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Record W6989535521

Assessing the value of stable water isotopes in hydrologic modeling: a dual-isotope approach

2016· dissertation· en· W6989535521 on OpenAlexfundno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesManitoba Hydro
KeywordsCalibrationIsotopeHydrological modellingStable isotope ratioStreamflowSimulation modelingHydrology (agriculture)
DOInot available

Abstract

fetched live from OpenAlex

This thesis presents the development of a dual-isotope simulation in a hydrological model, and its application to the lower Nelson River basin. The purpose of this study is to find if the simulation of stable water isotopes aids in hydrological simulation, and if a dual-isotope simulation is an improvement over a single-isotope simulation. The isoWATFLOOD model was enhanced to include δ2H and improve physical representativeness. The model was calibrated using various isotope and flow simulation error functions. Internal hydrologic storages and fluxes were verified by comparing simulated isotope values to observed isotope data. Adding isotope error to the calibration resulted in small but consistent improvements to the physical basis of calibrated parameter values. Isotope simulation error was found to be the best predictor of streamflow simulation performance beyond the calibration period. The dual-isotope simulation identified a number of model limitations and potential improvements from the verification of internal hydrologic storages.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.215
Teacher spread0.197 · 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 designObservational
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

Citations4
Published2016
Admission routes1
Has abstractyes

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