0.9983736,0.0006223585,0.0004222363,0.0002041634,0.0002576672,0.0001199637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000561406,0.0007472107,0.6501645,0.0001079585,0.0009173446,0.000004897447,0.0224322,0.2528785,0.004874686,0.0003017532,0.01612703,0.05088258],"study_design_scores_gemma":[0.001575934,0.0009773284,0.6986088,0.0000307444,0.0001737236,0.00000375377,0.0236832,0.2712569,0.000731672,0.0004103702,0.002086502,0.0004611377],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838764,0.0002229631,0.01151273,0.003309466,0.0001638147,0.0002872713,0.00006736216,0.00006023596,0.0004997449],"genre_scores_gemma":[0.6566448,0.00001327503,0.342389,0.0007537723,0.00003437209,0.000004657355,0.00002658126,0.000003144224,0.0001303258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3308763,"threshold_uncertainty_score":0.7717066,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3005813145","doi":"10.1016/j.rse.2020.111697","title":"Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":629,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Global Water Futures; National Natural Science Foundation of China","keywords":"Global Precipitation Measurement; Environmental science; Satellite; Precipitation; Snow; Scale (ratio); Meteorology; Climatology; Geography; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.03225247623199527,"gpt":0.2495503031227362,"spread":0.217297826890741,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000200974,0.0002014349,0.0004612121,0.0001049064,0.00007057725,0.00002548742,0.00008679587,0.00004049927,0.0000762535],"category_scores_gemma":[0.00004638924,0.0001673353,0.00005975158,0.0002358634,0.000176568,0.0001531965,0.00002224395,0.0001079968,0.00001083542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009127712,"about_ca_system_score_gemma":0.00001728644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006294378,"about_ca_topic_score_gemma":0.000631408,"domain_scores_codex":[0.9982401,0.0001528523,0.0004880983,0.0004351148,0.0004920175,0.0001918521],"domain_scores_gemma":[0.9989605,0.0001108952,0.0004671524,0.0002728784,0.00005435302,0.0001342046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006086637,0.00006296405,0.1904604,0.0002446944,0.0007811335,0.000008991055,0.003724235,0.03653983,0.2083817,0.000001917862,0.00007712553,0.5591084],"study_design_scores_gemma":[0.001298117,0.0005792502,0.5716722,0.0001404317,0.0008375592,0.000004125591,0.001080014,0.370477,0.05044549,0.00002780064,0.003020118,0.0004178896],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933749,0.002447266,0.003128127,0.0004488369,0.0000218459,0.0002760331,0.0001106084,0.00001468294,0.0001777305],"genre_scores_gemma":[0.9694259,0.0006331633,0.02894701,0.00007738395,0.00003938891,3.047441e-8,0.0008521435,0.000007153995,0.00001782728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5586905,"threshold_uncertainty_score":0.6823733,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3023073569","doi":"10.1029/2019ms001689","title":"Confronting the Challenge of Modeling Cloud and Precipitation Microphysics","year":2020,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":612,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"Horizon 2020; European Research Council; National Aeronautics and Space Administration; U.S. Department of Energy; National Science Foundation","keywords":"Cloud computing; Cloud physics; Meteorology; Precipitation; Climate model; Computer science; Microscale chemistry; Environmental science; Population; Climate change; Geography; Mathematics; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.03819095310188898,"gpt":0.2420342268497959,"spread":0.2038432737479069,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008387775,0.0000843904,0.0002704758,0.00006852751,0.00006096872,0.00004058886,0.0001454701,0.00002880808,0.000006880657],"category_scores_gemma":[0.0001057484,0.00005692442,0.00006251864,0.0001550394,0.00002387506,0.0005064656,0.000005953925,0.0001688823,0.000001366199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002250731,"about_ca_system_score_gemma":0.00002868916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007329346,"about_ca_topic_score_gemma":0.0001002269,"domain_scores_codex":[0.9986659,0.0001287596,0.0006405617,0.0001074258,0.0003439295,0.0001134641],"domain_scores_gemma":[0.9991729,0.00009471318,0.0004174989,0.0000615421,0.0001994408,0.00005392355],"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":[0.00003616642,0.000004371685,0.008217047,0.00007401961,0.0000174892,0.000001094951,0.001787156,0.9831946,0.0001588831,0.00004734884,0.000001488426,0.006460312],"study_design_scores_gemma":[0.0002465407,0.00009240232,0.0001278769,0.0001775758,0.00002385575,0.000002353447,0.002170824,0.9964262,0.0000156205,0.0005667927,0.00008725866,0.00006269797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8020376,0.04584176,0.1508597,0.0004471286,0.0003957441,0.0001147015,0.000005814675,0.000005311661,0.0002922859],"genre_scores_gemma":[0.995569,0.002519091,0.00157036,0.00002394813,0.0003094308,2.3452e-7,0.00000206706,0.000002741551,0.000003145581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1935314,"threshold_uncertainty_score":0.2321311,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2019775760","doi":"10.1175/jas3535.1","title":"A Multimoment Bulk Microphysics Parameterization. Part II: A Proposed Three-Moment Closure and Scheme Description","year":2005,"lang":"en","type":"article","venue":"Journal of the Atmospheric Sciences","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":582,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Moment (physics); Moment closure; Statistical physics; Context (archaeology); Closure (psychology); Physics; Meteorology; Classical mechanics; Geology; Turbulence","retraction":null,"screen_n_in":null,"score":{"opus":0.02766970600309337,"gpt":0.219463132711734,"spread":0.1917934267086406,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008350933,0.0001040939,0.0001566509,0.00001182093,0.0005310625,0.0001606664,0.0003395654,0.00002632696,0.0002777846],"category_scores_gemma":[0.00005610985,0.00005762461,0.00009823898,0.0005591001,0.0002196424,0.0005578817,0.00002274052,0.0000941417,0.000009014825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000015026,"about_ca_system_score_gemma":0.00008990723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005680016,"about_ca_topic_score_gemma":0.0001157128,"domain_scores_codex":[0.9986012,0.00007831457,0.0003431788,0.0001433801,0.0006566588,0.0001772663],"domain_scores_gemma":[0.9992508,0.00003534626,0.0004183229,0.00009786691,0.0001154817,0.00008212667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006032459,0.0001288631,0.8354144,0.00001664562,0.0001259775,0.000001914716,0.001182887,0.08697815,0.01058237,0.00006623695,0.001091938,0.06435025],"study_design_scores_gemma":[0.001137302,0.0009952669,0.4589483,0.0001845338,0.0001915097,0.00004458532,0.0007373291,0.5140764,0.002309072,0.00190722,0.01907526,0.0003932383],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994885,0.0009597499,0.0004882942,0.003069823,0.0003397224,0.0001079834,0.00000216887,0.00000626174,0.0001409629],"genre_scores_gemma":[0.9661495,0.0001426614,0.03300773,0.0002969685,0.0002003557,4.250934e-7,8.67898e-7,0.000001647706,0.0001998549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4270982,"threshold_uncertainty_score":0.4084557,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2477555870","doi":"10.5334/jors.119","title":"The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language","year":2016,"lang":"en","type":"article","venue":"Journal of Open Research Software","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":499,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Pacific Northwest National Laboratory; Argonne National Laboratory; Biological and Environmental Research; Rheinische Friedrich-Wilhelms-Universität Bonn; Marshall Space Flight Center; Office of Science; Universität Stuttgart; U.S. Department of Energy; McGill University; University of Wyoming","keywords":"Python (programming language); Computer science; MIT License; Interfacing; Radar; Weather radar; Programming language; License; Operating system; Software; Computer hardware; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.1582354633699748,"gpt":0.3594173750290672,"spread":0.2011819116590924,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00869057,0.0001023712,0.0001880915,0.000179857,0.0004607262,0.001025739,0.002720456,0.00003711383,0.0002813551],"category_scores_gemma":[0.000985276,0.00003919573,0.00006291741,0.0006334889,0.0001182696,0.001469759,0.0001290902,0.0003274927,0.00001424111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001047271,"about_ca_system_score_gemma":0.0002987373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001332927,"about_ca_topic_score_gemma":0.002085885,"domain_scores_codex":[0.9973882,0.0006590343,0.0003360099,0.0002048026,0.001007909,0.0004040278],"domain_scores_gemma":[0.9963992,0.002698976,0.000193381,0.0004866521,0.0001348337,0.00008700565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006183676,0.0000440061,0.1587718,0.00001842399,0.00008150348,0.00005345466,0.0006252556,0.000007213029,0.0000251651,0.00002625946,0.01585419,0.8238744],"study_design_scores_gemma":[0.002626013,0.0009582679,0.05585073,0.001306901,0.00007463721,0.00004780762,0.006917608,0.0002427526,0.0001723089,0.002527031,0.9290001,0.0002758674],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5801451,0.05181418,0.09104346,0.2344764,0.001487341,0.01526608,0.0009848563,0.0002462355,0.02453631],"genre_scores_gemma":[0.9062362,0.001290922,0.08384761,0.0005661391,0.0008521965,0.00002961966,0.0001950678,0.00002454792,0.006957736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9131459,"threshold_uncertainty_score":0.9891221,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2096701604","doi":"10.1002/qj.375","title":"REAL—Ensemble radar precipitation estimation for hydrology in a mountainous region","year":2009,"lang":"en","type":"article","venue":"Quarterly Journal of the Royal Meteorological Society","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":233,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Radar; Precipitation; Meteorology; Quantitative precipitation forecast; Weather radar; Environmental science; Ensemble forecasting; Flood forecasting; Surface runoff; Remote sensing; Computer science; Flood myth; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02179393761533857,"gpt":0.2443583799540861,"spread":0.2225644423387475,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001264808,0.0001100822,0.0002559613,0.00004521672,0.0001488922,0.00004034562,0.0002617745,0.0001255417,0.00004294072],"category_scores_gemma":[0.0001280499,0.00006532767,0.0003778139,0.0002028932,0.00004850235,0.000175444,0.000001958957,0.0002038489,0.000003785719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002786944,"about_ca_system_score_gemma":0.00003613598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007197654,"about_ca_topic_score_gemma":0.00009589065,"domain_scores_codex":[0.9985783,0.000290639,0.0004702961,0.0001388025,0.000295951,0.0002259787],"domain_scores_gemma":[0.999029,0.000278957,0.0004159078,0.0001006294,0.000111662,0.00006389123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001242888,0.0002925026,0.1286957,0.0000300154,0.0002108281,0.00001330066,0.00509961,0.1833155,0.00167137,0.0006928123,0.003212486,0.675523],"study_design_scores_gemma":[0.001279484,0.003866693,0.673462,0.00002458616,0.0001238617,0.00001666465,0.0004872719,0.2511783,0.00007219466,0.06920918,0.00012603,0.000153774],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771693,0.0001602011,0.01862854,0.003345101,0.0001861313,0.0001990295,0.000002843547,0.00001133376,0.0002975407],"genre_scores_gemma":[0.9892314,0.00002090062,0.01015664,0.0004294028,0.0001098642,0.000001083248,0.000007686324,0.00000159615,0.00004140869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6753693,"threshold_uncertainty_score":0.2663985,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2601151695","doi":"10.1175/bams-d-16-0182.1","title":"The Olympic Mountains Experiment (OLYMPEX)","year":2017,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":205,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Snow; Precipitation; Environmental science; Global Precipitation Measurement; Satellite; Meteorology; Climatology; Dropsonde; Peninsula; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02390508888947828,"gpt":0.2509326899756226,"spread":0.2270276010861443,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008764986,0.0001158897,0.0002161555,0.000005058551,0.001538663,0.0001155617,0.001205978,0.00003115748,0.001464358],"category_scores_gemma":[0.0003401832,0.00005220241,0.0004257644,0.00007334927,0.001449132,0.00002116946,0.00008635295,0.0001185268,0.0001181227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007517132,"about_ca_system_score_gemma":0.00001898691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001770556,"about_ca_topic_score_gemma":0.00008179586,"domain_scores_codex":[0.9986899,0.0002172344,0.0002120619,0.0001985985,0.0004180801,0.0002641547],"domain_scores_gemma":[0.9984652,0.0003093785,0.0005117576,0.0006013404,0.00004643565,0.0000658297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001435446,0.00007132633,0.8472824,0.000006503708,0.0003865142,0.000001203937,0.0003772912,0.000527019,0.002117577,0.0005278442,0.04195454,0.1066043],"study_design_scores_gemma":[0.0001778191,0.0001258644,0.9329941,0.00000395938,0.000045362,7.217058e-7,0.0007500281,0.0005525719,0.0008337818,0.0005484549,0.06385691,0.0001103979],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716472,0.000542298,0.00005951078,0.02050338,0.0001566632,0.0001257084,0.00001163823,0.00002147003,0.00693213],"genre_scores_gemma":[0.9961882,0.0002809498,0.001029211,0.00162814,0.00007721535,0.000003606276,0.000002365367,0.000002076256,0.0007882984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1064939,"threshold_uncertainty_score":0.9997612,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2022349500","doi":"10.1016/j.rse.2015.02.024","title":"A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":195,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Geospatial-Intelligence