MétaCan
Menu
Back to cohort

Evaluation of Future Streamflow Patterns in Lake Simcoe Subbasins Based on Ensembles of Statistical Downscaling

2017· article· en· W2637856311 on OpenAlexaffabout
Chun Chao Kuo, Thian Yew Gan, Kaz Higuchi

Bibliographic record

VenueJournal of Hydrologic Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsYork UniversityUniversity of Alberta
Fundersnot available
KeywordsStreamflowDownscalingEnvironmental scienceHydrographPrecipitationFlood forecastingDrainage basinClimatologyHydrology (agriculture)Climate changeFlood mythMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Future streamflow patterns of three subbasins, i.e., East Holland, Beaver, and Pefferlaw River basins, located in the upstream of Lake Simcoe of Canada are assessed for 2021–2099. The individual set of parameters of a conceptual hydrological model, HBV (Hydrologiska Byråns Vattenbalansavdelning)-light, are first calibrated for these three subbasins. The calibrated model was validated and further used to estimate the future streamflow driven by statistically downscaled projected precipitation and air temperature from the Pacific Climate Impacts Consortium under Representative Concentration Pathways 8.5 and 4.5 scenarios. The uncertainty of annual streamflow, hydrograph, flow duration curve (FDC), and flood frequency were evaluated. The results reveal that the annual streamflows of the Beaver and Pefferlaw River Basins (PRB) are projected to slightly increase in 2020–2099 while the annual streamflows of the East Holland River Basin (EHRB) are expected to be similar in 2020–2099. The monthly streamflow in winter is projected to increase but to decrease in spring across three subbasins. Based on the projected FDCs, daily streamflow of EHRB and PRB will likely increase by 2070–2099.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.253
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Hydrologic EngineeringSame topicHydrology and Watershed Management StudiesFrench-language works237,207