{"meta":{"query_hash":"e87623b3f70d","filters":{"venue":"Nature Environment and Pollution Technology"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/e87623b3f70d","api":"https://metacan.xera.ac/api/v1/cohort?venue=Nature+Environment+and+Pollution+Technology"},"results":[{"id":"W2578489870","doi":"","title":"Quasi-3D numerical simulation of salinity transport for reservoir initial impoundment","year":2015,"lang":"en","type":"article","venue":"Nature Environment and Pollution Technology","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Salinity; Hydrology (agriculture); Groundwater; Estuary; Water quality; Surface water; Geology; Environmental science; Environmental engineering; Geotechnical engineering; Oceanography","score_opus":0.021269135978253757,"score_gpt":0.257404944275638,"score_spread":0.23613580829738423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2578489870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9307696,0.0015374271,0.06378728,0.0032722,0.000120235934,0.00018211482,0.00011582211,0.0000541898,0.00016109749],"genre_scores_gemma":[0.99679494,0.00006262331,0.0027993908,0.000101012134,0.00005419187,0.0000021833023,0.00013667386,0.0000017238596,0.000047267084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927133,0.000029440254,0.00019908878,0.00019679524,0.00015166865,0.00015167327],"domain_scores_gemma":[0.999693,0.00003680413,0.00007787623,0.00011098288,0.000016613452,0.000064766275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002456509,0.000089002475,0.00017437218,0.00011063096,0.000066939785,0.0000032079765,0.000084944986,0.0004042428,0.000118947915],"category_scores_gemma":[0.00006631673,0.000069438465,0.000047127294,0.00012178076,0.00011584807,0.000045945733,0.00000808269,0.00020951829,0.000009868431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004129259,0.0002089844,0.3078293,0.000022699578,0.000062761545,0.000003635765,0.00015845185,0.61377543,0.00006888988,0.000719442,0.00015042882,0.07658707],"study_design_scores_gemma":[0.0021295114,0.0029879606,0.10841345,0.000016775033,0.00018922827,0.000009235362,0.00045315185,0.7995561,0.0006592878,0.032072235,0.05306249,0.00045058184],"about_ca_topic_score_codex":0.00010012785,"about_ca_topic_score_gemma":0.00006343426,"teacher_disagreement_score":0.19941585,"about_ca_system_score_codex":0.0000067363626,"about_ca_system_score_gemma":0.000012786832,"threshold_uncertainty_score":0.31178907},"labels":[],"label_agreement":null},{"id":"W3120875142","doi":"10.46488/nept.2020.v19i05.013","title":"A New Index Contributing to an Early Warning System for Cyanobacterial Bloom Occurrence in Atlantic Canada Lakes","year":2020,"lang":"en","type":"article","venue":"Nature Environment and Pollution Technology","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Bloom; Phytoplankton; Nova scotia; Dominance (genetics); Algal bloom; Environmental science; Ecology; Trophic level; Oceanography; Geography; Biology; Nutrient; Geology","score_opus":0.0036360906718933177,"score_gpt":0.16656574930898085,"score_spread":0.16292965863708753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120875142","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99520075,0.00022079033,0.0011834775,0.0025400005,0.0002221691,0.00031949033,0.00020392679,0.000049600192,0.000059767557],"genre_scores_gemma":[0.9994132,0.000008656382,0.000105453015,0.00022797886,0.00012915583,0.0000035223431,0.000085940344,0.000002128136,0.000024001753],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99911106,0.00002581793,0.00017702751,0.000267219,0.00010871915,0.0003101746],"domain_scores_gemma":[0.9996834,0.00002399231,0.000065625885,0.00008147545,0.0000047716503,0.000140769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010153872,0.00011276354,0.00018148831,0.00007644701,0.00010133269,0.000024188612,0.00013306197,0.00022361832,0.000069666414],"category_scores_gemma":[0.000049547336,0.00010327022,0.000014062755,0.00016156632,0.000