Water-yield estimates for critical loadings assessment: comparisons of gauging methods versus an isotopic approach
Bibliographic record
Abstract
An isotope mass balance technique is applied to quantify water yield and refine a steady-state critical acid loadings assessment for 49 lakes in hydrologically complex, wetland-rich terrain of northeastern Alberta. The approach uses physical and climatological data combined with site-specific measurements of evaporative isotopic enrichment of 2H and 18O in lake water to measure lake residency and ungauged runoff to lakes. Mean water yields to individual lakes across the region over a 3-year period are estimated to range from 5 to 395 mm·year–1, with a standard deviation of two times the predicted estimates based on interpolation of gauged stream flow from broad-scale watersheds in the area. Comparison of the method with longer-term Water Survey of Canada hydrometric data suggests very similar average water yields for moderate- to large-sized watersheds. However, the isotope-based estimates appear to capture extreme low water yields in flat, disconnected areas and extreme high water yields in other areas thought to be related to stronger connections to regional groundwater flow systems. For aquatic ecosystems of northeastern Alberta, an area expected to be affected by acid deposition from regional oil sands development, continued refinement of the technique is important to accurately assess critical loads for ungauged systems, particularly those in low-yield settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".