{"meta":{"query_hash":"f88a140a109f","filters":{"venue":"River"},"cohort_total":4,"direct_labels_cover":0,"predictions_cover":4,"exported":4,"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/f88a140a109f","api":"https://metacan.xera.ac/api/v1/cohort?venue=River"},"results":[{"id":"W4283171246","doi":"10.1002/rvr2.6","title":"Resilience to climate change‐caused flooding—Metro Vancouver case study","year":2022,"lang":"en","type":"article","venue":"River","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Sleep Society; Western University","funders":"Canadian Institutes of Health Research; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; International Development Research Centre","keywords":"Flood myth; Flooding (psychology); Climate change; Environmental resource management; Resilience (materials science); Psychological resilience; Environmental planning; Population; Environmental science; Geography; Sociology; Ecology","score_opus":0.020738291481004235,"score_gpt":0.2708872094007812,"score_spread":0.25014891791977695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283171246","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9771566,0.00014671304,0.00084926863,0.0008028393,0.0000180027,0.00018018238,0.0006723277,0.000045306635,0.020128747],"genre_scores_gemma":[0.99457365,0.00025906562,0.00070574635,0.000065920096,0.000006934794,0.000060175225,0.00023326262,0.000008393582,0.004086999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99969923,0.000084774445,0.000009491938,0.000023678023,0.000056277437,0.0001265366],"domain_scores_gemma":[0.99941075,0.00016665271,0.000041220574,0.000032792712,0.00015215258,0.00019645048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031935459,0.0004168671,0.00028667037,0.00084247696,0.0039261975,0.0012040042,0.001201239,0.0012828759,0.0024269985],"category_scores_gemma":[0.0011960663,0.0002361464,0.0002922542,0.002013542,0.00090861396,0.00030202168,0.0011068903,0.00072719477,0.00018372132],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007472773,0.001581124,0.26575077,0.00050931313,0.0002431532,0.058950588,0.006943908,0.55607,0.004505107,0.01933132,0.025353964,0.060013417],"study_design_scores_gemma":[0.0005749421,0.00065117085,0.19397144,0.0003123604,0.00018412805,0.0050013713,0.05643522,0.6694484,0.002613565,0.008180729,0.062306587,0.00032012083],"about_ca_topic_score_codex":0.90266556,"about_ca_topic_score_gemma":0.9463802,"teacher_disagreement_score":0.097334445,"about_ca_system_score_codex":0.013286691,"about_ca_system_score_gemma":0.006140291,"threshold_uncertainty_score":0.19581527},"labels":[],"label_agreement":null},{"id":"W4289943419","doi":"10.1002/rvr2.11","title":"Urban water security for developing countries","year":2022,"lang":"en","type":"article","venue":"River","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":15,"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":"Sanitation; Clean water; Urbanization; Developing country; Millennium Development Goals; Water security; Business; Economic growth; Sustainable development; Environmental planning; Population; Water resources; Geography; Political science; Economics; Engineering; Environmental engineering; Environmental health","score_opus":0.014721867650307804,"score_gpt":0.2571289273641292,"score_spread":0.24240705971382137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289943419","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06983162,0.14402257,0.0039363205,0.24791272,0.0052686674,0.0003493493,0.009466385,0.00043504182,0.51877743],"genre_scores_gemma":[0.7177987,0.15411912,0.008916091,0.011500198,0.0017662896,0.00050554733,0.0056410437,0.00009663218,0.09965632],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99951327,0.0001701108,0.000035379035,0.000028132587,0.00009470413,0.00015839204],"domain_scores_gemma":[0.99918824,0.000103843544,0.0001563034,0.000053035914,0.00023667216,0.00026191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011081173,0.00037554366,0.00013663407,0.00093388755,0.0010275582,0.0021125989,0.00026867745,0.00056455337,0.01706923],"category_scores_gemma":[0.0014426317,0.00008686848,0.00015077989,0.0013146588,0.00050205115,0.0011164577,0.0022088122,0.00092980126,0.0018054622],"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":[0.00007274781,0.000111643116,0.028406208,0.0015713145,0.000027426695,0.00052776345,0.0016912736,0.00096352684,0.0015575483,0.16405402,0.38949788,0.41151863],"study_design_scores_gemma":[0.00000581051,0.000048830792,0.019097032,0.0011255528,0.000005068293,0.00022331275,0.0016784418,0.00016011336,0.0003061112,0.006226297,0.9711146,0.0000088142415],"about_ca_topic_score_codex":0.00603507,"about_ca_topic_score_gemma":0.005290031,"teacher_disagreement_score":0.01706923,"about_ca_system_score_codex":0.0017226188,"about_ca_system_score_gemma":0.008556291,"threshold_uncertainty_score":0.057102203},"labels":[],"label_agreement":null},{"id":"W4387451098","doi":"10.1002/rvr2.63","title":"Water