Agency; Chinese Academy of Sciences; National Natural Science Foundation of China; National Aeronautics and Space Administration","keywords":"Downscaling; Precipitation; Normalized Difference Vegetation Index; Environmental science; Quantitative precipitation estimation; Satellite; Remote sensing; Algorithm; Climatology; Rain gauge; Vegetation (pathology); Meteorology; Computer science; Climate change; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.0328635121938099,"gpt":0.2187170694369136,"spread":0.1858535572431037,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005085143,0.0001526458,0.000214845,0.0001020312,0.00009553793,0.00004425148,0.00005048507,0.00006656385,0.00002015634],"category_scores_gemma":[0.0000776916,0.0001365289,0.00003415369,0.0001492723,0.00005956262,0.0002030219,0.000005458879,0.00009562765,0.00002469808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002540559,"about_ca_system_score_gemma":0.00007741074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007310054,"about_ca_topic_score_gemma":0.0001210986,"domain_scores_codex":[0.9985906,0.0001317097,0.0003400127,0.0002565039,0.0005203278,0.0001608534],"domain_scores_gemma":[0.9989667,0.0003755265,0.000266121,0.0001474374,0.00005720224,0.0001869946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005512653,0.000006378804,0.6814624,0.00001928866,0.00003258371,0.000002141089,0.0007499792,0.05249573,0.00006759397,7.653508e-7,0.00001451359,0.2650935],"study_design_scores_gemma":[0.0005438494,0.000145338,0.7743919,0.0000671044,0.0001016642,8.668279e-7,0.0001274472,0.2238121,0.0002021043,0.0003557955,0.0001067078,0.0001451683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7778116,0.000210214,0.2211488,0.0002461646,0.00004428642,0.0001981219,0.00004471908,0.00001902671,0.0002770693],"genre_scores_gemma":[0.7453967,0.00001872742,0.2537817,0.00001654047,0.00005665387,2.590928e-8,0.0006537793,0.000005911588,0.00007002935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2649483,"threshold_uncertainty_score":0.5567488,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2095274434","doi":"10.5194/amt-5-2661-2012","title":"Improved Micro Rain Radar snow measurements using Doppler spectra post-processing","year":2012,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":193,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Deutsches Zentrum für Luft- und Raumfahrt; Deutsche Forschungsgemeinschaft","keywords":"Radar; Remote sensing; Doppler radar; Snow; Continuous-wave radar; Environmental science; Doppler effect; Drizzle; Precipitation; Spurious relationship; Pulse-Doppler radar; Weather radar; Computer science; Meteorology; Radar imaging; Geology; Physics; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.05400461618566246,"gpt":0.2528970271273483,"spread":0.1988924109416858,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00392537,0.0004815867,0.0004630945,0.00004555838,0.0004915626,0.0002013744,0.0004754884,0.0001610233,0.002119778],"category_scores_gemma":[0.0002137416,0.0004220467,0.0002378354,0.0007405665,0.00009866678,0.00109657,0.00002768475,0.0002605453,0.00008098644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001666781,"about_ca_system_score_gemma":0.0002087818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001594213,"about_ca_topic_score_gemma":0.0005336408,"domain_scores_codex":[0.9956823,0.0002975796,0.0006894585,0.0005221211,0.001703102,0.001105445],"domain_scores_gemma":[0.9981052,0.00003652326,0.000390331,0.0004346362,0.000639889,0.0003934401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000100404,0.0002061289,0.3709795,0.00009247023,0.0002710973,0.000003143925,0.0005234759,0.00004907234,0.443607,0.000008048325,0.001826099,0.1823335],"study_design_scores_gemma":[0.002276019,0.0007003304,0.3263835,0.0008738425,0.001337963,0.00008713414,0.002149526,0.01746966,0.628957,0.0004953785,0.01527319,0.003996426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6705511,0.02193647,0.2694384,0.001330592,0.002062652,0.003640902,0.0000725615,0.003104476,0.02786286],"genre_scores_gemma":[0.814236,0.00005001491,0.1844668,0.0006802772,0.0003943427,0.00001075842,0.00005549113,0.00002473438,0.00008157397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.18535,"threshold_uncertainty_score":0.9998232,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2077861728","doi":"10.1016/j.advwatres.2011.05.007","title":"Evaluation of precipitation products over complex mountainous terrain: A water resources perspective","year":2011,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":165,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Natural Environment Research Council; Universidad de Concepción","keywords":"Precipitation; Environmental science; Terrain; Quantitative precipitation estimation; Hindcast; Water resources; Climatology; Drainage basin; Structural basin; Water balance; Quantitative precipitation forecast; Satellite; Meteorology; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.05367018989973621,"gpt":0.2755091984003926,"spread":0.2218390085006564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002151985,0.0001818991,0.000246932,0.000330014,0.0001307408,0.0000379279,0.000267498,0.00005729515,0.002537854],"category_scores_gemma":[0.0001350201,0.0001113564,0.00006492557,0.0002386074,0.0001691112,0.0008141272,0.00002053894,0.0001093178,0.00007102048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003328418,"about_ca_system_score_gemma":0.00001240833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002350575,"about_ca_topic_score_gemma":0.005157482,"domain_scores_codex":[0.9972552,0.0005285185,0.0004109189,0.0004153813,0.001043211,0.0003467721],"domain_scores_gemma":[0.999074,0.00004368434,0.0001252898,0.0002324021,0.0004738992,0.00005072163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005757586,0.0002124836,0.4809368,0.000114626,0.0001864051,0.000006506155,0.4394377,0.02091301,0.01115806,0.00005269358,0.0000297805,0.04637622],"study_design_scores_gemma":[0.001680405,0.0005253039,0.8802139,0.0001083076,0.0003122553,0.000004594297,0.01862798,0.01259653,0.05107289,0.02452665,0.009710505,0.0006206939],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787644,0.001826814,0.00002646484,0.000126454,0.0001034614,0.0003502468,0.000009772712,0.00003001809,0.01876242],"genre_scores_gemma":[0.9990405,0.00005530433,0.0005603133,0.00004928917,0.00008732536,0.00001130922,0.00009342378,0.000006357453,0.00009610637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4208097,"threshold_uncertainty_score":0.998374,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W33954318","doi":"10.1007/978-1-4020-5835-6_48","title":"International Global Precipitation Measurement (GPM) Program and Mission: An Overview","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":163,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Global Precipitation Measurement; Precipitation; Environmental science; Meteorology; Aeronautics; Climatology; Remote sensing; Geology; Geography; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1648682612228083,"gpt":0.3250689091053535,"spread":0.1602006478825453,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008638975,0.0002746119,0.0002458995,0.0001339274,0.0001129461,0.0001796085,0.0002271521,0.0001905313,0.01429626],"category_scores_gemma":[0.00003387084,0.000226346,0.0001138231,0.00005675419,0.00005840572,0.0003225674,0.00001410266,0.0001349215,0.0002154391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003622924,"about_ca_system_score_gemma":0.00006743024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001710514,"about_ca_topic_score_gemma":0.003513607,"domain_scores_codex":[0.9975579,0.00003300154,0.0003934391,0.0004473863,0.001371639,0.0001966202],"domain_scores_gemma":[0.9989987,0.00002244775,0.0001956673,0.0001818354,0.0003558179,0.0002455561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003943706,0.00002710876,0.01008312,0.00003588948,0.0001847682,0.000006164106,0.00004336067,0.00004084658,0.000002643295,0.006054638,0.001551789,0.9819303],"study_design_scores_gemma":[0.0004970204,0.0003589756,0.07230248,0.0002669508,0.0002642901,0.00001120554,0.00006095381,0.004266019,0.000005590376,0.01488385,0.9064077,0.0006750255],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001497258,0.004937741,0.0002495896,0.0003192924,0.0004806191,0.0003881539,0.00009435702,0.0001291763,0.9932513],"genre_scores_gemma":[0.1203517,0.0274373,0.0773802,0.004196372,0.005361662,0.00001898921,0.01649055,0.0001054109,0.7486578],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9812552,"threshold_uncertainty_score":0.9866048,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2132366275","doi":"10.1175/jam2183.1","title":"Variability of Drop Size Distributions: Time-Scale Dependence of the Variability and Its Effects on Rain Estimation","year":2005,"lang":"en","type":"article","venue":"Journal of Applied Meteorology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":158,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"McGill University","funders":"","keywords":"Disdrometer; Environmental science; Storm; Climatology; Radar; Scale (ratio); Meteorology; Atmospheric sciences; Statistics; Mathematics; Rain gauge; Geology; Precipitation; Physics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.007275312588322849,"gpt":0.2146831103271094,"spread":0.2074077977387865,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003239691,0.00007991148,0.000289741,0.00004838012,0.00006285059,0.000006213562,0.0001734804,0.00007340337,0.0003205798],"category_scores_gemma":[0.001120179,0.00005142964,0.00008388785,0.0001971572,0.00009352135,0.00009461767,0.00001100678,0.0001526491,0.000006361435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001034618,"about_ca_system_score_gemma":0.00005786125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000627518,"about_ca_topic_score_gemma":0.00001909443,"domain_scores_codex":[0.9986558,0.0003549907,0.0004619775,0.0001180804,0.0003006529,0.0001084812],"domain_scores_gemma":[0.9973357,0.001825311,0.0005087937,0.0001346236,0.0001403471,0.00005516472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002417068,0.000649647,0.4199506,0.0004027539,0.0006092412,0.000002253204,0.001112876,0.1015402,0.2113135,0.005134874,0.0002541786,0.2566129],"study_design_scores_gemma":[0.001028082,0.000409134,0.8737447,0.0000337226,0.0003293658,0.00001300525,0.00001902021,0.04928098,0.05162381,0.02330778,0.00009032799,0.0001200945],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949214,0.00005008527,0.0033257,0.0004301427,0.00006793028,0.0001415944,0.00002367104,0.000003061429,0.0010364],"genre_scores_gemma":[0.9951872,0.0000118323,0.004687178,0.00005674362,0.00004350266,7.038741e-7,0.000003854257,0.000001094049,0.000007948824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4537941,"threshold_uncertainty_score":0.3510124,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2734606400","doi":"10.5194/hess-21-3525-2017","title":"Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE","year":2017,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":154,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada; Ministry of Land, Infrastructure and Transport","keywords":"Precipitation; Gauge (firearms); Shielded cable; Environmental science; Spice; Meteorology; Materials science; Physics; Computer science; Electrical engineering; Telecommunications; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06548468526031885,"gpt":0.2635729529968045,"spread":0.1980882677364856,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001290297,0.0001114381,0.0004182936,0.0002831196,0.0004845129,0.00009619632,0.0001974246,0.00008070382,0.00007093347],"category_scores_gemma":[0.0001393381,0.00008813564,0.00005174888,0.0002378823,0.0006444183,0.0003628149,0.00002230773,0.00004560244,0.000001830599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001050227,"about_ca_system_score_gemma":0.00001998635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00217254,"about_ca_topic_score_gemma":0.008263446,"domain_scores_codex":[0.9986036,0.0002157792,0.0003549359,0.0003346005,0.000322534,0.0001685197],"domain_scores_gemma":[0.9989418,0.000234166,0.0004783458,0.0001856794,0.00007598627,0.00008404049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003133113,0.000009345939,0.9883139,0.00003358175,0.0002740364,5.910535e-7,0.0005506842,0.0005797747,0.003425248,0.00003255551,0.000002031816,0.006746928],"study_design_scores_gemma":[0.0002917681,0.0002107878,0.9616431,0.00004540178,0.0005145235,0.000001257507,0.0005172565,0.03419253,0.002346874,0.0001376851,0.000003734846,0.00009505935],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967538,0.0008790629,0.0001607739,0.0001223619,0.0001353307,0.00009040185,0.00003314661,0.000009021503,0.001816096],"genre_scores_gemma":[0.9993756,0.00004674021,0.0005017626,0.00001733781,0.00002045305,8.438381e-7,0.00001862149,0.00000103562,0.00001763058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03361275,"threshold_uncertainty_score":0.4611197,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2460809943","doi":"10.1175/amsmonographs-d-15-0037.1","title":"Development