End-Member Selection in Two-Component Isotope-Based Hydrograph Separation
Bibliographic record
Abstract
The science that underpins our knowledge and understanding of Isotope-Based Hydrograph separation (IHS) has gained grounds, over the last few decades, in the identification of streamflow sources. However, challenges still exist in identifying appropriate tracers and the right combination of end-members for the IHS process. In a two-component IHS analysis, the application of the dual isotopes tracers, δ18O and (or) δ2H, is regarded as the simplest method. We undertook an IHS study within a nested system of eight Prairie watersheds located in South central Manitoba, Canada. The work evaluated about 17,000 results emanating from the application of a combination of two potential tracers (δ18O and δ2H) and eight each of potential “old” and “new” water end-members in a two-component IHS process. The outcome showed occurrences of many mathematically possible but hydrologically unacceptable IHS results. The observation was particularly predominant within relatively larger perennial sub-catchments of the watershed. It is also shown that inter-site sub-catchment isotopic end-member transferability is possible within watersheds of similar physio-hydrographic characteristics. We suggest that a careful evaluation of the physio-hydrographic characteristics of catchments be considered in IHS studies in addition to the recommended guidelines in the selection of tracers and end-members.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".