level prediction using deep learning models: A case study of the Kien Giang River, Quang Binh Province","year":2023,"lang":"en","type":"article","venue":"River","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Typhoon; Water level; Mean squared error; Flood myth; Wind speed; Computer science; Long short term memory; Meteorology; Environmental science; Artificial intelligence; Recurrent neural network; Statistics; Geography; Artificial neural network; Cartography; Mathematics","score_opus":0.10893924002684521,"score_gpt":0.2621455300029658,"score_spread":0.1532062899761206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387451098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961218,0.000055845503,0.0020136742,0.00017833141,0.000009528524,0.00001560577,0.00047346242,0.00007990989,0.0010519094],"genre_scores_gemma":[0.9983553,0.000025537263,0.00093700335,0.0000068008603,0.0000017505427,0.0000062669315,0.00027047002,0.0000034411944,0.00039328056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999876,0.000027766131,0.000008423104,0.00002967365,0.000025782514,0.000032328186],"domain_scores_gemma":[0.99971753,0.00010843181,0.00003054076,0.000025953692,0.00008066048,0.00003687356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003227225,0.00041797647,0.0002750202,0.0005178094,0.00049958145,0.0005524344,0.000655383,0.00050125696,0.0007360865],"category_scores_gemma":[0.0007188367,0.00020582795,0.00031700972,0.0012680074,0.00035707804,0.00047999743,0.00034374653,0.000460092,0.000068645204],"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":[0.00016874548,0.00027045546,0.122633435,0.000103284845,0.00010740783,0.0020461157,0.0002909906,0.83523935,0.0025233456,0.0016759053,0.0018545168,0.033086374],"study_design_scores_gemma":[0.000012106636,0.000020183548,0.016742704,0.0000050748963,0.000014168285,0.000018846538,0.00022839473,0.9817246,0.00064072176,0.00024708433,0.00033529583,0.000010891925],"about_ca_topic_score_codex":0.41450778,"about_ca_topic_score_gemma":0.4274944,"teacher_disagreement_score":0.41450778,"about_ca_system_score_codex":0.0033585601,"about_ca_system_score_gemma":0.001816658,"threshold_uncertainty_score":0.82419014},"labels":[],"label_agreement":null},{"id":"W4399748118","doi":"10.1002/rvr2.89","title":"Imprints of large‐scale climate oscillations on river flow in selected Canadian river catchments","year":2024,"lang":"en","type":"article","venue":"River","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Scale (ratio); Hydrology (agriculture); Environmental science; Streamflow; Flow (mathematics); Climate change; Geography; Drainage basin; Physical geography; Climatology; Geology; Oceanography; Cartography; Physics; Geotechnical engineering","score_opus":0.005939062530745485,"score_gpt":0.22447651856864326,"score_spread":0.21853745603789776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399748118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986307,0.000032810865,0.00012703119,0.000048304664,0.0000013766571,0.0000046199284,0.00046193736,0.0000069326225,0.00068630715],"genre_scores_gemma":[0.9995602,0.00002499901,0.00006502115,0.000007473586,8.146081e-7,0.0000016607705,0.00020880335,0.0000015554283,0.00012936883],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99977046,0.000029231529,0.000007787596,0.00004833688,0.00006612977,0.000078131234],"domain_scores_gemma":[0.99933916,0.00018875654,0.00010938183,0.000036048838,0.00022250567,0.00010415869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040919008,0.00018200511,0.0002017607,0.00087009463,0.00072005397,0.0007610752,0.00030207442,0.00020994733,0.00073649484],"category_scores_gemma":[0.0015796182,0.00015418172,0.00026754275,0.0013497877,0.0005895498,0.00021469584,0.00054277136,0.0002166692,0.000040621762],"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":[0.00009632292,0.000026403708,0.98128086,0.000021785605,0.00009896091,0.00015254278,0.00073101674,0.004940207,0.0044220355,0.00030282757,0.00040330924,0.007523676],"study_design_scores_gemma":[9.329868e-7,0.00000319088,0.9984664,0.0000018040083,0.0000053461854,0.0000056147574,0.00018996897,0.0010528013,0.000091958886,0.000017838081,0.00016011613,0.000004177367],"about_ca_topic_score_codex":0.9581325,"about_ca_topic_score_gemma":0.98146915,"teacher_disagreement_score":0.041867495,"about_ca_system_score_codex":0.005638127,"about_ca_system_score_gemma":0.004285782,"threshold_uncertainty_score":0.08422804},"labels":[],"label_agreement":null}]}