and Applications of ARM Millimeter-Wavelength Cloud Radars","year":2016,"lang":"en","type":"article","venue":"Meteorological Monographs","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":151,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Meteorology; Environmental science; Millimeter; Atmospheric physics; Radar; Telecommunications; Atmosphere (unit); Geography; Engineering; Physics; Astronomy","retraction":null,"screen_n_in":null,"score":{"opus":0.03033993969283791,"gpt":0.2208776597731981,"spread":0.1905377200803602,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003925903,0.00009846007,0.0001737793,0.0001311288,0.00009335683,0.00001340822,0.0001335968,0.00005435804,0.0007331552],"category_scores_gemma":[0.00003621731,0.00005416241,0.00006759144,0.0002887369,0.0001395515,0.00007470569,0.00001087492,0.00004015658,0.00003642115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001540096,"about_ca_system_score_gemma":0.00001029984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001291483,"about_ca_topic_score_gemma":0.00008878801,"domain_scores_codex":[0.9991019,0.00006038529,0.0002554075,0.00021463,0.0001954278,0.0001722962],"domain_scores_gemma":[0.9994642,0.0001868523,0.00009149484,0.0001160764,0.00003939638,0.0001020341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002284113,0.00003040317,0.2362999,0.000005325929,0.00006042435,5.954028e-7,0.00008479091,0.000002158145,0.000770164,0.0001689322,0.000063012,0.7624915],"study_design_scores_gemma":[0.0005950714,0.0002687148,0.9356287,0.0000160661,0.00007810332,0.000002437409,0.0002048502,0.0001105946,0.00661782,0.006327039,0.04986295,0.0002876825],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989825,0.0005776134,0.007471551,0.0002157504,0.00003963412,0.0001463333,0.00002871194,0.00003368895,0.001661746],"genre_scores_gemma":[0.983156,0.0002055307,0.01643832,0.0001090575,0.00002690339,0.000006923472,0.00001817147,0.000001381722,0.00003767322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7622038,"threshold_uncertainty_score":0.8027536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2580791415","doi":"10.1175/jhm-d-16-0187.1","title":"Evaluation of Integrated Multisatellite Retrievals for GPM (IMERG) over Southern Canada against Ground Precipitation Observations: A Preliminary Assessment","year":2017,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":141,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Centrum fÖr Personcentrerad Vård","keywords":"Hydrometeorology; Environmental science; Global Precipitation Measurement; Precipitation; Climatology; Meteorology; Geography; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.07601916754362864,"gpt":0.3079233442144686,"spread":0.23190417667084,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004955091,0.0001253266,0.0003481498,0.0002102559,0.0002385103,0.00005918472,0.0003440195,0.0000832665,0.0004059163],"category_scores_gemma":[0.00189361,0.0001010719,0.0001378929,0.0001401758,0.00007161663,0.0004915162,0.000008603093,0.0001399875,0.00000192774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007554307,"about_ca_system_score_gemma":0.0009927691,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03542959,"about_ca_topic_score_gemma":0.2080097,"domain_scores_codex":[0.9974669,0.0004376335,0.0007525576,0.0001530102,0.001012192,0.0001776759],"domain_scores_gemma":[0.9960647,0.0003632492,0.001743455,0.0002263479,0.001522163,0.00008012678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006242468,0.0001097281,0.8272252,0.00005806974,0.001067854,0.000006323148,0.0007801529,0.03408712,0.007361789,0.00003199412,0.0008045007,0.127843],"study_design_scores_gemma":[0.001243783,0.0005374857,0.8457121,0.00004096235,0.0004675673,0.000003420084,0.0003198528,0.1491245,0.000182581,0.001818153,0.0004499848,0.00009953923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963662,0.0007347684,0.00112048,0.0004612301,0.0006323048,0.0002876926,0.0001581915,0.000003356227,0.0002358021],"genre_scores_gemma":[0.9943147,0.00006357978,0.005199513,0.00008133487,0.00008444868,0.000003291705,0.0001642651,0.000004591637,0.00008423373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1725802,"threshold_uncertainty_score":0.9709936,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1882439680","doi":"10.1175/jhm-d-14-0191.1","title":"Performance Evaluation of the Canadian Precipitation Analysis (CaPA)","year":2015,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":135,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precipitation; Environmental science; Meteorology; Quantitative precipitation estimation; Numerical weather prediction; Climatology; Quantitative precipitation forecast; Radar; National weather service; Satellite; Computer science; Geography; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.0607525631418683,"gpt":0.2602682118734719,"spread":0.1995156487316036,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004274803,0.00005690581,0.0002020897,0.0006244484,0.00007717883,0.00001426044,0.0002322669,0.00005119346,0.0006790421],"category_scores_gemma":[0.0004504784,0.00003634911,0.0001489203,0.001032935,0.00005850202,0.0002204404,0.000003180378,0.0001041586,0.00001436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003579399,"about_ca_system_score_gemma":0.0005514108,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01161197,"about_ca_topic_score_gemma":0.3514272,"domain_scores_codex":[0.9981752,0.0004279496,0.0003710092,0.00006720839,0.0008347928,0.0001238218],"domain_scores_gemma":[0.9983606,0.00005055772,0.0004825806,0.0001178809,0.0008555756,0.0001327995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001521754,0.00000506691,0.8348704,0.000001378973,0.0002649924,2.61521e-7,0.0002872997,0.1574121,0.00004302623,0.000003284068,0.0001504148,0.006946617],"study_design_scores_gemma":[0.000282936,0.0001464042,0.8683382,0.00000350408,0.0009993443,0.000005422992,0.00007328389,0.1294748,0.00009912018,0.0003821261,0.0001581844,0.00003663693],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948152,0.0002808479,0.00002655222,0.0005253572,0.0003638663,0.00005385859,0.000005283672,0.000001300662,0.003927727],"genre_scores_gemma":[0.9995726,0.00001026353,0.0002543919,0.00006531856,0.00004972894,2.437727e-7,0.00001028603,0.000001005276,0.00003619671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3398153,"threshold_uncertainty_score":0.9949698,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1984694326","doi":"10.1175/bams-d-13-00262.1","title":"Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEX): For Measurement’s Sake, Let It Snow","year":2014,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":130,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"National Research Council Canada; University of Manitoba; McGill University; Environment and Climate Change Canada","funders":"","keywords":"Snow; Environmental science; Precipitation; Global Precipitation Measurement; Snowpack; Context (archaeology); Climatology; Meteorology; Radar; Satellite; Atmospheric sciences; Geology; Geography; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.04052970993595976,"gpt":0.2497428014036273,"spread":0.2092130914676676,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004179428,0.0002952971,0.0005188406,0.00002476686,0.0003782765,0.00006959285,0.0005914148,0.00009302553,0.0007052587],"category_scores_gemma":[0.001882423,0.0002019712,0.0007034793,0.0004399251,0.0003949848,0.00005826975,0.00004015307,0.0001244849,0.00004925801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001210578,"about_ca_system_score_gemma":0.00007135818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005842684,"about_ca_topic_score_gemma":0.0003353728,"domain_scores_codex":[0.9957126,0.0007928382,0.0005858387,0.0005357563,0.001898669,0.0004742489],"domain_scores_gemma":[0.9975961,0.0003985502,0.0007654795,0.0003844905,0.0006920459,0.0001633299],"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":[0.002424158,0.001037451,0.3267134,0.0002597944,0.002170104,4.856213e-7,0.002463738,0.03468316,0.01862285,0.001571503,0.3973958,0.2126575],"study_design_scores_gemma":[0.002392576,0.002607534,0.8029263,0.000124291,0.0006502104,0.000001253092,0.002234612,0.01183034,0.005293797,0.002163342,0.1689546,0.0008211115],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8715002,0.001460497,0.07536905,0.03798009,0.001186016,0.003485625,0.0002984541,0.0002701244,0.008449929],"genre_scores_gemma":[0.9800559,0.00005374221,0.01679191,0.002748302,0.0001618154,0.00006077859,0.0000364874,0.000007437869,0.00008363323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4762129,"threshold_uncertainty_score":0.8236148,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1111091419","doi":"10.1016/j.jhydrol.2015.08.003","title":"Assimilation of radar quantitative precipitation estimations in the Canadian Precipitation Analysis (CaPA)","year":2015,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":127,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada","funders":"European Commission","keywords":"Quantitative precipitation estimation; Environmental science; Radar; Meteorology; Precipitation; Quantitative precipitation forecast; Weather radar; Global Precipitation Measurement; Climatology; Computer science; Geography; Telecommunications; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.0659194257920925,"gpt":0.2901420700190869,"spread":0.2242226442269944,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002688817,0.00007842332,0.0002575942,0.001180263,0.00008124459,0.00003254927,0.0002075981,0.00006782043,0.0001642127],"category_scores_gemma":[0.0007763285,0.00005590627,0.0001220636,0.00128748,0.00006790254,0.0004415867,0.000001845342,0.000153445,0.00001327624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003186031,"about_ca_system_score_gemma":0.0003302923,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01844127,"about_ca_topic_score_gemma":0.6075959,"domain_scores_codex":[0.998114,0.0005787307,0.0005863442,0.00009200499,0.0004846937,0.0001442261],"domain_scores_gemma":[0.9983373,0.0003872259,0.0006048603,0.00009940821,0.0004669862,0.0001042122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006027089,0.00002636783,0.6479791,0.000003566512,0.0002394803,0.000003922391,0.006762146,0.3422045,0.00003300041,0.0003979623,0.0003426139,0.001947021],"study_design_scores_gemma":[0.0003718367,0.0003183155,0.8463883,0.000006658051,0.0003986198,0.000006205014,0.0008963435,0.145815,0.00002006162,0.005602255,0.0001164381,0.00005995423],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834024,0.0003075146,0.009105129,0.003065113,0.0001920837,0.0001256168,0.00002301034,0.000003134641,0.003775939],"genre_scores_gemma":[0.9926369,0.00001252351,0.007084282,0.0001193957,0.00003665894,7.245697e-7,0.00009752953,0.000001491219,0.00001055232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5891547,"threshold_uncertainty_score":0.988095,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4239091324","doi":"10.5194/essd-12-1525-2020","title":"AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating the GPM-era IMERG at a daily scale using APHRODITE","year":2020,"lang":"en","type":"article","venue":"Earth system science data","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":122,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"State Key Laboratory of Remote Sensing Science; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Environmental science; Precipitation; Global Precipitation Measurement; Climatology; Satellite; Scale (ratio); Calibration; Meteorology; Remote sensing; Geography; Geology; Statistics; Mathematics; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.06222603384577385,"gpt":0.2680277605387067,"spread":0.2058017266929329,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002258576,0.0002508394,0.0002850397,0.0001257806,0.001360912,0.000775964,0.002031119,0.00005594902,0.001062135],"category_scores_gemma":[0.0002882489,0.0001813108,0.00006661579,0.001911332,0.0002930594,0.003188506,0.0002606735,0.0001600538,0.0008308396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002255289,"about_ca_system_score_gemma":0.0005447314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005088196,"about_ca_topic_score_gemma":0.004357788,"domain_scores_codex":[0.9960217,0.0003282336,0.0005609211,0.001009336,0.001494546,0.0005851983],"domain_scores_gemma":[0.9977029,0.0001183632,0.0003668074,0.001179091,0.0000870987,0.0005457416],"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":[0.00015069,0.00003200768,0.1572524,0.0002181981,0.0002178654,0.00003292705,0.007884561,0.02134054,0.0245858,0.00003544697,0.7420208,0.04622876],"study_design_scores_gemma":[0.0004892366,0.00009843936,0.02201143,0.000127566,0.0001746767,0.00002993851,0.002604879,0.9125946,0.0006724356,0.0000100343,0.06063892,0.0005478011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6601653,0.01906832,0.06301513,0.05912319,0.005716861,0.005692831,0.1743744,0.001372573,0.01147141],"genre_scores_gemma":[0.9686305,0.00002246864,0.006861468,0.0008864063,0.0006315599,0.000001424291,0.02272294,0.00001183887,0.0002314287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8912541,"threshold_uncertainty_score":0.9999471,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2164394722","doi":"10.1002/2014jd022079","title":"How