015948013,0.00007065927,0.000031739764,0.00025124595,0.000006578445],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001279844,0.0000036230947,0.95910054,0.000036035985,0.000008986336,0.000012698449,0.000085568,0.00029401478,0.00048206007,0.0002348243,0.0002088411,0.039404836],"study_design_scores_gemma":[0.0019798786,0.001100251,0.838575,0.00011413025,0.00003355647,0.000033991862,0.000996625,0.012504913,0.0011180004,0.000089845256,0.1428302,0.00062361656],"about_ca_topic_score_codex":0.10823503,"about_ca_topic_score_gemma":0.42048794,"teacher_disagreement_score":0.3122529,"about_ca_system_score_codex":0.000022616026,"about_ca_system_score_gemma":0.000073161864,"threshold_uncertainty_score":0.8977033},"labels":[],"label_agreement":null},{"id":"W3165975092","doi":"10.46488/nept.2021.v20i02.043","title":"Statistical Downscaling of Rainfall Under Climate Change in Krishna River Sub-basin of Andhra Pradesh, India Using Artificial Neural Network (ANN)","year":2021,"lang":"en","type":"article","venue":"Nature Environment and Pollution Technology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Downscaling; Climate change; Structural basin; Artificial neural network; Environmental science; Climatology; Geography; Physical geography; Water resource management; Geology; Oceanography; Artificial intelligence; Computer science; Geomorphology","score_opus":0.014913618804682454,"score_gpt":0.2507314066366191,"score_spread":0.23581778783193663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165975092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971582,0.0005031252,0.0009881114,0.000968674,0.0000993396,0.0001621431,0.00004191027,0.000027288637,0.000051207273],"genre_scores_gemma":[0.993985,0.00012955157,0.0055651185,0.00024039736,0.00004353256,0.00000521948,0.000017124208,0.000011298106,0.0000027559356],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985675,0.00011118454,0.00035587425,0.0003584627,0.00021662527,0.00039032666],"domain_scores_gemma":[0.9995407,0.000051465075,0.00017602934,0.00017756244,0.0000038711287,0.000050393744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028946853,0.00015656606,0.00029370617,0.000097757715,0.000083281535,0.0000062681397,0.00010589315,0.00061910786,0.0002482834],"category_scores_gemma":[0.00006734045,0.00014964094,0.00003751619,0.0004023008,0.0008808094,0.00007895262,0.0002830901,0.0005521921,0.000006834491],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023999884,0.0005621093,0.6650473,0.00006887767,0.000044068962,0.00012938764,0.00053570425,0.113426365,0.11578381,0.02003555,0.000076321434,0.0840505],"study_design_scores_gemma":[0.0010541158,0.00043697664,0.91234344,0.00011179846,0.0000883869,0.000088407636,0.00006793905,0.035362937,0.024604267,0.024432525,0.00090673903,0.00050248305],"about_ca_topic_score_codex":0.000057770118,"about_ca_topic_score_gemma":0.0000337917,"teacher_disagreement_score":0.24729611,"about_ca_system_score_codex":0.00011353798,"about_ca_system_score_gemma":0.0000070739484,"threshold_uncertainty_score":0.61021805},"labels":[],"label_agreement":null},{"id":"W3167239440","doi":"10.46488/nept.2021.v20i02.034","title":"Synchrotron Based TXRF for Assessment of Treated Wastewater","year":2021,"lang":"en","type":"article","venue":"Nature Environment and Pollution Technology","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Synchrotron; Wastewater; Environmental science; Environmental chemistry; Chemistry; Environmental