{"id":"W3118918738","doi":"10.22541/au.161069949.99425719/v1","title":"Emerging trends about uncertainty in hydrologic modeling and water resources management: A bibliometrics analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Bibliometrics; Water resources; Citation; Field (mathematics); Computer science; Domain (mathematical analysis); Environmental resource management; Environmental science; Data science; Data mining; Library science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01093344,0.0006018342,0.001384912,0.166127,0.001281464,0.006872163,0.0005910669,0.0007773468,0.003095057],"category_scores_gemma":[0.06108547,0.000276704,0.001125017,0.2587343,0.001558977,0.007268239,0.002510817,0.0006109998,0.0004922675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002546877,"about_ca_system_score_gemma":0.003338103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004950891,"about_ca_topic_score_gemma":0.004359198,"domain_scores_codex":[0.9880955,0.00208497,0.002253735,0.001186523,0.005979788,0.0003995662],"domain_scores_gemma":[0.9044355,0.07079456,0.01023874,0.002629286,0.01106058,0.0008413417],"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":[0.0001891586,0.00008642746,0.3414631,0.03275728,0.002180534,0.000855118,0.01054144,0.003079206,0.001565434,0.03867976,0.03860657,0.529996],"study_design_scores_gemma":[0.00003422764,0.0000983198,0.6760272,0.01492769,0.003033451,0.001887878,0.01758398,0.007127346,0.001787302,0.03337876,0.2439029,0.0002109749],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.4218631,0.4337886,0.01083648,0.01584486,0.0007447657,0.0002464327,0.05425486,0.0005411186,0.06187974],"genre_scores_gemma":[0.7801089,0.1885721,0.006295732,0.0005506847,0.001064838,0.0002497998,0.02151223,0.0001166093,0.001529129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.833873,"threshold_uncertainty_score":0.05782229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01814890409578313,"score_gpt":0.2529346620977068,"score_spread":0.2347857580019237,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}