does the spaceborne radar blind zone affect derived surface snowfall statistics in polar regions?","year":2014,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":112,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Goddard Space Flight Center; Vlaamse regering; Rheinische Friedrich-Wilhelms-Universität Bonn; Fonds Wetenschappelijk Onderzoek; Deutscher Akademischer Austauschdienst; Deutsche Forschungsgemeinschaft; National Aeronautics and Space Administration","keywords":"Snow; Precipitation; Environmental science; Radar; Satellite; Polar; Climatology; Meteorology; Geology; Geography; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.04203288616486529,"gpt":0.2966258908233793,"spread":0.254593004658514,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003384181,0.0001456698,0.0003402518,0.00004086309,0.0002626545,0.0003473812,0.0005514486,0.00005832149,0.0003106736],"category_scores_gemma":[0.002082082,0.00007585887,0.0001381378,0.0007132888,0.0003033887,0.0003736485,0.00002745962,0.0007792981,0.0000688235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001991064,"about_ca_system_score_gemma":0.0001603706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002834046,"about_ca_topic_score_gemma":0.01509732,"domain_scores_codex":[0.9958655,0.001515024,0.0003173534,0.0001908533,0.001616272,0.0004950577],"domain_scores_gemma":[0.9959773,0.002804298,0.0002205865,0.0002381666,0.000508744,0.0002508669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003053153,0.0006818631,0.7556458,0.0001887971,0.0007743046,0.0002933362,0.001910925,0.007339273,0.01780718,0.004768881,0.03391295,0.1736236],"study_design_scores_gemma":[0.002009948,0.001214866,0.9393364,0.0001458461,0.00006768623,0.000008596585,0.001932121,0.01883635,0.001209315,0.024395,0.01055766,0.000286238],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846479,0.0006589898,0.006371947,0.007651967,0.0001223136,0.0001666299,0.00002225125,0.000006566568,0.000351483],"genre_scores_gemma":[0.9906422,0.0003152598,0.007765358,0.00005826965,0.0003848094,4.702314e-7,0.00001013633,0.00000589502,0.0008175446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1836907,"threshold_uncertainty_score":0.8424661,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3084274556","doi":"10.1007/s11263-020-01366-3","title":"Rain Rendering for Evaluating and Improving Robustness to Bad Weather","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":112,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Rendering (computer graphics); Robustness (evolution); Global illumination; Object detection; Synthetic data; Object based","retraction":null,"screen_n_in":null,"score":{"opus":0.04708621936685208,"gpt":0.3068659415989364,"spread":0.2597797222320843,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005217122,0.00005528354,0.00009898416,0.00009654243,0.00004224549,0.0001254463,0.0001851191,0.00001528547,0.00009875811],"category_scores_gemma":[0.0001011442,0.00004422926,0.00005863662,0.00006102533,0.000006342385,0.000249675,0.00001820459,0.00005250385,0.000003610483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005025043,"about_ca_system_score_gemma":0.00002047069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008379965,"about_ca_topic_score_gemma":0.000009285651,"domain_scores_codex":[0.9991743,0.00003483445,0.000243382,0.00009688612,0.0003823781,0.00006821016],"domain_scores_gemma":[0.999341,0.0001068082,0.0001502643,0.00002464785,0.0002827278,0.00009453248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001878581,0.000006791778,0.01197453,0.000008157195,0.00007295589,0.000006596063,0.0005994078,0.1950451,0.002265313,0.00001319015,0.0005027761,0.7893173],"study_design_scores_gemma":[0.0005193021,0.0004316094,0.02924458,0.00005975862,0.00001688233,0.00001189209,0.00004639604,0.9685594,0.0001395403,0.0001173201,0.0007861418,0.00006717485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.390667,0.00008089606,0.6054158,0.003367431,0.0003647812,0.0000464627,0.000004050552,0.000005118933,0.0000484858],"genre_scores_gemma":[0.8948734,0.000004821711,0.1039636,0.0006470751,0.0004954316,1.498553e-7,0.000004796731,0.000001941433,0.000008718453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7892501,"threshold_uncertainty_score":0.1803617,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226354791","doi":"10.1016/j.advwatres.2022.104144","title":"Extreme Precipitation in China: A Review on Statistical Methods and Applications","year":2022,"lang":"en","type":"review","venue":"Advances in Water Resources","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":110,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of Saskatchewan; Global Institute for Water Security","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Precipitation; Climatology; Extreme weather; Environmental science; China; Climate change; Extreme value theory; Terrain; Natural disaster; Meteorology; Geography; Statistics; Mathematics; Geology; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.06270109138381103,"gpt":0.3625907330977053,"spread":0.2998896417138943,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001588761,0.0002595742,0.0008867777,0.0003948047,0.0001061109,0.00004247165,0.000270896,0.0000613635,0.002270903],"category_scores_gemma":[0.000128791,0.0001698651,0.00009504316,0.0005197478,0.0000662684,0.000209641,0.00002689632,0.0003558776,0.00005361557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000216704,"about_ca_system_score_gemma":0.0000159118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009189872,"about_ca_topic_score_gemma":0.0005552932,"domain_scores_codex":[0.9970415,0.001198083,0.0006561839,0.0005185567,0.0003245698,0.0002610535],"domain_scores_gemma":[0.9988167,0.0007149117,0.0001740807,0.0002210356,0.000009179431,0.00006410665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004163756,0.00001533567,0.0005250529,0.005991958,0.00001181559,0.000003692362,0.0001776356,0.0001388178,2.505439e-8,0.0000268454,0.0000114055,0.9930933],"study_design_scores_gemma":[0.0000772377,0.00004458952,0.0005010167,0.004193302,0.0001274628,0.000003020828,0.00002937299,0.00008377276,1.507221e-7,0.001240939,0.9934783,0.000220837],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000008489445,0.9951646,0.0005292473,0.00004074853,0.00005399669,0.0007714265,0.00006242532,0.00001816616,0.0033509],"genre_scores_gemma":[0.00001687857,0.9953876,0.003262125,0.00007627336,0.00004871672,0.0002104422,0.0008870144,0.000008233283,0.0001026956],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9934669,"threshold_uncertainty_score":0.9986411,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2010410731","doi":"10.1175/2009jtecha1284.1","title":"A Methodology to Derive Radar Reflectivity–Liquid Equivalent Snow Rate Relations Using C-Band Radar and a 2D Video Disdrometer","year":2009,"lang":"en","type":"article","venue":"Journal of Atmospheric and Oceanic Technology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":108,"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":"National Aeronautics and Space Administration","keywords":"Disdrometer; Snow; Radar; Power law; Environmental science; Remote sensing; Meteorology; Geology; Mathematics; Physics; Computer science; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.0418292126744678,"gpt":0.2830070461496829,"spread":0.2411778334752151,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009435606,0.0001561181,0.000427239,0.00009382886,0.0001910867,0.00003719238,0.0001518743,0.0001438171,0.0002187537],"category_scores_gemma":[0.0004424303,0.0001230615,0.00007837194,0.0007432703,0.0001135499,0.0002630394,0.00001583734,0.0002643234,0.000004525299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001965213,"about_ca_system_score_gemma":0.00006878763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002232945,"about_ca_topic_score_gemma":0.00005376856,"domain_scores_codex":[0.9986919,0.000219181,0.0004290483,0.0002246123,0.0001737009,0.0002615933],"domain_scores_gemma":[0.9990529,0.0002462399,0.0003212844,0.0001236722,0.0001105817,0.000145352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002569819,0.0002421704,0.2306764,0.00005733692,0.001265243,0.0003834048,0.002768431,0.004107485,0.1673012,0.001537291,0.001504295,0.5875869],"study_design_scores_gemma":[0.005196885,0.01523974,0.8740883,0.0004374025,0.001875943,0.002987432,0.004368772,0.01480205,0.01116087,0.06099021,0.007283131,0.001569243],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9225602,0.002825555,0.07087842,0.003369692,0.0001096316,0.00009288856,0.000002636511,0.00002407794,0.0001368637],"genre_scores_gemma":[0.8847781,0.0003332977,0.1144501,0.00033472,0.000050428,1.678238e-7,0.000001035934,0.000003553288,0.00004868136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6434119,"threshold_uncertainty_score":0.50183,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106210883","doi":"10.1175/jam2253.1","title":"Error Statistics of VPR Corrections in Stratiform Precipitation","year":2005,"lang":"en","type":"article","venue":"Journal of Applied Meteorology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Homogeneity (statistics); Range (aeronautics); Stratification (seeds); Precipitation; Radar; Root mean square; Ranging; Geology; Environmental science; Physics; Computational physics; Remote sensing; Geodesy; Meteorology; Mathematics; Statistics; Materials science; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.02101189470426284,"gpt":0.2485848565884376,"spread":0.2275729618841748,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007238324,0.00006568489,0.0002280983,0.0002967664,0.00002955853,0.00000814633,0.0001062978,0.00005797163,0.001129544],"category_scores_gemma":[0.00006723632,0.00005451471,0.00004132522,0.0002174596,0.00004524442,0.0001367528,0.000002190716,0.0001606587,0.00002414227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008776879,"about_ca_system_score_gemma":0.00007202759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004774603,"about_ca_topic_score_gemma":0.004199198,"domain_scores_codex":[0.9989877,0.00005065506,0.000547675,0.00006997567,0.000221863,0.0001221428],"domain_scores_gemma":[0.9991298,0.0002093752,0.00043759,0.00005666211,0.0001144849,0.00005207874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001006136,0.0002395399,0.2336746,0.00003737741,0.0002662068,0.000008119963,0.003350218,0.2735606,0.01018074,0.003612085,0.002037656,0.4720267],"study_design_scores_gemma":[0.001873991,0.0008569557,0.9526998,0.00001685471,0.0002195676,0.00001646646,0.001688411,0.02563224,0.002790448,0.01038122,0.003614362,0.0002096661],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821679,0.0001410654,0.007321185,0.0002266744,0.0002671999,0.00008676566,0.00003808258,0.000004390951,0.009746762],"genre_scores_gemma":[0.973993,0.00004624978,0.02573655,0.00007284532,0.00009155519,4.314462e-7,0.00002391318,0.000001578872,0.00003385291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7190253,"threshold_uncertainty_score":0.9997836,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2020307155","doi":"10.1175/jas3563.1","title":"Modeling of the Melting Layer. Part III: The Density Effect","year":2005,"lang":"en","type":"article","venue":"Journal of the Atmospheric Sciences","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":106,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Snow; Snowflake; Precipitation; Supercooling; Radar; Atmospheric sciences; Liquid water content; Atmosphere (unit); Rain and snow mixed; Environmental science; Flux (metallurgy); Meteorology; Materials science; Geology; Physics; Cloud computing","retraction":null,"screen_n_in":null,"score":{"opus":0.02927699850221913,"gpt":0.2365319563875517,"spread":0.2072549578853326,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003720067,0.0000786586,0.0001595792,0.000004657301,0.0006998835,0.00007659033,0.001060063,0.00002057109,0.0001835742],"category_scores_gemma":[0.0003237312,0.00002751127,0.0002401367,0.0006937651,0.0002330694,0.0002871286,0.00003080294,0.0001558895,0.000005751289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006656321,"about_ca_system_score_gemma":0.00009157664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000287065,"about_ca_topic_score_gemma":0.0006675855,"domain_scores_codex":[0.9982091,0.0003123452,0.0003483378,0.00008977739,0.0008772921,0.0001632029],"domain_scores_gemma":[0.9989939,0.0002622449,0.000439549,0.0001620214,0.0001040736,0.00003822599],"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":[0.000008305039,0.000004226054,0.3251878,0.000001713617,0.00002239663,1.431771e-7,0.000257276,0.6678536,0.0001949695,0.000007022125,0.0001930688,0.006269432],"study_design_scores_gemma":[0.0001511459,0.00006285248,0.09124395,0.00005861745,0.00009261568,0.0000141864,0.0005347971,0.9059799,0.001155151,0.0002927541,0.000353647,0.00006039774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944037,0.0008000359,0.0001309191,0.003171319,0.0005314561,0.00005974773,4.948059e-7,0.00000297584,0.0008993613],"genre_scores_gemma":[0.9984389,0.00003905231,0.0008807703,0.0003119921,0.0002258151,1.155519e-7,5.004072e-8,9.555293e-7,0.0001023462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2381263,"threshold_uncertainty_score":0.5383009,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2252465257","doi":"10.1002/2015gl067618","title":"First