engineering; Physics; Optics","score_opus":0.008065348452349875,"score_gpt":0.256179107832765,"score_spread":0.2481137593804151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167239440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99232185,0.0002725715,0.0019928701,0.00495675,0.00006321552,0.00014114371,0.000031920463,0.0000464348,0.00017325436],"genre_scores_gemma":[0.99230784,0.000043878525,0.0067871236,0.000053127525,0.000021876142,0.000028483788,0.000037659232,0.000008305451,0.0007116997],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915016,0.000040295647,0.0001791264,0.00029366964,0.000154746,0.00018198515],"domain_scores_gemma":[0.9996318,0.000016870865,0.00007520851,0.00023588247,0.0000042955594,0.000035930276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001376748,0.00011503011,0.00018163073,0.000076378594,0.0000865206,0.0000069563725,0.000084860934,0.00033092088,0.00032081193],"category_scores_gemma":[0.000014373276,0.000099378274,0.00006630924,0.00015911521,0.00018865724,0.00004044365,0.000083932406,0.00018280956,0.0000082918905],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015594176,0.00018152074,0.077901214,0.000027282244,0.000053685595,0.0000044943863,0.000023875858,0.0008999824,0.9134705,0.000830793,0.00049467944,0.0060963714],"study_design_scores_gemma":[0.0007482313,0.00015948535,0.04996519,0.000016890073,0.000096116935,0.000004126248,0.000076374454,0.0014737496,0.92165375,0.00083240046,0.024787435,0.00018627274],"about_ca_topic_score_codex":0.00001469256,"about_ca_topic_score_gemma":0.0000018825999,"teacher_disagreement_score":0.027936023,"about_ca_system_score_codex":0.000103427505,"about_ca_system_score_gemma":0.0000062623753,"threshold_uncertainty_score":0.40525284},"labels":[],"label_agreement":null},{"id":"W4411000227","doi":"10.46488/nept.2025.v24i02.b4252","title":"A Complete Review on Ericoid Mycorrhiza: An Understudied Fungus in the Ericaceae Family","year":2025,"lang":"en","type":"review","venue":"Nature Environment and Pollution Technology","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ericaceae; Mycorrhiza; Biology; Fungus; Botany; Ecology; Symbiosis; Paleontology","score_opus":0.020868241926350742,"score_gpt":0.2654538821900362,"score_spread":0.24458564026368546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411000227","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000052915777,0.9922956,0.0000026102896,0.0037032652,0.00020544013,0.0012785138,0.0010486732,0.00006569065,0.0013472643],"genre_scores_gemma":[0.00042173997,0.9946604,0.000019294153,0.002909143,0.00009691851,0.00021139198,0.0015192154,0.0000020097095,0.00015993547],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99830735,0.00030723054,0.00038678283,0.0005232846,0.0001807237,0.0002946253],"domain_scores_gemma":[0.99876606,0.00078651897,0.00020182882,0.00019847963,0.0000065277027,0.000040551324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026050446,0.0003771385,0.00085480284,0.00011576426,0.0002293554,0.000023577335,0.0004901115,0.00088292814,0.00034867172],"category_scores_gemma":[0.00010254348,0.0001312177,0.0001801402,0.0007919462,0.00002551395,0.00004181052,0.000107967586,0.0015117063,0.00018885841],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000956898,0.0000941105,0.000028471597,0.0011380803,0.000028556246,0.000012105919,0.000006535999,2.087201e-7,0.00005726083,0.0052402453,0.010734405,0.98265046],"study_design_scores_gemma":[0.00002907489,0.00024816702,0.000130255,0.0061706984,0.00018219298,0.00004917488,0.000073923955,0.0000013587619,2.5951766e-7,0.00039625817,0.9924893,0.00022936384],"about_ca_topic_score_codex":0.00003633998,"about_ca_topic_score_gemma":0.00004046943,"teacher_disagreement_score":0.9824211,"about_ca_system_score_codex":0.00008562031,"about_ca_system_score_gemma":0.000012620315,"threshold_uncertainty_score":0.68099505},"labels":[],"label_agreement":null},{"id":"W4416277787","doi":"10.46488/nept.2025.v24i04.b4312","title":"An Analytical Investigation of Urban Expansion Patterns in the Kolkata Metropolitan Development Authority (KMDA) Region Using Geoinformatics","year":2025,"lang":"en","type":"article","venue":"Nature Environment and Pollution Technology","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geoinformatics; Metropolitan area; Urbanization; Urban expansion; Urban planning; Urban sprawl; Common spatial pattern; Land