observations of triple‐frequency radar Doppler spectra in snowfall: Interpretation and applications","year":2016,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":101,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Deutscher Akademischer Austauschdienst; Natural Environment Research Council; Sight Research UK","keywords":"Snow; Spectral line; Radar; Doppler effect; Doppler radar; Environmental science; Meteorology; Physics; Remote sensing; Precipitation; Computational physics; Geology; Computer science; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.05371988581068977,"gpt":0.2853494138241053,"spread":0.2316295280134155,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005035847,0.00007120994,0.0001278295,0.0002354899,0.0001033138,0.00002951205,0.0001790036,0.00002594525,0.0002500015],"category_scores_gemma":[0.0002059855,0.00005039973,0.00004436898,0.0006632838,0.0002135693,0.0002999008,0.00001218204,0.0001207324,0.00009415544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001453026,"about_ca_system_score_gemma":0.00003262913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001514524,"about_ca_topic_score_gemma":0.002877324,"domain_scores_codex":[0.9986709,0.0001420652,0.0002188295,0.0002276793,0.0004870709,0.0002534514],"domain_scores_gemma":[0.9990644,0.0005503796,0.00004585779,0.0001621684,0.00009172108,0.00008543071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001165499,0.0001327328,0.7412491,0.00009224817,0.00007477898,0.000005969518,0.0008360112,0.0001292676,0.181174,0.006068638,0.002454042,0.06766669],"study_design_scores_gemma":[0.0003293984,0.00004837901,0.9896457,0.00004628152,0.000006810427,2.88312e-7,0.00006299834,0.001014756,0.0003552956,0.007784453,0.0006214818,0.00008411632],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761642,0.000126545,0.006436135,0.01581291,0.00003998981,0.0004097779,0.00005946376,0.00002007432,0.0009309514],"genre_scores_gemma":[0.998534,0.00007966616,0.001046055,0.0001241206,0.00007620431,0.00001779084,0.00003127808,0.000002458207,0.00008838458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2483966,"threshold_uncertainty_score":0.2737341,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2519567241","doi":"10.1016/j.envsoft.2015.01.011","title":"Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka","year":2015,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":101,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University","funders":"Japan Aerospace Exploration Agency","keywords":"Inverse distance weighting; Kriging; Interpolation (computer graphics); Multivariate interpolation; Terrain; Geostatistics; Bayesian probability; Environmental science; Variable (mathematics); Spatial dependence; Statistics; Geography; Meteorology; Mathematics; Computer science; Cartography; Spatial variability; Bilinear interpolation; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.2606301012098816,"gpt":0.310371990809608,"spread":0.04974188959972642,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00104428,0.0001165868,0.000176465,0.00009889223,0.0001497287,0.00004599717,0.0001297258,0.00003693948,0.00002491619],"category_scores_gemma":[0.00005547864,0.0001140756,0.00002506949,0.00005833743,0.00003422367,0.000458452,0.00004547312,0.00005569465,0.000001296383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001874643,"about_ca_system_score_gemma":0.00001428491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002293164,"about_ca_topic_score_gemma":0.000932461,"domain_scores_codex":[0.99886,0.0001182587,0.0002912428,0.0002960679,0.0003118251,0.0001226129],"domain_scores_gemma":[0.9994357,0.0001026303,0.000150039,0.0002207771,0.00002237088,0.00006843588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003546369,0.0000566035,0.7107795,0.00001817254,0.00003825804,0.00000379449,0.01221839,0.2709032,0.0001174639,3.026608e-8,0.000004558359,0.005824462],"study_design_scores_gemma":[0.0005819274,0.00009135392,0.003763413,0.00003737839,0.0001164157,0.000008771166,0.02269739,0.9724643,0.00002774606,0.00006920033,0.000018205,0.0001238517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8227351,0.0003371078,0.1762879,0.000003016404,0.00003444082,0.0004573486,0.0001096939,0.00002755212,0.000007805251],"genre_scores_gemma":[0.9414673,0.000005068342,0.05760888,0.000009825247,0.00003785834,0.000004821239,0.0008600518,0.000004634951,0.000001534103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7070162,"threshold_uncertainty_score":0.4651867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2883837588","doi":"10.1080/07055900.2018.1474728","title":"Ten Years of Science Based on the Canadian Precipitation Analysis: A CaPA System Overview and Literature Review","year":2018,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":92,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true},"ca_institutions":"Manitoba Hydro; University of Manitoba; Impact; Environment and Climate Change Canada","funders":"","keywords":"Hydropower; Precipitation; Product (mathematics); Computer science; Environmental science; Strengths and weaknesses; Flood myth; Environmental resource management; Meteorology; Geography; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01696037814633741,"gpt":0.2270228483719965,"spread":0.2100624702256591,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001544638,0.0001056452,0.0002207822,0.00005150806,0.0003346714,0.0001230607,0.0002930999,0.0000372076,0.0005750351],"category_scores_gemma":[0.0001818884,0.00007094991,0.00009106721,0.002804995,0.0003195883,0.000187211,0.000005487542,0.00007920564,0.00003920784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002191082,"about_ca_system_score_gemma":0.0002161713,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02041249,"about_ca_topic_score_gemma":0.1561466,"domain_scores_codex":[0.998594,0.00015428,0.0002335805,0.0002635193,0.0005452494,0.0002093207],"domain_scores_gemma":[0.9989824,0.0001052338,0.0001446101,0.0003153067,0.0002884759,0.000163935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003055835,0.000023378,0.9479703,0.0009772745,0.0004569569,0.00001643059,0.002620233,0.002767057,0.00001677522,0.001539093,0.005678058,0.03790388],"study_design_scores_gemma":[0.000157781,0.0001480686,0.8912788,0.002336084,0.0007241762,0.000002226772,0.0004533137,0.1025445,0.00002992044,0.00007909926,0.002033182,0.000212851],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9368536,0.03848893,0.00006366093,0.002181758,0.0002542665,0.0007085414,0.0001540046,0.00005288103,0.0212423],"genre_scores_gemma":[0.9978462,0.0006150305,0.0003729198,0.0009915888,0.00004047854,6.612142e-7,0.0000463724,0.000002414937,0.00008429337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1357341,"threshold_uncertainty_score":0.9861107,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2093652946","doi":"10.1016/j.atmosres.2015.04.011","title":"Separating stratiform and convective rain types based on the drop size distribution characteristics using 2D video disdrometer data","year":2015,"lang":"en","type":"article","venue":"Atmospheric Research","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":92,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"McGill University; University of Alabama; National Aeronautics and Space Administration","keywords":"Disdrometer; Radar; Remote sensing; Meteorology; Wind profiler; Environmental science; Geology; Computer science; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.2046590456908283,"gpt":0.3565518843357641,"spread":0.1518928386449357,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003242467,0.0000935133,0.000127778,0.000004530312,0.0003513192,0.0002550251,0.0002941695,0.00003891193,0.0005657295],"category_scores_gemma":[0.001950833,0.00005951147,0.00001998619,0.0005195409,0.0001565673,0.0002768159,0.00003809083,0.0002076337,0.00006338971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002154714,"about_ca_system_score_gemma":0.0001606499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007820431,"about_ca_topic_score_gemma":0.0002649464,"domain_scores_codex":[0.9981108,0.0004564226,0.0001709374,0.0002709502,0.0007231568,0.0002677793],"domain_scores_gemma":[0.9980568,0.001208449,0.00006450714,0.0003396646,0.0002059859,0.0001246071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002950678,0.00006913702,0.9406664,0.00003618006,0.0001359078,0.00001901471,0.0009590195,0.002666933,0.0006425322,0.0001611866,0.004183027,0.05016554],"study_design_scores_gemma":[0.0001670537,0.0001255585,0.1718117,0.00001882103,0.0000167351,8.374341e-7,0.0006067483,0.8261122,0.00003504176,0.0002938664,0.0007267518,0.00008474776],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938027,0.0001136678,0.003040236,0.0007243468,0.00007340962,0.0002254995,0.0001914994,0.00001581776,0.001812823],"genre_scores_gemma":[0.9977081,0.00001061969,0.00157094,0.00008158058,0.00008331116,0.000001852404,0.0004024708,0.000003129976,0.0001379906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8234452,"threshold_uncertainty_score":0.6194341,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2048625779","doi":"10.1016/j.atmosres.2014.07.013","title":"Use of 2D-video disdrometer to derive mean density–size and Ze–SR relations: Four snow cases from the light precipitation validation experiment","year":2014,"lang":"en","type":"article","venue":"Atmospheric Research","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":90,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"Academy of Finland; National Aeronautics and Space Administration","keywords":"Disdrometer; Snow; Environmental science; Precipitation; Meteorology; Snowflake; Radar; Remote sensing; Atmospheric sciences; Rain gauge; Geology; Physics; Telecommunications; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.1029682492406748,"gpt":0.3050736774399993,"spread":0.2021054281993245,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001389155,0.0001112701,0.0001676616,0.00001831171,0.0003464117,0.0002027981,0.0001903916,0.00005077074,0.001082516],"category_scores_gemma":[0.002897904,0.00007791924,0.00005335347,0.0006355199,0.00008674272,0.0004571155,0.00003721823,0.0001273046,0.0001361562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001542565,"about_ca_system_score_gemma":0.000035766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004257123,"about_ca_topic_score_gemma":0.002645364,"domain_scores_codex":[0.9975095,0.0008247675,0.0002853593,0.0003122651,0.000828982,0.0002391099],"domain_scores_gemma":[0.9954976,0.003642156,0.00009177556,0.0003169629,0.000321317,0.0001301298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001497564,0.00004387214,0.9045575,0.00001143173,0.0001487349,0.000003759415,0.007957786,0.003586983,0.005521655,0.00009519546,0.004322993,0.07360034],"study_design_scores_gemma":[0.0002270164,0.0002697382,0.9768965,0.00005775705,0.00004834079,0.000001584043,0.001337149,0.01162575,0.004589543,0.001152308,0.003641113,0.0001531594],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994634,0.0002676898,0.002429057,0.001810186,0.00007386439,0.0003458566,0.00001305016,0.00001606015,0.0004102183],"genre_scores_gemma":[0.9863621,0.00008117568,0.01280581,0.0001018167,0.00008160483,0.00001296582,0.00006136587,0.000005258588,0.000487902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07344718,"threshold_uncertainty_score":0.9998306,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3044411297","doi":"10.1109/tgrs.2020.3008033","title":"Precipitation Merging Based on the Triple Collocation Method Across Mainland China","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":90,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Global Water Futures; State Key Laboratory of Remote Sensing Science; China Postdoctoral Science Foundation; State Key Laboratory of Resources and Environmental Information System; National Natural Science Foundation of China","keywords":"Mean squared error; Precipitation; Scale (ratio); Computer science; Collocation (remote sensing); Benchmark (surveying); Weighting; Meteorology; Algorithm; Remote sensing; Environmental science; Mathematics; Data mining; Statistics; Machine learning; Geology; Geography; Physics; Geodesy; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.02893915602115757,"gpt":0.2613849442538375,"spread":0.2324457882326799,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008314552,0.0001249968,0.000122707,0.00008010705,0.001036779,0.000161578,0.0001003176,0.00004297837,0.00007219009],"category_scores_gemma":[0.00005407224,0.00008517758,0.00006757789,0.0007441914,0.00009213683,0.0001929524,4.199743e-7,0.000169747,0.00002862552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007538831,"about_ca_system_score_gemma":0.00004041776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005983048,"about_ca_topic_score_gemma":0.0005091562,"domain_scores_codex":[0.9986529,0.0002170402,0.0001792886,0.0003239101,0.0004024983,0.0002243925],"domain_scores_gemma":[0.9993568,0.00027738,0.00007776604,0.0001255643,0.00004968909,0.0001127689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006735365,0.000007825823,0.00007848423,0.00000952202,0.00001145238,0.000001648735,0.002051356,0.1006435,0.001198795,0.000002167301,0.00002710169,0.8959008],"study_design_scores_gemma":[0.0002231037,0.0001079342,0.009460271,0.00003429101,0.00002727923,0.000002026181,0.0007079327,0.9855059,0.003558928,0.00009224261,0.0001612354,0.0001188667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09988334,0.00001571715,0.8935443,0.00563487,0.0002348966,0.0001646397,0.00001838805,0.00003987172,0.0004640179],"genre_scores_gemma":[0.9697122,0.00002408754,0.02865726,0.001435042,0.00004367632,9.37447e-8,0.000006238306,0.000003305491,0.0001181001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8957819,"threshold_uncertainty_score":0.7974167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014804400","doi":"10.2166/hydro.2010.056","title":"Towards better utilization of NEXRAD data in hydrology: an overview of Hydro-NEXRAD","year":2010,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":88,"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, Okanagan Campus; University of British Columbia","funders":"University Corporation for Atmospheric Research; National Science Foundation","keywords":"Hydrometeorology; Radar; Environmental science; Meteorology; Remote sensing; Computer science; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.1220614014423749,"gpt":0.3198690472201267,"spread":0.1978076457777517,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00178886,0.0001001122,0.0003652739,0.0003533979,0.00002723799,0.00002202074,0.0006314472,0.00007684524,0.0005521347],"category_scores_gemma":[0.0002211904,0.00007712734,0.00007920346,0.0003515125,0.00007098701,0.001641828,0.00002375927,0.0002550316,0.000008725165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003063037,"about_ca_system_score_gemma":0.0001130372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000114913,"about_ca_topic_score_gemma":0.001366349,"domain_scores_codex":[0.9980296,0.00007023074,0.001131032,0.0000672368,0.0005577528,0.0001441199],"domain_scores_gemma":[0.9983561,0.00007161236,0.001009099,0.0003644609,0.0001114306,0.00008731821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001414095,0.0002532082,0.8514532,0.0005022679,0.0002293502,0.00001360376,0.005557356,0.02403647,0.002218573,0.0002160005,0.0008053719,0.1145732],"study_design_scores_gemma":[0.0006719814,0.0002876446,0.2059112,0.00008893527,0.0001178437,0.00003959601,0.0003738081,0.7882794,0.0008336494,0.002001363,0.001260339,0.0001342753],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997353,0.0003920246,0.000182586,0.00021638,0.0002031346,0.00005662487,0.00004913396,0.000003298583,0.001543821],"genre_scores_gemma":[0.9936705,0.0003467558,0.005648989,0.000140928,0.00006340514,5.616344e-8,0.0001218316,0.000002290949,0.000005247088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7642429,"threshold_uncertainty_score":0.6045488,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167954354","doi":"10.1109/8.943313","title":"Effect of wet antenna attenuation on propagation data statistics","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University of British Columbia","funders":"","keywords":"Attenuation; Statistics; Fade; Antenna (radio); Fading; Path loss; Cumulative distribution function; Path (computing); Log-normal distribution; Mathematics; Remote sensing; Environmental science; Telecommunications; Acoustics; Computer science; Geology; Probability density function; Physics; Optics; Wireless","retraction":null,"screen_n_in":null,"score":{"opus":0.0342740862969056,"gpt":0.263748146754147,"spread":0.2294740604572414,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007995843,0.0001513019,0.0001859278,0.0002125667,0.0002093864,0.0000524707,0.000131254,0.00005928332,0.0003569595],"category_scores_gemma":[0.00004109395,0.0001166202,0.00003627834,0.0003399563,0.00006749672,0.0003932956,6.24229e-7,0.0001275911,0.0000695241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006891806,"about_ca_system_score_gemma":0.00002428176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001629805,"about_ca_topic_score_gemma":0.0006588323,"domain_scores_codex":[0.9986047,0.0002339925,0.0002998077,0.0003122729,0.000400067,0.0001491259],"domain_scores_gemma":[0.9991639,0.0002185543,0.000159537,0.0002719226,0.0001174175,0.00006861566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001402272,0.0001419108,0.02539811,0.0001675694,0.0001186269,0.00000738872,0.0002551343,0.00535926,0.009265324,0.00006493352,0.0001865042,0.957633],"study_design_scores_gemma":[0.001517736,0.003013991,0.06259221,0.000212047,0.0003128127,0.0000140591,0.00009668426,0.9141642,0.01722691,0.0002598351,0.0002188251,0.0003706359],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3457099,0.00006463872,0.6521801,0.0003835763,0.0002879727,0.0004661682,0.0004406742,0.000049645,0.0004172819],"genre_scores_gemma":[0.9984328,0.0003251391,0.0004169252,0.00006251711,0.00004195318,0.000004017895,0.0004926011,0.000005301992,0.000218782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9572623,"threshold_uncertainty_score":0.4755633,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2795340014","doi":"10.1080/07055900.2018.1433627","title":"An Overview of Surface-Based Precipitation Observations at Environment and Climate Change Canada","year":2018,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":87,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Precipitation; Context (archaeology); Snow; Radar; Environmental science; Computer science; Environmental resource management; Climate change; Meteorology; Identification (biology); Geography; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.06568249508963439,"gpt":0.2395826785184402,"spread":0.1739001834288058,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002631912,0.0001124016,0.0001481504,0.000004551503,0.000195984,0.00002163415,0.000111104,0.00003391108,0.002012683],"category_scores_gemma":[0.00001304217,0.0001014561,0.00003011464,0.000151275,0.00007336402,0.0002455665,0.000008276251,0.00003247314,0.00002242484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002017937,"about_ca_system_score_gemma":0.00005583197,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1465067,"about_ca_topic_score_gemma":0.6670231,"domain_scores_codex":[0.9989445,0.00009310817,0.0002231348,0.0002158128,0.0003331537,0.0001903242],"domain_scores_gemma":[0.9994267,0.00006523303,0.000138649,0.0001948792,0.00005795419,0.000116639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001718844,0.00001299703,0.9921163,0.00002467393,0.00001626257,5.378859e-7,0.000226972,0.002950699,0.00008636195,0.00002984356,0.0003385351,0.004179585],"study_design_scores_gemma":[0.0002147535,0.0001336139,0.9458526,0.00002369795,0.00004536172,2.365452e-7,0.0001277632,0.05111222,0.0002835156,0.00005228053,0.002028702,0.0001252039],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978658,0.001121145,0.00003092465,0.0002795355,0.0000830642,0.0001477707,0.0001274137,0.00001541468,0.0003289184],"genre_scores_gemma":[0.9965261,0.0003257498,0.002381548,0.0003612894,0.00005827409,5.097376e-7,0.0002888208,0.00000425401,0.00005347385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5205165,"threshold_uncertainty_score":0.9988996,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2090422664","doi":"10.1175/2009jamc1927.1","title":"Probabilistic Parameterizations of Visibility Using Observations of Rain Precipitation Rate, Relative Humidity, and Visibility","year":2009,"lang":"en","type":"article","venue":"Journal of Applied Meteorology and Climatology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":85,"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":"Visibility; Environmental science; Precipitation; Relative humidity; Meteorology; Probabilistic logic; Percentile; Humidity; Atmospheric sciences; Climatology; Statistics; Geography; Mathematics; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.04764762418521118,"gpt":0.2754946950528007,"spread":0.2278470708675895,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001607745,0.0001016959,0.0004905351,0.0001815491,0.00009877072,0.000007796527,0.00008743869,0.0001253405,0.00007773801],"category_scores_gemma":[0.0005852918,0.00008391867,0.00006454605,0.0002710165,0.0003349087,0.0002235455,0.000008518148,0.0001486122,3.546253e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003982298,"about_ca_system_score_gemma":0.00006802326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001602967,"about_ca_topic_score_gemma":0.0001157633,"domain_scores_codex":[0.9984585,0.0003268259,0.000790683,0.0001701541,0.0001236168,0.0001301872],"domain_scores_gemma":[0.997898,0.0008303371,0.0008297305,0.0001091101,0.0002693707,0.00006340121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001044362,0.0002336094,0.9230844,0.0001421632,0.0002736219,0.000002565651,0.002460446,0.007794521,0.04501804,0.01482881,0.000007433786,0.005109955],"study_design_scores_gemma":[0.0005476768,0.0004784362,0.8707411,0.00001308669,0.0002537346,0.00001631748,0.0002830104,0.005973827,0.0006730579,0.1209399,0.000006799183,0.00007300796],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958805,0.0002372472,0.003151,0.0002643254,0.00005591208,0.0001593751,0.00002068159,0.000003872718,0.0002271174],"genre_scores_gemma":[0.9880529,0.00008454679,0.01175429,0.00007371482,0.00001239209,4.664732e-7,0.00001900657,0.000001378788,0.000001283065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1061111,"threshold_uncertainty_score":0.3422104,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994731825","doi":"10.1007/s00704-009-0140-y","title":"GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies","year":2009,"lang":"en","type":"article","venue":"Theoretical and Applied Climatology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":84,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Precipitation; Climatology; Monsoon; Environmental science; Climate change; Climate model; Elevation (ballistics); Physical geography; Geology; Geography; Meteorology","retraction":null,"screen_n_in":null,"score":{"opus":0.02710249308281571,"gpt":0.299496305378414,"spread":0.2723938122955983,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005489968,0.0001088506,0.0003209322,0.0001974735,0.00005573051,0.000006963392,0.0000613207,0.00008164519,0.0001338734],"category_scores_gemma":[0.00005324059,0.00008305308,0.00005973185,0.000134829,0.00029467,0.00005976319,0.000004018278,0.00005497291,0.00000242047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008978296,"about_ca_system_score_gemma":0.00001766304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003295152,"about_ca_topic_score_gemma":0.00137482,"domain_scores_codex":[0.9990257,0.0000859871,0.0003620401,0.0001725183,0.0001324453,0.0002213137],"domain_scores_gemma":[0.9993042,0.0003535734,0.0001451555,0.00006983575,0.00007479054,0.00005249058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.002830432,0.0000842727,0.3439508,0.0001090244,0.00004524038,4.912467e-7,0.0009544072,0.0008717138,0.0006510682,0.6289805,0.00001196583,0.02151005],"study_design_scores_gemma":[0.001214885,0.0005307552,0.6988486,0.00006767274,0.00007209929,0.00000126444,0.0005409023,0.08712946,0.0004858746,0.2109637,0.000004157047,0.0001406706],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949108,0.0002516978,0.003031334,0.0008845361,0.00003100969,0.0003177509,0.00009382884,0.00001014556,0.0004688323],"genre_scores_gemma":[0.9984322,0.00008523587,0.001021101,0.0001247576,0.0000265909,0.000008655428,0.0002990695,0.00000203793,3.645187e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4180169,"threshold_uncertainty_score":0.3386806,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W423317447","doi":"10.1016/j.agrformet.2015.05.003","title":"Empirical estimation of daytime net radiation from shortwave radiation and ancillary information","year":2015,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":83,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; National Water Center, United Arab Emirates University; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences; Environment Canada; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canadian Foundation for Climate and Atmospheric Sciences; Center for Neuroscience and Regenerative Medicine; University of Virginia; Natural Resources Canada; Université Laval; Università degli Studi della Tuscia; U.S. Department of Energy; National Science Foundation","keywords":"Shortwave radiation; Shortwave; Environmental science; Albedo (alchemy); Radiation; Meteorology; Remote sensing; Geography; Radiative