use","score_opus":0.011136000006072786,"score_gpt":0.24366357971265581,"score_spread":0.23252757970658303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416277787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99696475,0.00015891029,0.0014659464,0.0009950346,0.0000448813,0.00017748852,0.0000032993178,0.00001929111,0.00017040406],"genre_scores_gemma":[0.9988742,0.0000591798,0.0008092458,0.0002134166,0.000009789864,0.000006866407,0.000015600617,0.0000031938926,0.000008544934],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991117,0.00006372173,0.00028343493,0.00018572599,0.0001848099,0.00017058059],"domain_scores_gemma":[0.99958634,0.000013918812,0.000102699836,0.00026494294,0.0000024533426,0.000029620205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031703638,0.000110101726,0.0001409171,0.00018352324,0.00013077748,0.000011708102,0.00021513457,0.00035614363,0.000029648827],"category_scores_gemma":[0.0000094544985,0.00007736241,0.000017805574,0.00032510405,0.00008844497,0.0001856811,0.00011372801,0.0002774689,0.00000410089],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012524035,0.00005379456,0.99006873,0.00004325439,0.000010772593,0.0000021122735,0.00061868853,0.0001800432,0.001024668,0.0042972746,0.000076819815,0.003611319],"study_design_scores_gemma":[0.00047070967,0.00010700935,0.9578008,0.0000909911,0.00004791049,0.000012298004,0.0028465472,0.025018089,0.0069600134,0.003277363,0.003162175,0.00020611762],"about_ca_topic_score_codex":0.000111233385,"about_ca_topic_score_gemma":0.0001997957,"teacher_disagreement_score":0.03226795,"about_ca_system_score_codex":0.00023892555,"about_ca_system_score_gemma":0.000010644468,"threshold_uncertainty_score":0.31547475},"labels":[],"label_agreement":null},{"id":"W4416609968","doi":"10.46488/nept.2025.v24i04.d1774","title":"Modelling the Future: Groundwater Responses to Climate Change in Talomo-Lipadas Watershed, Davao City, Philippines","year":2025,"lang":"en","type":"article","venue":"Nature Environment and Pollution Technology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Commission on Higher Education; International Development Research Centre","keywords":"Groundwater recharge; Baseflow; Climate change; Groundwater; Water scarcity; Downscaling; Hydrology (agriculture); MODFLOW; Water resources","score_opus":0.009020428603847535,"score_gpt":0.23370564743868713,"score_spread":0.2246852188348396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416609968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.869528,0.001848126,0.00096639374,0.12572408,0.00032794767,0.00064946763,0.0000069597663,0.00009540285,0.00085365254],"genre_scores_gemma":[0.99221414,0.002585771,0.000315695,0.003725005,0.000059881084,0.00016490075,0.000006257484,0.000008634341,0.00091970805],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99868035,0.000070434944,0.00020396436,0.00045734356,0.00013868172,0.00044923596],"domain_scores_gemma":[0.999595,0.000018606059,0.00004167517,0.00031241588,0.0000016964906,0.00003064447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003660836,0.0002117762,0.0001915204,0.0002852739,0.00036857912,0.000018071829,0.000289833,0.00041681647,0.00017425214],"category_scores_gemma":[0.0000082812385,0.0001459007,0.000034220877,0.00031010367,0.0004228561,0.00014207161,0.0009118441,0.00047093284,0.00010351672],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094400335,0.0004677432,0.87376076,0.00007170703,0.00017387632,0.00007039192,0.0040689604,0.014343766,0.0034079796,0.033955924,0.0066614943,0.06207339],"study_design_scores_gemma":[0.0015554348,0.00034758484,0.27011707,0.0000649956,0.000118584794,0.000018685936,0.0010111678,0.004337505,0.0028239503,0.025956852,0.69286126,0.00078694365],"about_ca_topic_score_codex":0.00010305738,"about_ca_topic_score_gemma":0.00019959315,"teacher_disagreement_score":0.6861997,"about_ca_system_score_codex":0.00011414388,"about_ca_system_score_gemma":0.0000013427345,"threshold_uncertainty_score":0.5949658},"labels":[],"label_agreement":null}]}