transfer; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02042883994495708,"gpt":0.2185484453340794,"spread":0.1981196053891224,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002302752,0.00008341153,0.0001486517,0.0000660333,0.00005412045,0.00003178259,0.00004041897,0.00007966145,0.00005235243],"category_scores_gemma":[0.0001217437,0.00005348827,0.00002293891,0.0001395593,0.00004680582,0.0007639493,0.000006897929,0.00004846078,0.00002024241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004611205,"about_ca_system_score_gemma":0.00001490293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007629534,"about_ca_topic_score_gemma":0.0004742162,"domain_scores_codex":[0.9993159,0.00008095848,0.0002305735,0.0001059616,0.0001651316,0.0001014993],"domain_scores_gemma":[0.9995614,0.00009004473,0.0001336526,0.00004519765,0.00007291804,0.00009684598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004259754,0.000004990254,0.8164024,0.00000540308,0.00003681575,2.667674e-7,0.001097421,0.009278547,0.00005513006,0.00005420348,0.0009899546,0.1720323],"study_design_scores_gemma":[0.0003333676,0.0001298626,0.9444785,0.000002370352,0.00005270627,0.000004181206,0.0001948014,0.05269057,0.00005637559,0.001321727,0.0006602902,0.00007522282],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973422,0.001027844,0.0004382981,0.0004870199,0.00009383816,0.00009342239,0.00004131136,0.00001615958,0.0004599081],"genre_scores_gemma":[0.9969001,0.000140188,0.0007923961,0.0001365081,0.00006125052,0.000001216935,0.001957582,6.446249e-7,0.00001013222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.171957,"threshold_uncertainty_score":0.2181188,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2172542931","doi":"10.1175/jam2222.1","title":"Variability of Drop Size Distributions: Noise and Noise Filtering in Disdrometric Data","year":2005,"lang":"en","type":"article","venue":"Journal of Applied Meteorology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":82,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Spurious relationship; Statistics; Mathematics; Regression; Noise (video); Linear regression; Robust regression; Regression analysis; Simple linear regression; Sample size determination; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.02611319092023593,"gpt":0.2458837760685952,"spread":0.2197705851483592,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002215479,0.00007786056,0.0003118572,0.0001963949,0.0000338756,0.00001352476,0.0002900681,0.00005419618,0.0006010554],"category_scores_gemma":[0.0006153955,0.0000621025,0.00003791643,0.0004915755,0.00007862852,0.0002274419,0.00003075222,0.0001543273,0.000005499152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008101015,"about_ca_system_score_gemma":0.00004091156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000042972,"about_ca_topic_score_gemma":0.0002202788,"domain_scores_codex":[0.998871,0.00009318966,0.0005351335,0.0001507388,0.0002024574,0.0001475223],"domain_scores_gemma":[0.9987172,0.0006245432,0.0003182949,0.0002022285,0.00005743833,0.00008032686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004117943,0.0001423621,0.8854975,0.00004105444,0.00013565,0.000007216337,0.0002082758,0.004205951,0.01492052,0.0001977929,0.0001689535,0.09406292],"study_design_scores_gemma":[0.0007533837,0.0001037022,0.9883825,0.000008232808,0.0001024876,0.00001330642,0.00006007332,0.006944898,0.0004419013,0.002052148,0.001050393,0.00008696868],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955419,0.0003773611,0.002605853,0.0004150371,0.00006201703,0.00005866163,0.00007981823,0.000003015683,0.0008563327],"genre_scores_gemma":[0.9902419,0.0001378392,0.009463202,0.000045702,0.00007219105,2.895791e-7,0.00003261865,0.000001310702,0.000004964007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.102885,"threshold_uncertainty_score":0.6581135,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2624989982","doi":"10.5194/hess-22-1437-2018","title":"Testing and development of transfer functions for weighing precipitation gauges in WMO-SPICE","year":2018,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":79,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"National Oceanic and Atmospheric Administration; Environment and Climate Change Canada","keywords":"Precipitation; Gauge (firearms); Environmental science; Shields; Shield; Meteorology; Geology; Materials science; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.05394305567802384,"gpt":0.2404432550239304,"spread":0.1865001993459065,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001201826,0.00006327571,0.0001292048,0.0001602911,0.0004048987,0.00002607813,0.00005777154,0.0000387711,0.00003284347],"category_scores_gemma":[0.00006570177,0.00004979226,0.00001135849,0.0002555305,0.0002379508,0.00020066,0.000003382793,0.00002853319,0.00000497077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001143041,"about_ca_system_score_gemma":0.00004634825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001734763,"about_ca_topic_score_gemma":0.004320873,"domain_scores_codex":[0.9992242,0.00006917613,0.0002337064,0.0002017194,0.0001165742,0.0001545706],"domain_scores_gemma":[0.9995188,0.0003064375,0.00004918408,0.0000336112,0.00005490253,0.00003704611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002617476,0.000005295657,0.9415945,0.00008099234,0.0000142958,2.27861e-7,0.002650782,0.000630816,0.0008237452,0.0001724012,0.000003039944,0.05399769],"study_design_scores_gemma":[0.0003307892,0.0003337842,0.9165037,0.0000859736,0.00001936199,0.000005061857,0.002154432,0.07950658,0.0005178591,0.0001626519,0.0002652053,0.0001146006],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941837,0.0002592507,0.003373198,0.00005939867,0.0001340649,0.0001239985,0.000006459557,0.00001264681,0.001847351],"genre_scores_gemma":[0.9924053,0.000003209359,0.007504872,0.00001898753,0.00004066111,0.00000347255,0.000005600287,7.55784e-7,0.00001716431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07887577,"threshold_uncertainty_score":0.3114195,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4245424902","doi":"10.1007/s11600-018-0226-y","title":"Analysis of deterministic and geostatistical interpolation techniques for mapping meteorological variables at large watershed scales","year":2018,"lang":"en","type":"article","venue":"Acta Geophysica","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Kriging; Inverse distance weighting; Interpolation (computer graphics); Multivariate interpolation; Watershed; Geostatistics; Spline (mechanical); Precipitation; Remote sensing; Weighting; Environmental science; Spline interpolation; Spatial variability; Meteorology; Mathematics; Bilinear interpolation; Geology; Statistics; Computer science; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02177538478060756,"gpt":0.2495931379751193,"spread":0.2278177531945117,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003066771,0.00009591788,0.0002697516,0.0001785194,0.0001548763,0.00002979107,0.00009743595,0.00004657183,0.0005158642],"category_scores_gemma":[0.0001391981,0.00007310435,0.00009200034,0.0003124806,0.0001345653,0.0001088001,0.00001973345,0.00003095733,0.000009085998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003248687,"about_ca_system_score_gemma":0.000006832648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008942967,"about_ca_topic_score_gemma":0.0005859159,"domain_scores_codex":[0.9991623,0.00006374506,0.0002227811,0.0002215085,0.0001458372,0.0001838322],"domain_scores_gemma":[0.9992529,0.0003736902,0.0001078688,0.0001134726,0.0000970925,0.00005493973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004614045,0.0001233719,0.8901663,0.0001067643,0.002074012,0.000002531844,0.001605054,0.00002624351,0.04219536,0.001197532,0.0006165448,0.06142489],"study_design_scores_gemma":[0.0001484567,0.0002632501,0.7018816,0.000009549707,0.0007514165,4.124462e-7,0.00008596772,0.2933124,0.0008385068,0.002002277,0.0005723346,0.0001338456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9631522,0.00002069446,0.03563391,0.0000984116,0.00003032114,0.0001356546,0.0002926232,0.00003291385,0.000603311],"genre_scores_gemma":[0.9891847,0.000006819772,0.01016498,0.00007440307,0.00006351936,0.000003454513,0.0004583102,0.000001983677,0.00004183709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2932861,"threshold_uncertainty_score":0.5648351,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1935178354","doi":"10.1175/bams-d-15-00048.1","title":"The Threat to Weather Radars by Wireless Technology","year":2015,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":72,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Wireless; Radar; Telecommunications; Process (computing); Wireless network; Computer science; Radio spectrum; Weather radar; Work (physics); Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0173076436645096,"gpt":0.2233788541627703,"spread":0.2060712104982607,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009210416,0.0001133433,0.0002336663,0.00001228203,0.0002330007,0.00002473997,0.0007235872,0.00004919103,0.0004106211],"category_scores_gemma":[0.000256396,0.00005165854,0.0002135594,0.0005342828,0.000796475,0.000009624428,0.00005795774,0.0001496549,0.0001598252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008260768,"about_ca_system_score_gemma":0.00002055443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007623963,"about_ca_topic_score_gemma":0.00005724527,"domain_scores_codex":[0.9987211,0.0002138635,0.0001944829,0.000204163,0.0003985089,0.0002678483],"domain_scores_gemma":[0.9991016,0.0002435956,0.0001840966,0.0002828811,0.0000794476,0.0001083874],"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":[0.0001455437,0.00004938926,0.4064916,0.000002442753,0.0002089003,6.065645e-7,0.0003135398,0.0005775596,0.001170694,0.0001933556,0.4447548,0.1460915],"study_design_scores_gemma":[0.0005743977,0.0009716745,0.144883,0.00001075086,0.0001352984,0.000004146909,0.006672146,0.0008656517,0.001826906,0.004534804,0.8391197,0.0004014663],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9409244,0.0006284846,0.0003025395,0.05559494,0.0000846657,0.000150041,0.00001957085,0.00004378783,0.002251585],"genre_scores_gemma":[0.9929222,0.0001119828,0.002559347,0.003465528,0.00003160574,0.000003896776,0.000003573078,0.000002820098,0.0008990874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3943649,"threshold_uncertainty_score":0.4496013,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2015421666","doi":"10.1016/j.jhydrol.2005.11.046","title":"Radar calibration by gage, disdrometer, and polarimetry: Theoretical limit caused by the variability of drop size distribution and application to fast scanning operational radar data","year":2006,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Disdrometer; Radar; Remote sensing; Polarimetry; Calibration; Differential phase; Environmental science; Meteorology; Rain gauge; Computer science; Mathematics; Geology; Statistics; Physics; Optics","retraction":null,"screen_n_in":null,"score":{"opus":0.009223132467648337,"gpt":0.2258112695667997,"spread":0.2165881370991513,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001585427,0.00007714136,0.000179019,0.00003246645,0.0001382016,0.0000487041,0.0001777324,0.00005469587,0.000115114],"category_scores_gemma":[0.0003034213,0.0000519877,0.00002302233,0.0001670269,0.0001907842,0.0002865488,0.00002162373,0.0001218576,7.423978e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006271511,"about_ca_system_score_gemma":0.00003228193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002836217,"about_ca_topic_score_gemma":0.00008001754,"domain_scores_codex":[0.9987726,0.0003266159,0.0003737397,0.0001610922,0.0002585031,0.000107417],"domain_scores_gemma":[0.999056,0.0004539771,0.000209841,0.0001433526,0.00007014586,0.00006672844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003601087,0.0001792437,0.8922026,0.00002598312,0.0001980995,0.000002400935,0.0002063399,0.001544886,0.07013065,0.002905558,0.01155831,0.02068578],"study_design_scores_gemma":[0.001240186,0.0006218936,0.7654059,0.00001499921,0.000332847,0.00007062546,0.0001016622,0.2168571,0.002593015,0.007688044,0.004829044,0.0002446875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8603352,0.000473519,0.134813,0.00369894,0.00003889678,0.0001017606,0.0004854811,0.000003409046,0.00004971734],"genre_scores_gemma":[0.997914,0.00002323373,0.001084461,0.0001473512,0.00006545066,2.476977e-7,0.0007551925,0.000001732771,0.000008378825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2153122,"threshold_uncertainty_score":0.2119997,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2031247233","doi":"10.3137/ao.460202","title":"Field accuracy of Canadian rain measurements","year":2008,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada","funders":"Transport Canada","keywords":"Rain gauge; Precipitation; Meteorology; Environmental science; Gauge (firearms); National weather service; Hydrology (agriculture); Geology; Geography; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04747071629957189,"gpt":0.227614636698609,"spread":0.1801439203990371,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003045144,0.000114296,0.0001730281,0.00003571013,0.0001924828,0.00001679916,0.0002476014,0.00005700533,0.007373713],"category_scores_gemma":[0.0002658333,0.0001015034,0.00009479453,0.0006338003,0.00004260788,0.0002032649,0.000004357358,0.00008208201,0.0001999863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007742221,"about_ca_system_score_gemma":0.0001897005,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2642716,"about_ca_topic_score_gemma":0.3239793,"domain_scores_codex":[0.9988114,0.00006664045,0.0002535787,0.0001810277,0.0004130907,0.0002743096],"domain_scores_gemma":[0.9992431,0.0001125595,0.0001035281,0.0002012236,0.0001081918,0.000231356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001252013,0.000008152816,0.970628,0.00000570038,0.00004938502,0.000007480006,0.0003072155,0.0006747739,0.00003331109,0.000009242033,0.02069579,0.007568429],"study_design_scores_gemma":[0.000524954,0.0001575349,0.9715287,0.00003006031,0.00005333605,0.000007372293,0.0002997803,0.00202615,0.001553155,0.0002716735,0.02324022,0.0003070718],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9337229,0.0003753107,0.00004332891,0.0005164698,0.000159241,0.00009848122,0.0000181446,0.00002434284,0.0650418],"genre_scores_gemma":[0.9972994,0.00008506868,0.001041532,0.0005753624,0.00005977727,1.487688e-7,0.00004857654,0.000003354615,0.0008867389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06415506,"threshold_uncertainty_score":0.9935337,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3095201267","doi":"10.1175/jhm-d-20-0100.1","title":"Large-Scale Analysis of Global Gridded Precipitation and Temperature Datasets for Climate Change Impact Studies","year":2020,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":69,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precipitation; Environmental science; Satellite; Climatology; Scale (ratio); Climate change; Computer science; Climate Forecast System; Gauge (firearms); Quantitative precipitation estimation; Meteorology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.04765562577156633,"gpt":0.3153899252982879,"spread":0.2677342995267215,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007913833,0.0001032353,0.0005386773,0.0002509992,0.000079058,0.0000199291,0.0001269193,0.00006363041,0.0001710538],"category_scores_gemma":[0.0002520817,0.00007210246,0.0002256013,0.0008036941,0.00004424395,0.0003159728,0.00001003998,0.00007870195,0.000002409787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008018761,"about_ca_system_score_gemma":0.00002822353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009093427,"about_ca_topic_score_gemma":0.003758493,"domain_scores_codex":[0.9989221,0.0001287614,0.000412327,0.0001362615,0.000197275,0.000203236],"domain_scores_gemma":[0.9990313,0.0001399418,0.0004077316,0.00006731676,0.0001948396,0.0001588852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003625391,0.00002175664,0.9897976,0.00005756223,0.002853176,0.000005247155,0.001764254,0.002266816,0.0005945686,0.00001183629,0.0008896137,0.001375013],"study_design_scores_gemma":[0.0006948112,0.001100795,0.9787021,0.00001071851,0.002143617,0.000007204087,0.0007482668,0.01600293,0.00003643515,0.0002667205,0.0001964687,0.00008997004],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916081,0.003916644,0.00007639548,0.001418889,0.0001163097,0.00009794882,0.002745444,0.000003694206,0.00001650424],"genre_scores_gemma":[0.997497,0.0006862603,0.0009246818,0.0003853403,0.0001094282,7.620183e-7,0.000394567,0.000001502087,5.208398e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01373611,"threshold_uncertainty_score":0.2940253,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2109938968","doi":"10.5194/amt-6-3635-2013","title":"Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity","year":2013,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Jet Propulsion Laboratory; Colorado State University; National Aeronautics and Space Administration","keywords":"Disdrometer; Snow; Environmental science; Precipitation; Radar; Meteorology; Remote sensing; Particle (ecology); Snowflake; Latitude; Atmospheric sciences; Physics; Geology; Computer science; Geodesy","retraction":null,"screen_n_in":null,"score":{"opus":0.02345897393193841,"gpt":0.2264150345275258,"spread":0.2029560605955874,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007917347,0.0001487023,0.0002744094,0.000022042,0.00006252217,0.00004201019,0.00008054057,0.00004847888,0.0002435638],"category_scores_gemma":[0.0001610365,0.0001132758,0.00003885812,0.0002175446,0.0001050274,0.0003440302,0.0000101886,0.00005512403,0.000002977754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009167818,"about_ca_system_score_gemma":0.00002261933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001230405,"about_ca_topic_score_gemma":0.0001745055,"domain_scores_codex":[0.998854,0.0001129484,0.0002953207,0.0002021849,0.0003887305,0.0001468093],"domain_scores_gemma":[0.9992568,0.00007354685,0.0002386883,0.0001419592,0.000221514,0.00006743131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004175727,0.0000368074,0.351546,0.0001056889,0.00008187906,2.846533e-7,0.0001994402,0.000005420529,0.566845,0.00003110617,0.00007404363,0.08103261],"study_design_scores_gemma":[0.0001289327,0.0003732255,0.7957672,0.000137026,0.00005040291,5.984396e-7,0.0000423077,0.001600398,0.2004466,0.001252365,0.00006081963,0.0001400462],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954563,0.000222809,0.003180071,0.0001494054,0.00002574674,0.0003246508,0.000008433784,0.00005740378,0.0005751953],"genre_scores_gemma":[0.9905844,0.0003294299,0.0089646,0.00006265768,0.00001202523,0.000007644421,0.00001919663,0.000004298724,0.00001570554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4442213,"threshold_uncertainty_score":0.4619253,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2767638814","doi":"10.3390/rs9111142","title":"Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Goddard Space Flight Center; National Natural Science Foundation of China; Japan Aerospace Exploration Agency; Canada Excellence Research Chairs, Government of Canada; National Aeronautics and Space Administration","keywords":"Global Precipitation Measurement; Precipitation; Environmental science; Radar; Meteorology; Precipitation types; Climatology; Remote sensing; Geology; Computer science; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.03401608123823094,"gpt":0.2740208816536024,"spread":0.2400048004153714,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001151737,0.0002141978,0.000299474,0.0001857937,0.0004087459,0.0002602609,0.0001022155,0.0001348823,0.00001413327],"category_scores_gemma":[0.001342282,0.0002000475,0.00004282509,0.0002417853,0.0001491806,0.001149329,0.00001988485,0.0001334438,0.000006552325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004782225,"about_ca_system_score_gemma":0.0000973943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001178388,"about_ca_topic_score_gemma":0.002418454,"domain_scores_codex":[0.9979379,0.0002923202,0.0006245995,0.0004436538,0.0004294329,0.0002721487],"domain_scores_gemma":[0.998682,0.000212256,0.0003965363,0.0002841749,0.0003218106,0.0001032341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004987976,0.00005172049,0.2954634,0.0004202987,0.0001423991,0.000008279195,0.003218486,0.002788902,0.06787948,0.0005598447,0.00002528023,0.6289431],"study_design_scores_gemma":[0.0007074418,0.0001071309,0.5587528,0.0001523413,0.00007234467,0.000003122671,0.0004458072,0.4301524,0.001857037,0.007572585,0.000005192393,0.0001718256],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918191,0.0002884677,0.005833529,0.0006491874,0.0002713121,0.0004657735,0.00002223175,0.00003458014,0.0006157636],"genre_scores_gemma":[0.962526,0.0002095852,0.0368798,0.00003302999,0.00005811009,1.297852e-7,0.0002619064,0.000007700295,0.00002373336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6287713,"threshold_uncertainty_score":0.8157701,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3023506241","doi":"10.1109/tgrs.2020.2989183","title":"Infrared Precipitation Estimation Using Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Mean squared error; Precipitation; Channel (broadcasting); Artificial intelligence; Correlation coefficient; Artificial neural network; Pattern recognition (psychology); Machine learning; Statistics; Mathematics; Meteorology; Telecommunications; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.03920391852620005,"gpt":0.2387745704677226,"spread":0.1995706519415226,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002091731,0.0001117596,0.0001169039,0.00008316153,0.0006111487,0.0001046242,0.00005983766,0.00004633253,0.00008325421],"category_scores_gemma":[0.0000198219,0.0001021134,0.0000529276,0.0005571502,0.0001150754,0.0004278212,4.666788e-7,0.0001285757,0.00002429256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000748051,"about_ca_system_score_gemma":0.00003869289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002599358,"about_ca_topic_score_gemma":0.0001328022,"domain_scores_codex":[0.9989122,0.00008089945,0.000196158,0.0002724264,0.0003226164,0.0002156561],"domain_scores_gemma":[0.9995636,0.00008522111,0.00007650464,0.00006771903,0.00006365724,0.0001432902],"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":[0.0000232837,0.000002285548,0.00007581887,0.000005935208,0.000008194676,0.000001542152,0.000291055,0.5369917,0.000340331,0.000001501727,0.00001422254,0.4622441],"study_design_scores_gemma":[0.0001560856,0.00006239247,0.005827985,0.0000278331,0.00003673432,0.000009165717,0.0001164748,0.9928595,0.0002832003,0.0004636619,0.00003024176,0.0001266718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1905036,0.00005007494,0.8082862,0.0004695888,0.0003116668,0.00008861075,0.000009478167,0.00004834231,0.0002324644],"genre_scores_gemma":[0.9154928,0.00002213025,0.08371617,0.0006019397,0.00008882809,1.695969e-8,0.0000102706,0.000002634773,0.00006517954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7249892,"threshold_uncertainty_score":0.4700523,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2138706393","doi":"10.1002/hyp.1350","title":"Assessment of the impact of meteorological network density on the estimation of basin precipitation and runoff: a case study","year":2003,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precipitation; Environmental science; Kriging; Flood myth; Surface runoff; Drainage basin; Hydrology (agriculture); Drainage; Meteorology; Statistics; Geology; Mathematics; Geography; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.04224512651912046,"gpt":0.2901773533221452,"spread":0.2479322268030247,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001267143,0.00009628578,0.0002284592,0.00003021314,0.0001332529,0.00001272129,0.0001105672,0.00004637237,0.0002523067],"category_scores_gemma":[0.001118417,0.00004046442,0.00007293465,0.0004378523,0.000161159,0.00007139694,0.00000976259,0.00009397975,6.247058e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003274541,"about_ca_system_score_gemma":0.00005850233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003609057,"about_ca_topic_score_gemma":0.0003213383,"domain_scores_codex":[0.9985544,0.0005977704,0.0002901112,0.0001541495,0.0002924369,0.0001111402],"domain_scores_gemma":[0.9984657,0.0009619428,0.0002792701,0.0001326995,0.000130332,0.00003008527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003179139,0.0001384953,0.8309335,0.00001798123,0.00005496679,0.000003102097,0.0001796253,0.167772,0.00003989427,0.000100026,0.00001195375,0.0007167456],"study_design_scores_gemma":[0.000182138,0.001096192,0.9591697,0.00001207708,0.00008309208,0.0000136029,0.0002843948,0.03543446,0.0002111417,0.003462577,7.519538e-7,0.00004987525],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984384,0.0001079196,0.0004044811,0.00006336986,0.00001829401,0.0003019461,0.000007340014,0.000006076637,0.0006522148],"genre_scores_gemma":[0.9995179,0.000012137,0.0004193807,0.00003217519,0.000007486416,0.000003105771,0.00000368486,9.892874e-7,0.000003105846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1323375,"threshold_uncertainty_score":0.2762581,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3017350530","doi":"10.3390/rs12081258","title":"Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Environmental science; Global Precipitation Measurement; Climatology; Meteorology; Quantitative precipitation estimation; Precipitation; Flash flood; Storm; Data assimilation; Flood myth; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.1098989412973402,"gpt":0.2784818655857837,"spread":0.1685829242884436,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005523901,0.00008955094,0.0001838789,0.0001397062,0.0001410269,0.00003334408,0.0000338281,0.00004246315,0.000003296052],"category_scores_gemma":[0.0005921076,0.00008832663,0.00003089696,0.0003243461,0.0001018701,0.0001826383,0.00001073676,0.00003943122,9.449301e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007963832,"about_ca_system_score_gemma":0.00002614847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002003051,"about_ca_topic_score_gemma":0.0001290534,"domain_scores_codex":[0.999027,0.0001014463,0.0002954804,0.0002131483,0.0002666317,0.00009629125],"domain_scores_gemma":[0.999009,0.0002871797,0.0002717534,0.00004820415,0.0003301875,0.00005374565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001606885,0.00001719587,0.2585176,0.0006469263,0.00006967258,2.541675e-7,0.003797554,0.001833206,0.07545777,0.00007989379,7.38667e-7,0.6594185],"study_design_scores_gemma":[0.000360797,0.00004688807,0.4907678,0.00005363369,0.00005059036,8.579405e-7,0.0003104339,0.5049179,0.00287064,0.0005530181,0.000002950084,0.00006451579],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.938188,0.001292793,0.06011565,0.000055708,0.00002833947,0.0002377326,0.00003505037,0.000007227405,0.00003950125],"genre_scores_gemma":[0.9712308,0.0002266036,0.02844679,0.00001204936,0.00001428617,2.142466e-8,0.00005783143,0.000002675196,0.000008948708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.659354,"threshold_uncertainty_score":0.3601856,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}